Bending Spoons has snapped up Airtable to add to its portfolio of software companies, alongside AOL, Evernote, WeTransfer, Brightcove and Vimeo

Airtable made its name as a builder of low/no code database services, aimed particularly at non-technical staff, but is now one of many vendors facing financial difficulties in the face of the SaaS/AIpocalypse. The arrival of AI coding tools, which offer non-technical employees more flexible ways to build business applications, has hit demand for its services.

Bending Spoons bought Airtable in a deal it valued at just $1.285 billion, a far cry from the $11.7 billion Airtable was worth at its peak.

Bending Spoons has built its portfolio by buying once-successful companies like Airtable that have struggled to cope with newer, nimbler competitors or failed to adapt to emerging technologies. Bending Spoons takes these companies, cuts costs and markets them aggressively with the goal of returning them to profitability.

“Airtable is a pioneering brand reshaping how teams organize data and manage critical workflows. We’re committed to investing in Airtable for the long run, and doubling down on its core strength: bringing teams and workflows together in one flexible workspace. We plan to expand what can be done across the full spectrum of work and make Airtable even more valuable to customers at every scale,” said Luca Ferrari, Bending Spoons CEO and co-founder.

This article first appeared on InfoWorld.

AWS is finally adding native vector search to its managed NoSQL database DynamoDB, which is typically used to store high-volume operational and transactional data.

The update, according to analysts, removes complexity for development teams that are trying to maintain separate vector databases for a rapidly growing class of AI and agentic applications that rely on real-time access to operational and transactional data to improve the accuracy and relevance of their responses.

“This collapses a common two-database architecture into one operational data layer. Developers can update an item and its vector representation together, use familiar DynamoDB APIs, and avoid building a separate synchronization pipeline. That should materially shorten time-to-market for AI features built around existing DynamoDB data,” said Stephanie Walter, practice lead of the AI stack at HyperFRAME Research.

Prior to the update, enterprises using DynamoDB typically had to copy data into OpenSearch or another vector database, such as Pinecone and Weaviate, often using DynamoDB Streams or custom pipelines, which meant operating two data layers and managing embedding generation, backfills, retries, schema changes, security policies, and synchronization, Walter noted.

That dependence on two separate data layers, Walter pointed out, added to query latency and increased the risk of the vector index lagging behind the operational record.

Such delays, according to Ashish Chaturvedi, executive research leader at HFS Research, can have real consequences for AI agents: “If an agent is acting on what it retrieves, a synced-five-minutes-ago copy can mean a confident wrong action.”

Beyond improving data freshness, eliminating a separate vector database could also lower cloud and operational costs, Chaturvedi said: “Maintaining a second database meant paying at least $700 a month for a second database regardless of usage.”

For CIOs, those benefits combined could translate into lower total cost of ownership and simpler governance as enterprises scale AI applications, Chaturvedi added.

“It is a real consolidation for CIOs: fewer systems to secure, no pipelines to maintain, fresh data. Add to it usage-based cost, which means no standing minimums for idle infrastructure, even at trillion-vector scale,” Chaturvedi noted.

The architectural simplification could also make AI adoption easier for business leaders by eliminating the need to staff and govern a second data platform, said Michael Leone, principal analyst at Moor Insights & Strategy.

Native vector search capabilities have been gaining popularity over the past few years. Over the last three years, AWS has steadily expanded vector search across its database portfolio, adding support to Aurora PostgreSQL through pgvector, Amazon MemoryDB for Redis and Amazon DocumentDB as enterprise demand for generative AI applications has grown.

Rivals have followed a similar path, with MongoDB, Microsoft, Google Cloud, Oracle and Couchbase integrating native vector search into their databases to support retrieval-augmented generation (RAG), AI agents and other generative AI workloads.

These announcements themselves reflect a broader convergence in the database market since the rise of generative AI. While specialized vector databases have expanded beyond similarity search by adding SQL, NoSQL and operational database capabilities, mainstream operational databases have been embedding vector search and RAG capabilities into their core platforms, allowing enterprises to consolidate AI and transactional workloads on fewer data platforms.

AWS has not shared details on the rollout of DynamoDB’s native vector search capability, including its availability timeline or the AWS Regions where it will initially be offered.

Microsoft has had a narrow escape from total embarrassment: A security company uncovered a critical vulnerability that could have compromised all Azure Cosmos DB databases — both those of customers and Microsoft’s own.

Google subsidiary Wiz found a flaw in the database’s Gremlin API, usually used for storing and managing property graph data.

If bad actors had discovered it first, they could have exploited it to acquire what Wiz called the Cosmos Master Key, which would have enabled them to use the primary key of any Cosmos database, resulting in read and write access to any account. They would also have had access to a list of every database on the service, with identifiers such as subscription and tenant IDs.

Azure Cosmos DB is a NoSQL database that underpins Microsoft’s cloud services. It can be accessed through SDKs for framework such as Python, Node.js, Java, and .NET.

Wiz described how it discovered the vulnerability in a blog post. It disclosed details of the flaw to Microsoft in November 2025.

Microsoft deployed a hot fix within two days, but it took another eight months to re-engineer the infrastructure, removing the Cosmos Master Key and introducing new guardrails to Cosmos DB to prevent similar attacks.

It is not the first time Cosmos DB customers’ primary keys have been under threat: In 2021, Wiz found a flaw in data exploration tool Jupyter Notebook that could be exploited to access the database keys and other secrets.

This article first appeared on CSO.

Oracle on Tuesday described new EBCDIC character set compatibility features in Oracle AI Database for customers transitioning from legacy databases on IBM mainframes.

In its post, Oracle noted that EBCDIC compatibility has historically been one of the top technical challenges for enterprises re-platforming to use newer, ASCII-based databases while continuing to use proven legacy applications.  

“Achieving this goal requires more than simply moving data. It requires preserving the EBCDIC compatibility on which existing applications depend,” wrote Michael Yau, VP for Oracle Database Globalization Engineering. The feature rollout “addresses two fundamental challenges of preserving EBCDIC compatibility: accurate character encoding conversion and preservation of EBCDIC binary ordering.”

