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.

Anyone can build an app now. But nobody seems to care.

Well, not nobody. VCs keep funding startups that add AI to, well, everything. But users aren’t buying the massive influx of new apps. In a chart shared by Jen Zhu Scott based on the new National Bureau of Economic Research’s working paper “Writing Code vs. Shipping Code,” iOS app releases have exploded since the advent of agentic AI. That would perhaps be cause for celebration had app reviews not declined during this same period, and apps with significant usage have stayed essentially flat.

In other words, more apps but almost nobody new showing up to use them.

For those of us that grew up in open source, it’s a familiar problem. The greater the abundance of code, the greater the need to help would-be customers navigate it through marketing (including branding), sales, etc. AI is creating so much noise, in terms of new code, new products, etc., that the real work has shifted to taste-making.

Getting more but not using more

I’ve been saying for a while that developer productivity isn’t about producing more code faster. Or at least it shouldn’t be. Productivity is about producing well-architected, secure, maintainable code that solves a problem someone actually has.

That’s a very different thing. It’s not something AI can fix; at least, not yet.

Charity Majors put it more bluntly in a post I wrote in 2024: “Writing code is the easiest part of software engineering,” she said. The harder parts are figuring out what to build, integrating it into a larger system, validating that it works, maintaining it over time, and getting humans to trust it enough to use it.

Turns out the harder parts are really hard. Mert Demirer, Leon Musolff, and Liyuan Yang tracked more than 100,000 GitHub developers alongside their AI usage telemetry. Autocomplete, interactive agents, and autonomous agents each ramped raw coding activity, with cumulative effects on commits of 40%, 140%, and 180%. That sounds great until you look how the gains attenuate the closer you get to actual users. For example, that 180% jump in commits became roughly 50% more projects and just 30% more actual releases. The report’s authors call this the weak-link problem: The strong link (writing code) got much stronger, while the weak links (everything else humans have to do) didn’t. The estimated elasticity of substitution between AI and human effort is 0.25, which is economist for “these complement each other, but they don’t replace each other.”

When the authors checked four major app marketplaces, they found a bump in new apps but no increase in total usage. In other words, we’re getting better at creating things with AI’s help; apparently we’re not good at turning that into user interest. User attention (and budget) is finite. The hard part is to figure out how to get users to care enough to pay (with their time or their money).

Learning to love marketing

So what’s actually scarce in a world of near-infinite software? Not code, for sure. No, what’s scarce is attention, trust, and a reason to switch.

That means the durable advantages in software increasingly live in the parts of the business developers often undervalue, like a recognizable brand (something that Red Hat figured out early on in open source) or a channel they already use. Or it could be in areas that developers appreciate but don’t pay for, like good documentation or a welcoming community.

This isn’t a new idea; it’s just newly unavoidable. We’re watching something similar inside enterprises, where AI adoption is wildly uneven, not because the technology has no value, but because the organizational plumbing around it often hasn’t been built. This is why a new kind of speed is important. I once argued that speed was the killer app, and that was mostly true. Today the more interesting speed is how quickly you can earn trust, fit into a workflow, answer objections, and get adopted.

Years ago I wrote that RethinkDB was dead and MongoDB wasn’t what killed it. RethinkDB was at the time, by many technical measures, a better database, built around “correctness, simplicity, and consistency.” It still lost—and badly. Its founder’s own postmortem was unsparing: They had picked a brutal market and tuned the product to the wrong definition of good. Being technically right turned out to have almost nothing to do with getting adopted.

With AI we’re generating a thousand smaller “RethinkDBs,” only faster and with nicer landing pages generated by the same model that wrote the code. These aren’t going to win, any more than RethinkDB was able to unseat MongoDB. It’s not just about the tech, but rather about making that tech fit within a user’s or enterprise’s world and making it easy to adopt. AI makes this harder, not easier.

It’s a cliché that developers don’t like marketing. It’s also false in my inexperience. What developers don’t like is traditional marketing. During my time at MongoDB and now at Oracle, my developer relations teams have focused on offering developers deep, hands-on enablement, and the response has been fantastic. Is a technical tutorial marketing? Of course it is. So is a forward-deployed engineer working side by side with an enterprise’s engineers to help them effectively use your tech. Just because it’s not a 30-second Super Bowl ad doesn’t mean it’s not marketing.

Years ago the Dilbert cartoon captured developers’ disdain for marketing. It was funny then, and it’s funny now, but it has never been true.

Years ago Matt Klein, creator of the open source Envoy project, once talked me through all the non-development work that goes into making an open source project thrive. As he reflected, “If you look at what I did in 2016 and early 2017 to [introduce] and grow the project, it was not technical.” So what was it, if not core engineering? “It was all leadership, public relations, marketing, documentation, etc., and I did it all myself and I nearly killed myself [doing it].” As he summed it up for me, it was a “f—ing lot of work,” and much (most?) of it wasn’t about code. It was about helping users appreciate the value of that code.

TL;DR? The boring but essential go-to-market grind has always been the real job. It’s what will separate winners from losers in AI.

The boring work wins

None of this is an argument against AI coding tools. Developers should absolutely use them. I use them constantly.

But AI can’t compensate for the hard work that goes into making a product successful in the market. In a world drowning in AI-generated sameness, taste becomes a competitive advantage. For me, “taste” translates into knowing what not to build, or what not to publish. It’s also all about knowing how to market one’s product, which might include ads but definitely needs to incorporate technical training that helps developers make sense of your code.

In short, there’s no Field of Dreams “build it and they will come.” There’s just a lot of hard, human work to help real people find and use your code.

Agentic AI has moved from conference hype to a budget line item. This is where the conversation gets more interesting and more uncomfortable. Unlike traditional AI systems that respond to a single prompt, classify a document, recommend an action, or generate a summary, agentic AI systems are designed to pursue goals. They plan, call tools, inspect results, retry failed steps, consult memory, hand off tasks to other agents, and sometimes critique their own work before producing an answer or taking an action.

That extra autonomy is the value proposition. It also introduces the cost problem.

A single chatbot interaction may consume a few thousand tokens. A useful agentic workflow can consume hundreds of thousands or millions of tokens per day because it does more than answer a question. It decomposes the problem, retrieves context, reasons through options, invokes APIs, checks the output, and often runs multiple passes before reaching a result. Therefore, the economics need to be understood at the level of “agent instances,” not just model calls.

For the estimates below, I am using a blended token cost of $3 dollars per million tokens. This is not intended to reflect a single vendor’s list price. It is a blended planning figure that assumes a mix of input and output tokens, reasoning steps, retrieval-augmented generation, summarization, tool calls, memory updates, and occasional use of larger context windows. Some enterprises will pay less through volume discounts or by routing work to smaller models. Others will pay more by using premium models, long-context prompts, web browsing, large document ingestion, and repeated reasoning loops.

The basic formula is straightforward. If an agent consumes 2 million tokens per day, it consumes 730 million tokens per year. At $3 per million tokens, that single agent costs about $2,190 per year in token burn. That number sounds surprisingly low until you multiply it by the number of agents, workflows, and users, plus the surrounding infrastructure required to run these systems safely.

What an agent really costs

In the model used here, the annual token-only cost per agent ranges from about $1,095 to $3,833, depending on the use case.

• A lightweight HR recruiting or onboarding agent that consumes one million tokens per day costs about $1,095 per year.
• A more demanding software engineering agent that consumes 3.5 million tokens per day costs about $3,833 per year.
• Customer support agents are roughly $2,190 per year.
• Legal contract agents cost about $2,409 per year.
• Security triage agents run about $2,738 per year.
• Research agents are around $3,066 per year.

These figures are useful but incomplete. They include only LLM token consumption and exclude orchestration platforms, vector databases, observability, model evaluation, security controls, workflow monitoring, human review, enterprise application integration, data pipelines, audit logging, prompt management, and the engineers needed to build and maintain the systems. In real deployments, I would expect the all-in operating cost to be two to five times the raw token cost. For regulated or mission-critical environments, the multiplier can be even higher.

This is where many agentic AI business cases become less clear. The model call may be inexpensive, but the system around the model is not. An agent that can update a customer relationship management system, approve a refund, generate a purchase order, or recommend a security containment action needs guardrails, permissions, logging, rollback mechanisms, and human escalation paths. These are not optional features. They are the difference between a demo and an enterprise system.