He added: “These client character sets implement IBM Character Data Representation Architecture (CDRA) code page definitions, providing source-to-target character mappings that are compatible with IBM’s published standards. This enables accurate and predictable character encoding conversion during data migration and subsequent database client/server communication.”

Yao observed that this is important because these mainframe migrations can be very complex.

“Many legacy EBCDIC applications, such as those written in COBOL, implicitly rely on the EBCDIC binary ordering defined by IBM EBCDIC code pages. SQL predicates that compare character values, perform range searches, or sort query results often assume this ordering,” he wrote. “After migration to an ASCII-based Oracle AI Database character set, these same SQL statements can produce different results, not because the data changed, but because the database’s default binary ordering follows that of the ASCII-based database character set rather than the source EBCDIC code page.” 

Compatibility repair, not modernization

While consultants generally applauded the new features, some questioned whether this will simply shift enterprise dependency on IBM to dependency on Oracle. 

Sanchit Vir Gogia, chief analyst at Greyhound Research, is one of the fans.

“Oracle has repaired one of the oldest silent faults in mainframe migration: EBCDIC ordering, the muscle memory of the legacy estate. Preserve the data and lose the ordering, and a query returns the wrong record while every dashboard stays green,” he said. “The application runs and the query completes. The answer is simply wrong.”

Gogia noted that the new feature is “not a modernization suite. It is a compatibility repair, narrow and genuinely useful, which CIOs who have bled on past migrations will read with equal parts relief and suspicion.”

AJ Thompson, CCO at UK IT consulting firm Northdoor, agreed that the Oracle announcement addresses a genuine technical barrier rather than just being a marketing gimmick, so it is worth taking seriously as a re-platforming enabler. “The two problems it solves, EBCDIC to ASCII character conversion and preserving EBCDIC binary sort ordering, have historically been real blockers for allowing COBOL to move away,” he said.

But, he cautioned, CIOs must also take resiliency challenges seriously. “Mainframes are not chosen primarily for character encoding, they are chosen for benefits like decades of proven uptime, IBM Z’s redundancy architecture, and workload isolation,” he pointed out.

“Oracle’s announcement solves a data compatibility problem, not a resilience or availability one. A client with genuinely mission-critical, zero-downtime workloads will still need convincing on the availability and disaster recovery side before moving [to the cloud], and Oracle’s own resilience claims would need scrutiny on their own merits, quite separate from this EBCDIC work.”

Leverages IT desperation

Mike Wilkes, enterprise CISO at Aikido Security, added that Oracle is leveraging IT desperation to squeeze long-term value from legacy systems. 

“I have always believed that Oracle will own the very last white-knuckle-grip workloads that migrate from on-premises data centers into the cloud,” he said. “This announcement certainly demonstrates that they understand their position in the world of cloud service providers. They are not the biggest, they are not the oldest, and they are not the most technically advanced. But they do own the market share for the trailing edge of cloud adoption.”

He pointed out that the greatest barrier to cloud migration is not containerizing modern applications, it is the decades of business logic buried inside COBOL applications and EBCDIC-encoded data.

“Oracle’s EBCDIC compatibility features acknowledge a practical reality: organizations are not rewriting these systems from scratch,” he said. “If Oracle can reduce the cost, risk, and operational disruption associated with moving those workloads, the announcement represents meaningful value for IT teams that have been delaying modernization because the migration path was simply too complex or too expensive.”

Could cause vendor lock-in

Then again, Wilkes noted, there is the potential for increasing vendor lock-in.

“Compatibility layers almost always increase long-term dependence on the platform providing them,” he said. “Rather than eliminating legacy technology, they abstract it behind Oracle’s database and cloud ecosystem, making future migrations potentially more difficult. Enterprises should view these tools as transition accelerators rather than permanent architecture.”

Thus, he said, if they use the opportunity to gradually modernize applications and data models, there is substantial value, but if they simply relocate technical debt into Oracle Cloud, they may find that they have exchanged one form of legacy lock-in for another.

Easier, but not easy

Matt Kimball, VP and principal analyst with Moor Insights & Strategy, also saw the Oracle move as a good one, but stressed that while it should make things easier for organizations, re-platforming still won’t be easy because EBCDIC migration isn’t just a character-conversion exercise. However, “Oracle’s built-in character-set support and EBCDIC collations move part of that compatibility burden into the database, making it easier to preserve existing application behavior,” he said.

Ishraq Khan, CEO of coding productivity tool vendor Kodezi, agreed. 

“One of the biggest obstacles to leaving mainframes is decades of applications built around EBCDIC encoding and legacy data formats. If these compatibility features reduce the amount of code that needs to be rewritten, they can lower migration risk, cost, and implementation time,” Khan said. But, he added, organizations should also consider whether these features simply make migration easier, or make future moves more difficult.

This article originally appeared on CIO.com.

Oracle’s July 2026 Critical Patch Update, its largest ever, contains 1,449 new security patches spanning 32 product families, from Oracle Database and E-Business Suite to PeopleSoft, GoldenGate, Java SE, and Fusion Middleware.

Fusion Middleware was particularly hard hit, with new security patches for 355 security vulnerabilities, 219 of them remotely exploitable without authentication, meaning they can be exploited over a network without requiring user credentials. Ten of them scored a “perfect” 10.0 on the Common Vulnerability Scoring System (CVSS).

These included easily exploitable vulnerabilities allowing unauthenticated attackers with network access via HTTP to compromise Oracle Data Integrator, Oracle Access Manager, Oracle HTTP Server, Oracle Platform Security for Java, Oracle WebCenter Content, Service Delivery Platform, or Oracle Weblogic Server Proxy Plug-in,

No other products were found to have quite such extreme vulnerabilities, but there were plenty of others scoring almost as badly.

Two critical flaws in Oracle Database Server

The most severe flaw Oracle patched in its flagship database product is CVE-2026-61211, a vulnerability in the RDBMS component’s DBMS_CLOUD package with a CVSS score of 9.9.

This easily exploitable vulnerability allows a low-privileged attacker having Execute DBMS_CLOUD privilege with network access via Oracle Net to compromise the RDBMS, Oracle said in the patch update statement. “While the vulnerability is in RDBMS, attacks may significantly impact additional products (scope change). Successful attacks of this vulnerability can result in takeover of RDBMS,” it warned.

The flaw affects Database Server versions 19.3 through 19.31 and 23.4.0 through 23.26.2.

Sanchit Vir Gogia, chief analyst at Greyhound Research, said the 9.9 score should be read as serious but conditional. Exposure depends on configuration, he said: On customer-managed databases, DBMS_CLOUD is absent until installed, and grants and network access lists determine the radius from there. “Where DBMS_CLOUD is broadly granted and reachable, the emergency is real and the window is seventy-two hours; where it is absent, the accelerated database wave will do.”

Vibhum Dubey, a cybersecurity researcher and red teamer, said the flaw stood out to him because it checks several boxes that concern defenders.

“Database servers often hold an organization’s most valuable data, so even if exploitation is not publicly observed yet, I don’t think this is the kind of issue you leave until the next routine maintenance window if your environment is exposed,” Dubey said.

A second Database Server flaw, CVE-2026-47040, affects Connection Manager in Oracle Net Services, is remotely exploitable without credentials. Oracle’s risk matrix lists six Database Product vulnerabilities in this cycle as reachable over a network with no authentication required, the statement added.

CVE-2026-7383, an OpenSSL-related TLS vulnerability, affects two products, Database Server and Autonomous Health Framework, since both bundle the same third-party component. Oracle’s advisory notes the Database Server patch for that CVE also resolves 19 related OpenSSL CVEs bundled into the same fix.

Oracle GoldenGate received 27 new patches, nine of which do not require authentication to exploit, including CVE-2026-2332, a flaw in the Big Data and Application Adapters component tied to Eclipse Jetty, the statement added.

There were also two critical flaws in Oracle’s TimesTen in-memory database.

The remainder of the release spans E-Business Suite, WebLogic Server, PeopleSoft, Siebel, JD Edwards, Communications, Retail Applications, Utilities Applications, MySQL, Solaris and VM VirtualBox.

Volume repair

Gogia said the volume itself marks a shift.

“At 1,449 patches, against 481 in April 2026 and 309 a year earlier, patch load has outgrown the queue built to hold it,” he said. He recommended a tiered response: “The reachable and the reported inside seventy-two hours, the trusted core inside ten days, the rest by risk before the October release.”

He also flagged a specific risk in how organizations might triage E-Business Suite. “Oracle’s advisory concedes that E-Business Suite exposure sits partly in underlying Database and Fusion Middleware versions outside the E-Business Suite matrix. The fastest way to mis-prioritise this release is to patch by product logo instead of trust boundary.”

Third Tuesday, quarterly cycle

The July release is the third quarterly Critical Patch Update of 2026, and the first since the introduction in May of the monthly Critical Security Patch Update program.

Gogia said Oracle has effectively layered a second cadence on top of the existing one rather than replacing it.

“Quarterly Critical Patch Updates remain and stay cumulative; monthly Critical Security Patch Updates now sit on top,” he said, adding that enterprise adoption of the new rhythm remains low because of “certification obligations, regression exposure and scarce specialist hours.”

Dubey made a similar point about organizational readiness: “In large enterprises, patching is rarely a technical problem. It is an operational one. Database administrators, application owners, infrastructure teams, business stakeholders, and change advisory boards all have to align.”

Niyati Daftary, principal analyst at Gartner, said the release underscores a broader shift in how patching is approached.

“Patching is no longer a race to remediate every vulnerability. It is a discipline of identifying the exposures that matter most and reducing business risk as efficiently as possible,” she said, adding that organizations should prioritize based on exposure, business impact and exploitability, starting with internet-facing assets and mission-critical systems.

Daftary pointed to continuous threat exposure management and adversarial exposure validation as increasingly relevant frameworks, since CVSS scores “measure theoretical severity rather than actual enterprise risk.” Patching alone will not be sufficient, Daftary said, and organizations should continue investing in defense in depth, including behavioral threat detection and incident response.

Oracle’s next cumulative Critical Patch Update will come on Oct. 20, 2026, with smaller Critical Security Patch Updates on Aug. 18 and Sept. 15.

This article first appeared on CSO.

Oracle is expanding its Cloud@Customer on-premises portfolio with a managed database offering that it says will enable enterprises to run databases, applications, and AI agents in their own data centers, helping CIOs modernize mid-sized workloads while meeting data residency, regulatory, and low-latency requirements.

The hybrid cloud offering, Base Database Cloud@Customer, combines existing database and infrastructure services such as the Base Database Service and Data Infrastructure Cloud@Customer X11 platform. It is designed for enterprises that do not need the scale of Exadata Cloud@Customer but still want their infrastructure and AI capabilities on-premises, managed by Oracle, the company said.

The Cloud@Customer X11 platform itself consists of two Oracle X11 compute servers and shared all-flash storage, offering up to 60 usable processor cores and 660 GB of memory per server, 47.2 TB of storage, and 10/25 GbE networking.

For regulated industries or restricted connectivity

Analysts see the new offering filling a gap for enterprises that want the operational and economic benefits of the cloud but cannot send their data to a public cloud because of legal restrictions or technology limitations.

These enterprises, according to Ashish Chaturvedi, executive research leader at HFS Research, are likely to be in regulated industries such as financial services, healthcare, government, and defense that must comply with data residency requirements, or needing low-latency access from remote sites to operational databases.

The offering could also appeal to enterprises modernizing mid-sized workloads at remote locations or within individual business units that could never justify the investment in a full Exadata rack, said Amit Chandak, chief analytics officer at IT consulting firm Kanerika.

In all cases, Chaturvedi said, the appeal of the offering is its managed nature, which takes away the burden of looking after the underlying infrastructure.

Deployment and maintenance becomes easier too, said Michael Leone, principal analyst at Moor Strategy and Insights: “They get automation that mid-size teams rarely have the staff to build. Clustering, patching, standby databases, and backups arrive configured instead of hand-assembled because the offering is managed.”

The economics are equally compelling, Chaturvedi said. The pay-as-you-go pricing model, combined with online compute scaling, helps enterprises avoid overprovisioning and paying license fees for idle cores, which is a “classic waste” of fixed on-premises systems, he said.