Use cases that make economic sense

Customer support is one of the more obvious use cases. A typical support automation deployment may use eight agents: an intake classifier, a knowledge retrieval agent, a response drafting agent, an escalation agent, a quality review agent, a CRM update agent, a sentiment detection agent, and an analytics agent. At two million tokens per agent per day, each agent costs about $2,190 per year in token burn, bringing the annual total to roughly $17,520. If that system deflects even a modest number of tickets or improves agent productivity, the economics can be attractive.

Sales development is another practical example. A five-agent system for account research, lead enrichment, email personalization, CRM updates, and follow-up scheduling may consume 1.2 million tokens per agent per day. That results in an annual cost of about $1,314 per agent, or $6,570 for the full agent team. This can be compelling if it improves pipeline quality, but it can also be wasteful if agents generate low-quality outreach at scale. The cost of brand damage is not measured in tokens.

Software engineering is more expensive but potentially more valuable. A 12-agent system covering requirements analysis, architecture, code generation, testing, review, security checks, documentation, CI debugging, refactoring, release notes, dependency analysis, and hot-fix support may consume 3.5 million tokens per agent per day. That works out to about $3,833 per agent annually or roughly $45,990 for the full system. Compared with engineering salaries, the token cost is small. The real question is whether the system reliably improves throughput without increasing defects, security vulnerabilities, or maintenance complexity.

Security operations also fit the agentic model because the work is repetitive, time-sensitive, and context-intensive. A 10-agent security triage system could include agents for alert triage, log analysis, threat intelligence, endpoint investigation, network investigation, incident summarization, ticketing, compliance evidence, escalation, and post-mortem. At 2.5 million tokens per agent per day, the annual token cost is about $2,738 per agent or $27,375 for the system. This is easy to justify if it reduces alert fatigue and accelerates response, but risky if the agents hallucinate causality or bury critical signals in confident summaries.

Finance, legal, healthcare administration, market research, HR, and supply chain are also viable:

• A six-agent finance close system may cost about $9,855 per year in token burn.
• A four-agent legal contract review system may cost about $9,636.
• A seven-agent healthcare administrative workflow may cost about $13,797.
• Competitive intelligence using six agents may cost $18,396.
• HR recruiting and onboarding using five agents may cost $5,475.
• Supply chain planning and exception management using eight agents may cost $21,024.

Across all 10 example use cases I’ve mentioned in this section, the model assumes 71 agents and a total annual token burn of about $175,638.

The comparison with traditional AI

The economics of agentic AI should always be compared with simpler approaches. Traditional AI, workflow automation, rule engines, robotic process automation, and non-agentic LLM calls are often cheaper, easier to govern, and more predictable. Agentic AI is usually overkill for tasks like classification, extraction, summarization, routing, or drafting within a narrow context. A deterministic workflow with a single model call can do the job at a fraction of the cost and risk.

Agentic systems make sense when the process requires judgment across multiple steps, dynamic planning, tool use, exception handling, and adaptation to incomplete information. They are valuable when the path to the answer cannot be fully scripted in advance. They are less valuable when enterprises use them as a trendy substitute for basic automation.

The best architecture is usually hybrid. Use traditional automation where the process is stable. Use non-agentic AI where the task is bounded. Use agentic AI only where autonomy creates measurable leverage. That means fewer agents, tighter scopes, explicit budgets, model routing, token monitoring, and human checkpoints for high-impact decisions.

The financial mistake many organizations will make is to treat agents as digital employees with near-zero marginal cost. They are not. They are probabilistic software components that consume tokens, trigger tools, create operational dependencies, and require supervision. The token bill may be manageable. The governance bill may not be.

Agentic AI can absolutely be worth the money. In many cases, the annual token burn for a useful agent team is less than the loaded cost of a single employee. But that is not the same as saying it is cheap. Companies must measure agent cost per completed business outcome, not cost per prompt or cost per model call. In the end, the question is not how much an agent costs. Ask yourself the right question: Does the autonomy an agent provides outweigh the complexity it introduces?

For the past two years, enterprises have focused on grounding AI systems in internal documents, databases and knowledge repositories. Microsoft now contends that the next challenge is giving those systems reliable access to the outside world as they move into production.

At its ongoing annual Build conference, Microsoft unveiled Web IQ, a new suite of AI-native APIs designed to connect AI agents and applications to real-time information from across the web, including web pages, news, images and videos.

The goal is to help developers build more accurate and context-aware AI systems while reducing the complexity of integrating web search, retrieval and grounding capabilities into enterprise applications, the company wrote in a blog post.