Private AI behind the firewall

Beyond the operational and economic benefits, the architecture of the new offering enables databases, applications, VMs, and AI agents to be collocated on the same platform, removing what Chaturvedi called “the single biggest blocker” to AI adoption in regulated environments: the need to keep private data behind the firewall.

“For a CIO in a regulated sector who wants to deploy AI agents but can’t let regulated data touch an external model API, that’s a real unlock,” Chaturvedi said.

More so because most AI offerings, at least in their present form and state, cannot guarantee sensitive data protection, said Igor Ikonnikov, advisory fellow at Info-Tech Research Group.

Even if Base Database Cloud@Customer turns out more expensive than fully cloud-based options, “It’s still attractive as it eliminates reputational and economic risk caused by possible AI-induced data leakage,” Ikonnikov said.

The offering’s consolidation of databases, applications, and AI agents will also simplify deployment of AI-based workflows, said Forrester principal analyst Noel Yuhanna. “It reduces stack complexity and helps accelerate development cycles, deliver real-time data, and eliminate data movement challenges.”

Despite those advantages, Chandak cautioned that the offering is unlikely to see broad adoption outside Oracle’s existing customer base: “If a company isn’t already on Oracle, the pull is weak. You don’t buy into Oracle’s database just to get this.”

Enterprises seeking similar hybrid cloud capabilities have no shortage of alternatives: AWS, Microsoft, Google Cloud, IBM, Dell Technologies, and HPE all offer combinations of on-premises infrastructure, cloud management, and AI services.

However, those alternatives typically require customers to integrate multiple software and hardware components rather than consume them as a single managed offering.

Oracle’s differentiation, although narrow, is hard to match, Chaturvedi said: “The vertical integration of database, engineered hardware, cloud management, high-availability architecture, and now private AI, all engineered together and delivered as a managed on-prem subscription should be genuinely convenient and attractive.”

The offering is compatible with Oracle AI Database 26ai and Oracle Database 19c in Enterprise Edition and Standard Edition configurations. It also supports Oracle Real Application Clusters, Oracle Data Guard, and Zero Data Loss Recovery Appliance through Oracle-managed cloud automation for high availability and disaster recovery, the company said.

Base Database Cloud@Customer is now generally available, Oracle said. It did not provide pricing.

This article first appeared on CIO.

Separating transactional databases from analytical systems was, until recently, considered good architecture. Now, as enterprises adopt AI agents that continuously read, reason over, and act on business data, data warehouse and database vendors are increasingly deciding that separation has become a liability.

Just weeks after Databricks unveiled its Lakehouse Transaction and Analytical Processing (LTAP) offering based on Neon Postgres to bring operational (OLTP) and analytical (OLAP) processing closer together, EnterpriseDB (EDB) has introduced converged analytics capabilities for its managed EDB Postgres AI database service with the same intent.

Both vendors are responding to the same pressure of enabling AI agents for enterprises to operate on fresh operational data without waiting for pipelines and replicas, but EDB argues its approach starts from a fundamentally different place.

“Databricks is building from the lakehouse outward, trying to pull transactional capability in through Lakebase,” said Max Romanenko, chief engineering officer at EDB, while “we’re building from the operational layer with Postgres, which is where enterprises already run their most critical workloads, and expanding from there.”

In contrast to Databricks’ lakehouse-centric LTAP, EDB keeps Postgres as the operational source of truth and uses Apache Iceberg as a shared catalog layer connecting Postgres with ClickHouse, WarehousePG, and Spark compute engines, Romanenko said.

In this way, operational data remains in Postgres while historical and tiered data is stored in Iceberg-managed object storage, allowing analytical engines to query the same data through a common catalog without requiring separate copies or ETL pipelines, he said.

That architectural distinction matters to EDB, according to Romanenko, because the vendor is targeting enterprises that want AI and analytics capabilities without moving sensitive data into a cloud-managed platform: “For us, it’s always been about the data sitting on infrastructure the customer owns and controls.”

Focus on data sovereignty and predictable economics

EDB’s promotion of control “will resonate with CIOs focusing on sovereignty, regulated data, and hybrid deployment,” said Stephanie Walter, practice leader of AI stack at HyperFrame Research. “This should enable them to run AI and analytics closer to the data, on infrastructure that their enterprise controls, without creating yet another proprietary data estate.”

For Ashish Chaturvedi, leader of executive research at HFS Research, EDB’s approach in converged analytics will offer more predictable costs than Databricks LTAP for CIOs already struggling to manage their analytics and AI budgets.

EDB’s per-core pricing model can make costs easier to forecast than consumption-based cloud data platforms, where query volumes, AI workloads, and data processing demands can cause bills to fluctuate, Chaturvedi said.

But predictable bills are not necessarily lower bills, warned, Igor Ikonnikov, advisory fellow at Info-Tech Research Group. “The hardware requirements for high-speed operational data processing are higher and relatively more expensive compared to cheap lakehouse storage,” he said.

EDB’s architecture could also simplify data governance by reducing the number of platforms enterprises need to manage. Since operational, analytical, and AI workloads can access data through a common Postgres-Iceberg foundation, enterprises may be able to avoid deploying and governing multiple specialized data stores, and so have fewer systems to license and secure, according to Devin Pratt, research director at IDC.

Reducing architectural tax for engineering teams

EDB’s converged analytics could also simplify operations for developers and data engineering teams.

Its architecture reduces the number of systems developers must integrate and maintain, while eliminating much of the pipeline work traditionally required to move data between transactional and analytical systems, according to Walter.

And, said Pratt, “Zero-ETL means far less plumbing to build and break, so engineers spend their time creating value.”

EDB and Databricks are not the only ones pursuing converged analytics to support agentic systems and other applications needing immediate access to operational data, historical context, and governance controls.

Snowflake has been expanding support for operational workloads by embracing open table formats,and Microsoft has combined transactional and analytical services under a broader data architecture via its Fabric platform.

Evolution of autonomous databases?

Converged analytics, though, was only one part of EDB’s update to its Postgres AI platform.

It has also made generally available what it calls an “agentic database” feature, designed to automate routine database administration tasks.

The system continuously monitors hundreds of operational and performance metrics, detects anomalies, recommends corrective actions, and, where enterprise policies permit, can automatically apply fixes, the company said.