The APIs already underpins grounding for Microsoft Copilot and ChatGPT, and unlike traditional search APIs are designed to retrieve highly relevant information while minimizing token consumption, helping reduce both inference costs and response latency, Microsoft said.

Reducing the cost and complexity of web grounding

That focus on reducing inference costs and response latency to deliver Web IQ’s search capabilities will be valuable for CIOs and developers, said Phil Fersht, chief analyst at HFS Research.

“Developers have typically stitched this together themselves using search APIs, web scraping, retrieval-augmented generation, vector databases, custom ranking logic, crawling tools and separate orchestration layers. That works, but it is messy, brittle and expensive to maintain,” he said.

“The hard part is not just finding a web page. It is retrieving the right evidence, ranking it, selecting useful passages, reducing token waste, respecting publisher controls and doing all this fast enough for multi-step agents,” he added.

An agent running in production will typically run at least five or ten retrieval steps and in such scenarios latency and cost can become “ugly,” said Moor Insights and Strategy principal analyst Mike Leone.

“I’ve watched plenty of teams handle this by gluing a search API onto a model and hoping the model can sort through whatever HTML comes back, which isn’t ideal,” he said.

Simplifying development of applications

Beyond the infrastructure and cost advantages, Web IQ could also simplify the AI application building process developers, said Stephanie Walter, practice lead of the AI stack at HyperFrame Research.

“The value of Web IQ is that Microsoft is trying to make web grounding a reusable, agent-native service rather than a bespoke integration,” Walter said.

That reusability, Fersht said, will matter to CIOs as most enterprises do not want every development team rebuilding its own web grounding stack: “They need a common capability that is governed, scalable, low-latency and economically efficient.”

Still, Microsoft is not entering an untapped market and there are several offerings already available, including OpenAI web search, Google grounding, Perplexity APIs, Bing Search APIs, Azure AI Search, vector databases, and custom RAG pipelines.

However, Microsoft might have an advantage with IQ, despite Google’s strength in search and web-scale knowledge, OpenAI’s tooling push, and AWS’ Bedrock-data-service-partner integrations combo, Fersht said.

“Microsoft’s differentiation is that it can combine Bing’s global web index with Azure AI, Copilot, Foundry, Microsoft 365 and enterprise developer channels,” he said.

The other advantage is the existence of existing IQ offerings, such as Work IQ, Fabric IQ, and Foundry IQ, all of which are targeted towards adding more business context for agentic systems, Fersht added.

But uptake, Walter said, will depend on whether these integrations translate into better production outcomes.

Web IQ is currently available in limited access for select Azure customers. Enterprises can request access to Web IQ via their Microsoft account team or submit a form.

Beyond direct API access, developers can also configure the model-agnostic Web IQ as an MCP tool within Foundry IQ, a Microsoft spokesperson said.

Google has released new tools that allow developers to run agentic AI workflows locally using Gemma 4 12B, a 12-billion-parameter model from Google DeepMind.

In a blog post, the company said the model, combined with the Google AI Edge stack, can be used to build and test applications on everyday machines. The model-runtime combination supports capabilities such as autonomous data processing, visual insight generation, webpage creation, and tool use.

The release includes Google AI Edge Gallery for macOS, where developers can use Gemma 4 12B to generate and run scripts for tasks such as data analysis. Google also said its Eloquent voice dictation and editing app now runs fully on-device on macOS, with support for local transcription and voice-driven text editing.

Google has also expanded LiteRT-LM, its lightweight command-line tool for running language models locally, with a new serve command. The company said this allows the CLI to act as a local LLM server and lets developers connect Gemma 4 12B to standard tools, SDKs, and frameworks through a local endpoint.

“Your data stays on your device while maintaining reliable responsiveness, utility, and cost efficiency,” the company said in the blog post.

The announcement comes as enterprises are looking beyond large, general-purpose models for some AI workloads. Gartner predicted that by 2027, organizations will use small, task-specific AI models at least three times more than general-purpose large language models, citing demand for more contextualized and cost-effective AI systems.

Challenges to overcome

But running agents on employee devices brings a number of problems. Companies must work within the limits of endpoint hardware, which can restrict the size of models that run effectively and the number of model instances that can operate at one time.

“While the AI can now fit on a laptop, enterprise IT infrastructure is largely unprepared to manage it,” said Rishi Padhi, Principal Analyst at Gartner. “Even highly optimized models like the Gemma 4 12B require around 16GB of unified memory or VRAM to run alongside standard applications. Many standard-issue enterprise laptops lack the memory bandwidth and NPUs/GPUs required for fluid, multi-turn agentic execution.”

Anand Joshi, AI analyst at TechInsights, said local deployment also changes the nature of the workloads. On a PC, search may mean finding information across internal folders and files. In a data center, the same function could involve searching the internet or querying a large database such as SQL.

“The framework for local deployment of agentic AI is different from that of a data center,” Joshi said. “The models are smaller; you can run only one instance of a large model at a time. You are limited by memory, CPU, and so on.”

Security and governance are also likely to become bigger concerns as AI agents move closer to enterprise endpoints. Agentic AI is designed to take actions, creating new security risks when local models are given access to employee files or allowed to interact directly with applications and scripts.

“Sandboxing these agents without breaking their utility is still a major operational challenge,” Padhi added. “And all this while enterprises need to audit AI usage for compliance and security. When inference happens entirely offline, capturing logs, tracking model drift, and ensuring employees are using the approved, compliant ways for a model becomes incredibly difficult.”

The cost tradeoff

Running AI agents locally could reduce some cloud inference costs, but the savings may be offset in the near term by higher spending on endpoint hardware and management.

“First and foremost, it is an OpEx-to-CapEx shift, as it shifts that financial burden by forcing accelerated hardware refresh cycles for premium PCs or edge devices,” Padhi said. “It would require buying expensive, high-memory laptops for employees at a time when memflation in the hardware industry is already driving up end-user average selling prices for laptops.”

Many enterprises refreshed PCs in 2025 to support Windows 11, but at that point most AI inference still ran in the cloud, and the case for on-device AI remained unclear, Padhi said.

Enterprises may therefore move cautiously, buying AI-capable PCs only where local inference has a clear business case.

Over time, however, on-device AI could make enterprise AI spending more predictable by reducing exposure to variable cloud inference bills. The tradeoff is that companies may face a higher baseline cost for equipping and managing employees’ devices.

Complementing cloud AI

For enterprises, local AI is unlikely to replace cloud-based AI outright. Analysts said local AI is more likely to be used for workloads that benefit from endpoint processing, especially when applications must operate offline or when privacy and response times are critical.

“For local agentic AI to proliferate, the use cases on edge will have to complement data center/cloud use cases,” Joshi said. “I don’t expect local agentic AI to replace cloud AI, but it has potential to take a slice away from the cloud, and models like Gemma are significant steps towards enabling that.”

The market, Joshi added, is still determining where local AI fits best. “I estimate that use cases that require privacy or have strict latency needs will move to local node first, with further migration of others in the next 2-3 years,” he said.

Padhi said model placement will depend on the privacy requirements of a workload, the computing power it needs, and where the relevant data resides. Tasks such as code generation or analysis of local files could increasingly run on employee devices, while enterprise-wide RAG systems and more complex AI workflows are likely to remain cloud-based.

Artificial intelligence does not advance at the same pace across industries. It presses forward in some directions while lagging behind in others.

Spend time with today’s most advanced AI applications, and this contrast becomes obvious. In software development, AI is quickly becoming ubiquitous. It writes production-ready code, explains obscure libraries, and iterates at a pace human teams have difficulty matching.

But place that same AI model inside a complex customer support workflow or ask it to reason through a nuanced clinical scenario, and the cracks begin to show. Multi-step reasoning falters. Context gets lost. Performance drops in ways that can seem inconsistent with the model’s strengths elsewhere.

These AI models are often similar. They run on similar hardware and are often trained in similar ways. So why the mismatch in performance across tasks? The simplest explanation is also the most overlooked: data.

Software engineering benefits from an immense, structured, and highly visible digital record. Code is written in standardized languages, benefits from robust documentation, is reviewed in public forums, and is discussed at scale. That ecosystem has generated a robust and massively useful pool of training material.

Other fields often do not. For example, healthcare data is scattered across institutions, wrapped in privacy constraints, expressed in multiple modalities, and rarely ready out-of-the-box for AI training. Enterprise workflows are captured in internal systems that were never designed for training AI. Multilingual speech data varies widely in quality and representation.