These automated agents can help enterprises optimize and tune their databases up to 10 times faster, it said.

Walter remained skeptical: “It is more an evolution of autonomous database concepts than a wholly new category. Oracle and other database vendors have offered autonomous database capabilities for years.” Where EDB can differentiate itself, she said, is in extending those autonomous capabilities with AI-driven reasoning, automated remediation, and governance controls that allow enterprises to determine how much authority the system receives.

Model Context Protocol (MCP) has gained considerable momentum as a standard connector between LLM-powered tools and local systems, internal and external APIs, and data sources. From major clouds to devops tools, MCP servers are enabling powerful, AI-powered development and operations capabilities through natural language commands.

Nowhere is this more true than in the world of databases. Most major database platforms now support agentic access through MCP servers. Using an MCP server for databases, you and your AI agent proxies can perform lookups, create and update data, and perform administrative tasks without you having to write SQL by hand.

The MCP server could also guide your LLMs to write new code or build automations that align with your database schema, like its tables, structure, and fields, as well as embeddings, indexes, and metadata. It could also aid debugging by enabling faster queries to surface data issues or misconfigurations, along with plenty of other possible use cases.

Below, we’ll cover official MCP servers from some of the top platform options across major database styles. Though maturity varies, the MCP servers discussed below represent some of the best vendor-backed offerings available today across relational SQL, NoSQL, graph, vector, and data warehouse systems.

These servers can be used by any MCP-compatible tool, IDE, or agent, whether it’s Claude Code, Codex, Cursor, Gemini CLI, Google Antigravity, VS Code, Windsurf, or something else. Adding them is typically simple, often involving a lightweight JSON addition to your MCP configuration file.

Amazon Aurora MCP Servers

MySQL and PostgreSQL are the world’s most widely-used open source databases. However, in both cases, there is no canonical MCP server—what we see are different MCP servers emerging across vendors. One of these vendors is Amazon Web Services (AWS), which offers official MCP servers for Amazon Aurora, its managed relational database service compatible with both MySQL and PostgreSQL.

According to the documentation on GitHub, the Amazon Aurora MySQL MCP Server can be used to convert natural language commands into MySQL-compatible SQL queries, which can then be executed against Aurora MySQL databases. Similarly, the Aurora Postgres MCP Server provides MCP tools for working on PostgreSQL databases. The Aurora DSQL MCP Server does the same for distributed Postgres databases.

AWS provides a growing portfolio of official MCP servers across its product line, including MCP servers for other Amazon database platforms like DynamoDB, ElastiCache, and Redshift. If you’re a heavy AWS shop and you want to enable LLM interactions with your data, these are sensible choices.

BigQuery MCP Server

BigQuery is Google’s cloud-based data analytics platform, and a popular data source for AI applications. BigQuery users with API access configured can also utilize the BigQuery MCP Server to interact with the platform using MCP-compatible AI clients.

Using the remote BigQuery MCP Server, engineers can generate and execute queries on data sources, or return metadata on datasets, tables, and schema. This can be done with a simple natural language prompt like “List the datasets in project PROJECT_ID.” Results are filterable by region, data set ID, column name, and more.

As part of Google’s fully-managed, remote-hosted MCP portfolio, the BigQuery MCP Server provides some peace of mind regarding security, maintenance, and ease of use for distributed teams. The MCP tools are subject to some limitations, however, in terms of query result size, processing time, and other factors. If you’re using BigQuery and you want more agentic control, you’ll want to check this one out.

Elastic Agent Builder

Another important database category includes platforms designed for keyword and semantic search. In this space, Elasticsearch is commonly deployed. Instead of providing a single MCP server, Elasticsearch provides the Elastic Agent Builder, which is a more comprehensive framework aimed at agentic workflows.

Using Elastic Agent Builder, you can chat with an agent to retrieve data context from Elasticsearch data and extend it into various environments. The Agent Builder itself includes an MCP server endpoint for programmability and exposing the agent to other clients.

Unlike others on this list, this is not a direct MCP interface to raw Elasticsearch APIs. Instead, it’s an interface that exposes skills from the agent platform. This should also not be confused with the Elasticsearch MCP Server, released in mid-2025, which has since been deprecated.

A possible downside of using this utility is that it includes an additional layer between your IDE or agent and the data you’re searching. The agent setup requires a higher subscription and takes additional steps to configure compared to other MCP servers.

That said, if you want an extensible common layer to interact with both Elasticsearch and external MCP servers, while centralizing responsibilities like permissions, this is an interesting proposition.

MCP servers for Neo4j

Graph databases are another key NoSQL database type these days, specializing in using nodes and edges to accelerate queries of highly interconnected data. Of these, Neo4j is a popular graph database option.

The Official MCP Server for Neo4j works with all kinds of Neo4j deployment (desktop, sandbox, self-managed, and the managed Neo4j Aura cloud service), and allows LLM-based clients to retrieve graph schema, execute read and write statements, execute graph algorithms, and more.

In addition, several other MCP servers for Neo4j are available from Neo4j Labs. These have specialized uses, such as generating Cypher queries from natural language, maintaining an in-memory graph database, modeling and visualizing graph, and interacting with the Neo4j Aura API.

The first MCP server for Neo4j was developed in December 2024. If you’re an avid Neo4j user and want to experiment with interacting with your graph databases in a chat-infused way, these stand as an interesting platform of servers.

MCP Toolbox for Databases

Google’s MCP Toolbox for Databases is a notable MCP server because it’s a popular catch-all for various database types. Unlike the other entries on this list, this server connects LLMs not to a single managed database, but unifies LLM access to multiple systems. The open source utility ships with pre-built configurations for nearly 30 databases including PostgreSQL, MySQL, SQL Server, Oracle Database, MongoDB, Redis, Neo4j, and Snowflake, as well as the databases in Google Cloud.

Once you define data sources in a tools.yaml file, you can use MCP Toolbox to perform structured queries or semantic searches against databases directly from within an IDE or agentic client using plain English. MCP tools translate commands into actions like list_tables and execute_sql.

MCP Toolbox for Databases is mature, originally built as a generative AI utility and later re-worked for MCP-style workflows. It offers numerous download, configuration, and interaction methods.