This imbalance creates what I describe as “the data gap.” This is the distance between what models are capable of in theory and what they can achieve in practice, because the right data does not yet exist in usable form. Closing this data gap may be the most important—and least glamorous—challenge in AI today.

The missing pillar of AI progress

Three forces are driving the recent advances in AI: the models, the chips, and the data.

On the AI models, the field has invested heavily. Major research organizations employ thousands of researchers and scientists who are actively refining architectures, training techniques, and evaluation methods. Breakthroughs are measured in benchmark scores, conference papers, and model performance on human tasks. On the computing chips, the investment has been equally intense. Hardware manufacturers and infrastructure providers are pouring billions of dollars into building and supporting data centers that deliver faster results via large-scale training.

Yet data has not received the same institutional focus for AI development. Conversations with researchers at frontier AI labs share a similar frustration that today’s model capabilities in key use cases, such as healthcare, are limited less by architectural imagination and more by the availability of high-quality, domain-specific data. The bottleneck is not always a lack of ideas, but a lack of reliable inputs.

We are long past scraping the internet for useful data, and this path does not scale. Progress depends on building and curating datasets that reflect the complexity of true lived experience and organizational processes. That work requires both scientific rigor and research specialization in the field of data for AI.

The dataset behind every leap

The history of AI reinforces a consistent lesson: major leaps in model capability follow major leaps in the availability of quality data. From early vision systems that relied on clearly labeled images to today’s language models trained on massive text collections, each major leap has depended on access to more high-quality data.

Architectural innovation alone is rarely enough. The value of these new approaches only emerges when paired with large, structured, and representative datasets that reveal what the models can actually do in practice. Whether in vision or language, progress has depended on the painstaking work of collecting, organizing, and validating the underlying data.

Large language models illustrate this clearly. Their emergence was not just the result of better training techniques, but of access to an unprecedented volume of data. The models did not generate that data. They relied on it. That pattern raises a pressing question for the present: who is building the next generation of foundational datasets?

Across domains ranging from healthcare to audio to agentic task performance, there is no widely accepted blueprint. What constitutes a gold-standard dataset for training an AI agent to handle complex enterprise tasks? What does a clinically meaningful evaluation look like for a model that will assist in medical decision-making? How should multilingual speech data be curated to ensure broad representation and reliable performance?

These are not simple sourcing problems. They are fundamental research challenges that need to be solved.

When data is treated as a commodity

Too often, consequential data decisions are handled like procurement exercises. An organization requests “medical conversations” or “wildlife scenes,” and the request is routed to internal procurement or data sourcing teams, or to external data vendors, who assemble data that appears to match the description. The implicit assumption is that data is interchangeable, that one dataset is as good as another so long as it meets a basic specification.

Actual application suggests otherwise. Seemingly small choices about factors such as inclusion criteria, annotation standards, filtering rules, and validation protocols can dramatically alter downstream performance. Data design shapes model behavior as much as architecture does.

Three structural issues compound the problem:

• Capacity: There are relatively few specialized teams dedicated to building domain-specific datasets at the highest level of rigor. Talent and funding have gravitated toward model development and hardware innovation. Data work often operates in the background, even though it underpins both.
• Design: Constructing a dataset is a distinct discipline from designing a neural network. It requires expertise in experimental design, domain knowledge, and statistical validation. Expecting model researchers to simultaneously shoulder the full burden of data research, while also training and evaluating the models, overlooks the complexity of the upstream task.
• Translation: The researchers who are requesting specific data sources to improve the models are often not the same people responsible for sourcing that data. As a result, nuances and research-backed expertise can often be missing or diluted as requests pass through layers of procurement and vendor relationships. The result can be data that meets the specification sheet, but in fact fails to advance model performance.

The rise of annotation providers and reinforcement learning services has addressed part of the need. Rating model outputs, labeling text, and evaluating structured information are essential for many optimization tasks. But these activities generate data that is carefully constructed for specific, bounded purposes.

The frontier challenges in AI require more. They demand datasets derived from real human activity and organic organizational processes. Such data is complex, multimodal, and sensitive. It is rarely AI-ready by default. And converting it into reliable training and evaluation material is a scientific undertaking.

The need for scientific rigor for the AI data layer

If high-quality data is a central bottleneck, then scientific rigor is part of the solution. Just as leading model-builders have dedicated research labs and hardware has dedicated development ecosystems, the data layer for AI requires focused, scientifically-grounded institutions.

This means engaging directly with core questions like dataset design, evaluation methodology, and quality control. The conversation cannot end at volume; it must address data structure, representativeness, and expert validation.

Dataset construction must be approached as experimental design. Protocols must be documented and validated. Evaluation frameworks must test whether the dataset truly reflects the intended applications.

The field also requires standards and benchmarks that reflect real-world complexity, not simplified proxies. In healthcare, for instance, evaluating a system intended for clinical assistance with generic question-and-answer tests is insufficient. Real-world clinical environments involve multimodal inputs and contextual judgment. Benchmarks must reflect that reality if they are to function as meaningful gates before deployment.

Quality measurement is another crucial frontier. Finance relies on standardized metrics such as credit scores to assess risk. AI lacks an equivalent for datasets and benchmarks. Developing clear methodology to quantify dataset quality and evaluation reliability brings clarity to model assessment.

The criteria for evaluating a multilingual audio library will differ from those of a multimodal oncology dataset. Yet the underlying principle remains constant. Better models require better-defined, better-measured data.

The risks of getting it wrong

As AI systems move closer to high-stakes deployment, weak data practices carry tangible risks.

Benchmarks cannot be created with the same data that is used for training—that’s giving the test answers to the model ahead of time. Scaling data volume without prioritizing data quality and selection diminishes model performance gains, and can even bias against or omit underrepresented populations. These are methodological challenges, and ones that must be solved.

The rigor required at the data layer may not attract headlines. It does not typically lend itself to dramatic product launches. Yet the data layer for AI is foundational to trust, safety, and sustained progress for all AI progress.

An ecosystem for the data era

No single organization can resolve the data gap alone. What is needed is an ecosystem of AI data labs and research groups, each focused on different domains and challenges but united by a commitment to scientific discipline. These institutions would collaborate with model researchers and domain experts who would tackle challenges such as dataset contamination, factuality, groundedness, de-identification, international representation, and bias. They would design benchmarks that mirror real-world complexity rather than simplified abstractions.

AI’s trajectory will not be determined solely by larger models or faster chips. It will be shaped by the datasets we construct, the standards we adopt, and the rigor we apply at the foundation. The uneven frontier we see today reflects an uneven data landscape. Bridging the gap requires deliberate, research-driven dataset design.

If we want AI systems capable of operating reliably in clinical contexts, navigating enterprise workflows, and functioning responsibly across languages and cultures, we must treat data for AI as a first-class scientific endeavor.

AI models have their research labs. AI chip-builders have their fabrication plants. AI data needs institutions of equal seriousness and ambition.

—

New Tech Forum provides a venue for technology leaders—including vendors and other outside contributors—to explore and discuss emerging enterprise technology in unprecedented depth and breadth. The selection is subjective, based on our pick of the technologies we believe to be important and of greatest interest to InfoWorld readers. InfoWorld does not accept marketing collateral for publication and reserves the right to edit all contributed content. Send all inquiries to doug_dineley@foundryco.com.

Cloud computing has reached a crossroads. The high cost and data sensitivity of AI workloads are raising the appeal of private clouds, even as neoclouds and sovereign clouds shake up the cloud provider landscape. New cyberthreats, shifting compute requirements, and management complexity are adding to cloud complications.

Download the June 2026 issue of the Enterprise Spotlight from the editors of CIO, Computerworld, CSO, InfoWorld, and Network World, and learn how to navigate the latest cloud strategy developments.

When I was a kid, we all marveled at the notion of the Star Trek computer that you’d just talk to and that would answer all your questions. It seemed impossibly fantastic and categorically impossible. Well, we have that now. In one episode, Captain Kirk used a universal translator to communicate with an alien (never mind that the aliens always seemed to speak English..). Today, everyone’s phone can do real-time translations between all the most commonly spoken languages on the planet. 

If you haven’t figured it out by now, I’m bullish on agentic coding. I think it’s marvelous, I think it will bring about an increase in software development jobs, and I think it will bring about unprecedented growth in the production of software. It’s that last point I want to explore a bit more deeply, because I think an explosion of software is coming.

Thomas Watson, the legendary IBM CEO, is often misquoted as saying, “I think there is a world market for maybe five computers.”  The true story is that he went out to talk to 20 customers, hoping to sell five IBM 701 computers, and ended up with 18 orders. Of course, today, computer sales are measured in the hundreds of millions. 