If you’re using a variety of databases on Google Cloud and elsewhere and you want an “all-in-one” MCP server, MCP Toolbox for Databases is a great place to start.

MongoDB MCP Server

MongoDB is the popular NoSQL document-oriented database. The creators of MongoDB have released an official MCP server that works with the open-source database as well as the company’s cloud-hosted MongoDB Atlas database platform.

The MongoDB MCP Server provides a number of tools to interact with MongoDB. You can query the database, return information on collections, create or remove collections or indexes, gather statistics on database usage, and more. Other tools enable MongoDB Atlas operations, like creating users or clusters, returning cluster data, and other functions.

The server’s tools are read-only by default but can be switched to allow write capabilities. It can be used locally, but also supports Streamable HTTP transport for remote servers, although that comes with greater security concerns.

For those using MongoDB and wanting to hook their AI-enabled IDE or CLI up with more automated powers, the official MongoDB MCP Server is worth checking out.

Pinecone MCP server

Among vector-native databases, Pinecone stands as a strong, widely used option with a well-designed API and comprehensive SDKs. The Pinecone MCP server extends this experience, allowing users to query its documentation and execute functionality via AI agents and AI-enabled IDEs.

To date, the Pinecone MCP server consists of nine MCP tools. These cover read-only actions, like knowledge gathering via the Pinecone official documentation and querying vector records, index metadata, configurations, and statistics. It also allows for write operations like updating records and creating new indexes.

Released in mid-2025, the Pinecone MCP server is one of the more complete early implementations, with easy configuration and installation.

For those using Pinecone who want to test new LLM-assisted workflows for creating indexes with embeddings, performing reranking, or testing results using natural language commands, the Pinecone MCP server is worth trying out.

Redis MCP

An ultra-fast in-memory database, Redis is commonly used for caching, real-time analytics, and other latency-sensitive use cases. And as you might have guessed, the company behind the Redis database provides an official MCP server. The server allows read, query, and write capabilities.

Developers can use Redis MCP from an LLM client to perform high-level actions to analyze, reference, or embed Redis data and interact with the Redis server within their prompts. The documentation suggests some example prompts for common use cases, such as “Cache this item,” “How many keys does my database have?,” and “What is user:1’s email?”

Unlike other MCP servers, which only allow a slice of platform capabilities, Redis MCP offers full Redis support. This enables working with Redis constructs such as hashes, lists, sets, sorted sets, streams, and more, according to the GitHub repository.

One possible drawback is that Redis MCP has yet to support Streamable HTTP transport. Until this is developed, the server is constrained to local deployment. But for those seeking a local MCP server to work with Redis data, this is the best choice.

Snowflake MCP Server

Snowflake is a cloud-hosted, AI-enabled data platform widely used in enterprise contexts for data warehousing, data analytics, and data engineering purposes. Compared to other data storage systems, Snowflake is unique in that it’s more fully managed and combines structured and non-structured data types.

The Snowflake MCP Server, available on GitHub, can be used to perform many of the standard Snowflake platform operations. This includes a “fuzzy” search of all records via Snowflake’s Cortex Search and structured data semantic lookups using Cortex Analyst.

Other abilities include object management operations like creating, updating, and deleting records. The server also can invoke other agentic-designed capabilities, like the ability to generate and execute SQL statements against back-end databases.

Snowflake MCP Server is well-thought-out and well-documented, with walkthroughs for various agent and deployment patterns. Those already building with Snowflake should find it complements the mechanics they already employ.

Supabase MCP Server

A longtime open-source favorite, PostgreSQL is one of the most popular and trusted object-relational SQL-based database systems. With an active open source community, Postgres has been maturing for decades. Given its open source nature, there isn’t a single “official” MCP server for the platform. Anthropic built an original reference implementation, but it’s now archived.

Instead, database platforms built on PostgreSQL provide different flavors of MCP servers, with a range of vendor neutrality and specificity. One notable option is the Supabase MCP Server, provided by Supabase, a cloud-based “back end as a service” and Postgres development platform.

Supabase MCP Server connects AI agents with Supabase projects, allowing engineers to issue natural language commands to manage tables, query data, get logs, fetch configuration information, and more. The Supabase MCP Server is pre-1.0 release and some features are still experimental.

If you’re an engineer using Supabase and looking for an MCP server to connect your AI assistant with your Postgres databases, this is a good tool to test out.

Other MCP servers for databases to consider

So far, we’ve reviewed official, vendor-backed MCP servers from some of the most-adopted managed databases. However, numerous MCP servers exist across other database platforms and types.

One MCP server that aggregates LLM access across various database types is DBHub, which works with MySQL, PostgreSQL, SQL Server, MariaDB, and SQLite. Developed by Bytebase, DBHub is described as a zero-dependency, token-efficient MCP server.

For SQL, the options are nearly endless. Official servers exist for Microsoft Azure SQL and DuckDB. PulseMCP catalogs more than 100 MCP servers for MySQL, although most are unofficial, solo-creator open source projects. Of these, one of the most starred is MCP Server for MySQL, developed by full-stack developer Ben Borla and optimized for Claude Code.

For Postgres, notable alternatives to Supabase include pgEdge Postgres MCP, Neon MCP server, and Postgres MCP Pro. For vector databases, others beyond Pinecone have been quick to adopt MCP as well, including Weaviate and Milvus.

Using MCP for databases: what to watch out for

Before diving into MCP servers for enterprise databases, it’s important to understand the security risks. For instance, prompt injection remains an unsolved problem, so it’s recommended to limit permissions for SQL statements.

To mitigate this, Supabase recommends enabling AI client settings that require manual approval for each tool call before execution. Experts also recommend assigning only the minimum permissions required and avoiding exposure of sensitive data like API credentials. Due diligence around authentication and authorization is especially important when hosting remote servers.

Lastly, to avoid shadow IT, it’s becoming common practice to catalog the internal MCP servers you use, even for experimental projects. For this, experts recommend an MCP registry that documents approved servers. An MCP registry improves both MCP server discovery and security awareness.

Licensing can be complicated, particularly when enterprises are forced to double-pay because the software they already own is only licensed for a specific environment, and moving it requires a whole different licensing model. Without proper portability rights, they need to make additional financial investments to run the same workloads in a different home.