We are currently in the “loving the thought of getting 18 orders after expecting five” stage of software. We are just getting started.

Mr. Watson probably couldn’t envision everyone walking around with a computer in their pocket that is a 100 million times more powerful than that IBM 701. And though I’m going to try, I’ll certainly come up way short of imagining a volume of software output that is eight orders of magnitude beyond what we are producing today. 

Ready for takeoff

The first chunk of this blitz will be software that looks familiar and isn’t that surprising. It will be the software sitting in our backlogs — things we’ve wanted to do for years but haven’t had the time for. All software development houses have these projects, and now they will get done. This software will be an expansion of the software we currently have. 

The second chunk will come when companies without development teams realize they can build the software they need using consultancies that can deliver quickly and at minimal cost. They will develop software tools that fit their unique business needs. Instead of using off-the-shelf solutions that they adapt to their needs, they will build custom software that works exactly the way their companies do.

The third chunk will come when those companies figure out that they can build the software they need themselves. Agentic coding tools will soon be powerful enough for non-developers to create the custom software a company needs, and they’ll be able to do these things in-house. 

The fourth wave is the one that really excites me — the wave of things we haven’t yet conceived of. I’d love to be able to tell you what that will be all about, but of course I can’t. It would be like someone telling you in 1988 about Google Maps, Wikipedia, ChatGPT, and Spotify.

The cost of developing software is about to drop through the floor, causing the demand for software to shoot through the roof. Software has no real physical limitations, like bridges do — the only limitation is our imaginations, and that has proven to be boundless.  

Asana has launched an AI personal assistant that can track various data sources to alerts users when a work project runs into problems and recommends next actions.

It’s one of a range of product announcements made Thursday at the company’s Work Innovation Summit in London, including updates to its existing AI teammates product. These follow Asana’s recent acquisition of AI workflow automation software vendor StackAI for $75 million.

Asana Dash is described as an “AI chief of staff” that can help users stay up to date on work projects by accessing information in Asana as well as across email, calendar and team messaging apps, said Arnab Bose, Asana’s chief product officer. “Keeping people in their ‘zone of genius’ and hooking up all of these unstructured signals to the structure of Asana — that’s what Dash does best,” said Bose.

The AI assistant can access the same Asana project information as the user, and can flag when problems occur that could push a project off-track. Asana Dash can then act to address problems, such as posting messages within Asana on behalf of the user or directing an AI teammate to take action. (Asana Dash will ask the user before making any changes.)

“Asana is building on recent acquisitions, and earlier investment in a graph database focused on human connections — the Asana Work Graph — and its position within a well-integrated flow of work to deliver to each worker an executive assistant rooted in the context of their job,” said Wayne Kurtzman, IDC research vice president.    

The Asana Dash personal assistant is enabled by an expanded Asana work graph — the data model related to work carried out by teams in the application. Asana has in the past been more focused on tasks, projects, portfolios, and goals, said Bose, but the work graph now includes new sources of data, linking to employee calendars and accessing meeting transcripts, for instance, alongside other documents and databases.

There are also updates to the AI teammates feature — collaborative AI agents that multiple human coworkers can interact with — which are now more powerful, said Bose. This includes additional skills and integrations with third-party apps such as Gmail, Slack, Outlook, Figma, and Canva.

As for the StackAI acquisition, Bose said it allows Asana to extend the reach of AI agents into a variety of business apps more easily and reliably, building ] on Asana’s “system of action” function. The latter tracks work carried out across an organization, he said, and can automate the complex processes that make up many enterprise workflows. 

“If you look at StackAI’s website, the thing that they are really, really great at is building these complex, multi-step processes,” said Bose. The aim is to combine StackAI’s agent builder with integration expertise agents already available in Asana. 

“So, the idea is when an AI teammate or Dash recommends the next best action, they will be able to choose downstream actions based on the portfolio of approved workflows that you’ve built out in StackAI.”

Overall, the announcements help Asana provide a platform that combines agents and workflow automation with AI assistance that aids humans to work more effectively, said Bose.

“Our terminology for this is a ‘human-agent operating system,’ because automation, I feel, is a little reductive in the sense that there are some things that are fully automated, but a lot that you’d want a human being and an AI agent to coordinate on and align on,” he said.

Asana did not immediately respond to a request for pricing and availability details for Asana Dash.