AWS says its new Bring Your Own Media (BYOM) service eliminates this duplication. Customers can now reuse their existing Microsoft SQL Server media and licenses on Amazon Relational Database Service (RDS) with no additional licensing fees.

This means enterprises no longer have to justify workload migrations to the cloud against existing licensing commitments and infrastructure investments, AWS said.

“What used to be a licensing barrier between operational SQL Server data and agentic AI is now gone,” AWS data engineer Srikanth Katakam and product marketing manager Colleen Betik wrote in a blog post announcing the service.

Avoiding the double-licensing problem

To drive true value, agentic AI apps must have access to elastic GPU capacity as well as direct, low-latency access to the data they need to reason and make decisions. But, AWS argued, that can be difficult in self-managed data centers and on premises infrastructure with limited access to agentic AI cloud-native services. But a lot of enterprise data lives in Microsoft SQL Server, and until now customers have had to pay for a second license to use a fully managed cloud service like RDS.

Now, with BYOM on Amazon RDS for SQL Server, enterprises can reuse their existing SQL Server Enterprise Edition or Standard Edition licenses through a “lift-and-shift” model. This brings their data into a fully-managed environment.

According to the licensing distribution terms, enterprises must provide their licensed SQL Server Release to Manufacturing (RTM) media to Amazon RDS. They can then upload that media to Amazon S3 and launch BYOM instances. Enterprises must configure AWS License Manager to perform automatic instance tracking.

From there, RDS automates patching, backups, high availability, and monitoring, while providing direct access to agentic AI and analytics services native to AWS. The platform also reports vCPU usage to provide visibility into SQL Server license usage, Katakam and Betik wrote.

“You do not need to choose between protecting your SQL Server licensing investments and giving your data a path to the AWS analytics and agentic AI services redefining what’s possible in the cloud,” they said. 

It’s important to note that this service is only available to enterprises with Microsoft Software Assurance (SA), an add-on which supports mobility and rehosting of existing licenses. Enterprises must verify that their SQL Server licenses comply with Microsoft’s licensing agreement, and that they have a SQL Server License (Standard or Enterprise) with SA. They also must submit a License Mobility Verification Form to Microsoft; it, in turn, will notify AWS once verification is complete.

AWS emphasized that enterprises remain responsible for compliance; it does not block operations if license limits are exceeded.

More control over workloads, loosening Microsoft’s grip

The advantage of this service, explained Mike Leone, principal analyst at Moor Insights & Strategy, is that enterprises can get a Microsoft SQL Server workload onto a managed AWS service without rewriting it for Aurora or paying for the license twice. This means they can modernize on their own schedule rather than being forced into rewrites before they’re ready.

“For a lot of shops, that control over timing is worth more than the license saving itself,” Leone said.

This also loosens Microsoft’s grip on enterprise workloads, because organizations finally have somewhere else to run SQL Server using licenses they already own, he noted. Realistically, their dependency shifts to AWS rather than disappearing entirely, as their data ends up living next to AWS’s AI services.

“For a lot of teams, that’s a trade they’re glad to make for the managed infrastructure and the AI tooling they get in return,” Leone said.

Yaz Palanichamy, a senior advisory analyst at Info-Tech Research Group, also pointed to significant improvements stemming from the migration. Notably, it allows organizations to transition away from static, linear, or reactive storage capabilities towards more dynamically intelligent environments.

“The key is to balance the unification of transactional data towards managed AI and machine learning pipelines,” he said.

Enterprises take on new responsibilities

The catch? Leone noted that enterprises now own the task of licensing compliance. Staying current on SA, the Microsoft maintenance contract that makes any of this legal, and the license mobility rules, “become your headache instead of something baked into the bill,” he said.

Furthermore, he added, lift-and-shift has a way of turning into “lift-and-forget” when enterprises move SQL Server as-is and never actually modernize, “so you end up carrying all the old baggage onto a shinier platform.”

Palanichamy also pointed to skyrocketing AWS costs, since operationalizing AI agents requires a considerable amount of data ingestion and querying. This can potentially be cost prohibitive on RDS for SQL Server.

“One aspect to consider would be the relative time to production value, so that enterprises can better handle the management of volatile AI workloads,” he said.

Just one challenge in the AI race

Although AWS frames licensing complexity as a roadblock to agentic AI, analysts say it’s really just one piece of the puzzle.

Organizations are not in an AI-ready state due to a number of factors, Palanichamy noted. This could be total cost of ownership (TCO)-related, a lack of appropriate acceptable use policies (AUPs), or concerns around security and compliance.

If and when enterprises are ready to adopt AI, they must perform thorough needs analysis/process optimization benchmarking exercises to determine what workflows could benefit from the technology, he said, and, conversely, flag workflows where AI could prove “an impediment or potential disservice.”

Leone also pointed out that moving SQL Server onto RDS doesn’t “magically make your data agent-ready,” nor does it make it smarter. The data proximity and managed plumbing are what actually support agents. Sitting data next to Bedrock and the rest of AWS’s AI services means builders “stop wasting time shipping the data around or building one-off integrations every time an agent needs it.”

Ultimately, though, licensing isn’t the real issue when it comes to AI; it’s more of a migration problem. The real roadblocks are messy data, questionable governance, and teams that aren’t ready to run it in production, and, Leone said, “no change to a license bill touches a single one of them.”

I’ve seen a lot of promising AI prototypes fall apart after launch. And it’s rarely because the model was bad. More often, the problem starts much earlier; teams treat the data layer like something they can figure out later.

They’ll spend weeks fine-tuning prompts, testing models and debating evaluation scores, then throw together the retrieval pipeline over a weekend and move on. At first, everything looks great in demos. But a few months later, the system gives outdated answers; the embeddings no longer match the source documents, and nobody fully understands what changed.

What started as an impressive prototype slowly becomes difficult to trust in production. The teams that avoid this tend to realize one thing early: Embedding pipelines are fundamentally a data engineering problem, not an entirely new AI discipline. It’s still ETL (Extract, Load, Transform) at its core, but with embeddings and vector stores as the destination instead of a warehouse.

Once you start looking at it that way, a lot of things become clearer. Problems like versioning, data freshness, lineage and retries stop feeling “AI-specific.” They’re data infrastructure problems we’ve already spent years learning how to solve.

Why do we need embedding pipelines?

Large language models are extraordinary reasoners trapped inside a time capsule. When training ends, the model’s knowledge is sealed. It does not know what your team decided in last quarter’s strategy review. It has never read the support ticket that came in this morning. It cannot find the clause buried on page 47 of your master service agreement. It’s brilliant, but blind to anything specific to your organization.

Layer on top of that a hard context window limit, a ceiling on how much text the model can process in a single interaction, and you have a clear problem: you cannot just hand it everything you own.

The answer the industry converged on is retrieval-augmented generation, or RAG. Instead of stuffing everything into the context window, you build a retrieval layer that fetches only the most relevant pieces of information at the moment a question is asked and passes just those to the model. That retrieval layer is powered by a vector database, and the process that populates it, which is taking raw documents and transforming them into searchable semantic representations, is what I mean when I say embedding pipeline.

Every team building an internal AI assistant, a smarter enterprise search tool, an automated customer support agent or a document Q&A system needs one. The question is not whether to build it. The question is whether you build it like a prototype or like infrastructure.

How an embedding pipeline works

An embedding pipeline has three stages: ingestion, chunking and indexing. Here is what each one means and how I relate them to a typical ETL process.

Ingestion is extraction

Getting your raw content, PDFs, wiki pages, Word documents, database records, transcripts, out of wherever it lives and into the pipeline. This is ETL’s extract stage, almost verbatim.

I see teams cut corners here more than anywhere else, and it’s often where production systems first start to fail. A document gets updated, but the pipeline doesn’t pick it up. A file gets deleted, but its chunks remain in the index, still returning outdated answers months later. And because there’s no obvious error, no one reports it.

The fix is Change Data Capture (CDC). This maintains a manifest of every document you have ingested, a content hash and a timestamp. On each run, we compare sources against that manifest, re-ingest what changed, delete what is gone and treat your document the way you would treat any source table you are syncing incrementally.

Chunking is transformation

Once your documents are in the pipeline, you cannot embed them whole. A 30-page technical report is too long to represent meaningfully as a single vector, and even if it were not, returning the entire report in response to a narrow question would bury the model in irrelevant context.

Chunking is the process of breaking each document into smaller pieces that are focused enough to embed accurately and retrieve precisely. This is ETL’s transform stage, and it deserves the same level of design discipline.

The most common mistake I see is treating chunk size as a default configuration option rather than a product decision. It is not. The right chunk size depends entirely on the nature of your content and the nature of your queries. Dense technical documentation needs finer granularity than a collection of FAQs. A legal contract with clause-level logic needs different treatment than a set of onboarding emails. What works for one document will actively degrade retrieval quality on another.

My strong preference is to treat your chunking configuration as a versioned pipeline parameter, not hardcoded logic. When you change it, and trust me, you will. You need to re-chunk in a controlled, observable way, compare retrieval quality before and after, and roll back if it degrades. That is just good transform-layer hygiene. It is no different from versioning a data cleaning rule or a field mapping.

Indexing is the load

The final stage is where chunked text gets converted into vectors and stored in a vector database where it can be searched by semantic similarity rather than keyword match.

In the conversion step, embedding is handled by a model specifically trained to turn text into dense numerical representations that encode meaning. Two chunks expressing the same idea in different words will produce vectors that cluster close together in that mathematical space. Two chunks discussing entirely different topics will sit far apart. When a user asks a question, the system embeds that question the same way, finds the chunks whose vectors are nearest, and returns them as context for the model to reason over.

That is a genuinely new capability. But the discipline around indexing is not. One data engineering principle I keep coming back to is versioning.

In embedding pipelines, every chunk in your index should be tagged with the embedding model name and version used to generate it, this is non-negotiable. Embedding models evolve, and vectors produced by different versions are not comparable in a reliable way. You cannot safely search across them as if they are interchangeable.

This exact problem shows up when teams upgrade embedding models mid-pipeline without a proper migration plan. You end up mixing vectors from different generations in the same index, and retrieval starts to degrade in ways that are hard to detect. The system just quietly begins returning subtly wrong answers.

I treat an embedding model upgrade the same way I treat a schema migration: Plan it explicitly, execute it in full and validate retrieval quality on a representative query set. The stakes are the same as any breaking change to your data model.

Pipeline observability is not optional

Once an embedding pipeline is running in production, the question shifts from “did it run” to “did it run correctly.” That distinction matters more here than in most pipelines, because failures are rarely loud because the index looks fine, queries return without errors and the system quietly surfaces wrong answers until someone notices the AI has stopped being useful.

The same observability discipline that makes any data pipeline trustworthy applies directly here. Once you treat embedding pipelines as production systems, you stop thinking in isolated steps and start thinking in signals. For example, chunk counts per document become a simple but powerful health check, a sudden drop is usually not a model issue, but a sign of broken ingestion or upstream parsing failures.

You also need a “golden set” of queries with known-good outputs. This runs after every pipeline change, much like data quality checks after a transformation. This is how you catch regressions that don’t show up as explicit failures.

On top of that, you can track lineage: Which embedding model version produced which chunks, and when each document was last ingested. That makes it possible to trace retrieval issues back to specific changes instead of guessing.

And finally, freshness becomes a first-class signal. If documents start going stale beyond an acceptable threshold, that should surface in monitoring long before users experience degraded results.

The metric that ties it all together is retrieval quality over time. Treat it like any other pipeline SLA, measured, tracked and owned.

Summary

Embedding pipelines definitely come with a lot of new language, new tools and a genuinely different capability in the semantic layer. But the funny thing is, the principles that actually make them reliable in production are not new at all.

We have versioning, freshness, quality checks and monitoring. These are problems data engineering has already spent years solving.

The real work is taking that same discipline and applying it to a pipeline that just happens to output vectors instead of rows in a table. Once you start seeing it that way, a lot of the chaos around AI systems becomes much easier to reason about.

That’s the difference between building a cool AI demo and building something people can actually depend on. One is a prototype, whereas the other is infrastructure.

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