Market Data API vs Research Layer: Distilla vs Financial Datasets, FMP and Daloopa

The short answer

A financial data API for AI agents usually does one or two of four jobs. Financial Datasets and FMP supply prices and financial statements across a wide universe of tickers. Daloopa supplies audit-grade, source-linked fundamentals and KPIs for financial models, sold to funds. Distilla supplies financials, consensus and valuation plus the narrative around a company and a layer of company knowledge: drivers, events, peers and the reasons behind price moves, for a narrower set of about 1,200 companies. They overlap, but they are not substitutes. Many agents end up using two of them, and if all your agent needs is prices and statements, one of the broad APIs (or free sources) is enough.

Why an agent needs a data layer at all

An agent that researches stocks from web search is working from content that can be biased, outdated, incomplete or wrong. Search results are ranked by criteria other than news relevance, search is not built around timestamps, and many articles are vague about when they were written, so an agent asked about a particular day can attach the wrong event to it. Because sources conflict, the same question can also get a different answer on a different run. Investors who rely on agentic analysis for money decisions need a governed data source behind it.

Institutional investors have always paid for that kind of data. What is changing is access: the same kinds of data are now packaged for individual investors and small teams, and the self-serve tier of financial MCP servers is where most of them start. There are two tiers of data MCP servers: enterprise tools sold to funds through sales teams (Capital IQ, FactSet, AlphaSense and specialists such as Daloopa), and self-serve tools (Financial Datasets, FMP, Finnhub, Massive, Alpha Vantage, Distilla and others). This comparison sits mostly in the self-serve tier, with Daloopa as the enterprise-priced specialist.

Four layers of financial data for AI agents

Layer 1: Market data (prices, volumes). Largely commoditized, and partly free. Yahoo Finance data is available through community-built yfinance MCP servers, and FRED covers macro series for free. Paid vendors add reliability and breadth. FMP offers a broad REST API covering more than 70,000 securities, from intraday prices to statements, estimates and transcripts. Financial Datasets describes itself as “the stock market API built for AI agents” and covers more than 27,000 US tickers. Ask either for Adobe’s closing price on a given day and you get a clean number.

Layer 2: Financials and other numbers. Statements and segment data have traditionally been compiled by people, which is why enterprise terminals cost what they do. AI-native extraction now pulls the same numbers, and more, directly from filings. Financial Datasets and Distilla both use AI to extract segment numbers from filings, overlapping with Daloopa. Daloopa focuses on the detail analysts put into models: thousands of KPIs, segments and company guidance, with every number hyperlinked to its original source, across 6,000+ public companies with 14 years of history. Distilla covers financial statements, consensus estimates and valuation history, and extracts further, including industry-specific metrics based on industry templates (examples: ARR, customer concentration, RevPAR).

Layer 3: Qualitative and narrative data. This layer is newer and is core to an agent’s ability to reason about fundamentals and sentiment. FMP and Finnhub offer transcripts and filings. Distilla indexes filings, earnings-call transcripts, summaries of sell-side views and investor podcasts, and company events: the widest mix of narrative source types we found in a self-serve product.

Layer 4: Company knowledge. A compression of facts into a standard understanding of each company with little need for opinion: what drives the business, products and peers, events, and price attribution, the link between price moves and the events and sentiment behind them over time. Distilla is the only one of these vendors whose documentation describes such a layer. As of 7 October 2026, its database holds 438K+ explained price moves, about 200K summaries of sell-side views and podcast episodes, and 7.3M+ qualitative knowledge cells (segments, guidance, KPIs and topic changes extracted from filings and calls), with about 50,000 company events added weekly. Company-level coverage is about 1,200 companies today, expanding across the US and Asia.

The layers overlap at the edges. FMP and Financial Datasets both carry news, and Distilla also returns daily prices. But each vendor is built around a different question.

Same question, different answers: “Why did Adobe fall after earnings?”

Adobe reported fiscal Q3 2026 results after the close on 10 September. It is a useful test because the honest answer is more complicated than the question.

Here is what each provider’s documented data can contribute.

We could not find a documented feature in the other three that explains why a stock moved. They give an agent the ingredients and leave it to do the reasoning, which is where errors creep in. That is a design choice, not a flaw: for modeling or quoting numbers, that is exactly what you want.

Here is the Distilla answer, from Claude with Distilla connected.

Claude with the Distilla connector, asked why Adobe fell after its latest earnings report.

The results themselves were good: revenue of $6.76B against a consensus of about $6.70B, and a raised full-year guide. The stock still dropped about 3.5% after hours, which the answer traces to a Q4 revenue guide the market read as light, a CEO transition due in December and worries about AI-native competitors.

Then comes the part a price feed would get wrong. Adobe’s shares actually closed up 1.37% on 11 September, the first full session after the report, according to Distilla’s price data. Most of the decline that followed came from other news: a 4.5% drop on 22 September on leadership changes and broker target cuts. An agent working only from end-of-day prices would see a stock that rose after earnings and fell two weeks later, and would have to guess at the connection. An agent with explained price moves gets the sequence, with dates.

For comparison, we asked the same question with Distilla switched off, so Claude could only search the web. Its first search turned up older earnings reports, and it had to search again to find the September one. It then got the headline right: a beat, with soft Q4 guidance. But it missed what happened next. It described the stock as slipping and then stabilizing, without noting that Adobe closed 1.37% higher on 11 September, jumped 5.3% on 14 September on the Topaz Labs deal, and fell 4.5% on 22 September on leadership changes. Without a dated explanation for each move, the agent saw one day and filled in the rest.

The same question with web search only. Claude found the September report on its second try, but missed the moves that followed.

What the explained price moves make possible

The Adobe example is a single stock on a single date. The larger difference shows up when an agent works across many moves at once. With explained price moves indexed and dated, you can ask an agent for the reasons behind every big move in a sector over the last seven days, or for every 3%-plus move in one company over 52 weeks, and look for patterns. That kind of bottom-up screening is hard for an agent that only has web search, which reads a few top-ranked pages, and it is not something a raw price feed supports either, because prices carry no reasons. It is useful for finding signals, studying how a stock reacts to different kinds of news and developing strategies to test.

Side-by-side comparison

A dash (—) means we could not confirm the capability on the vendor’s own pages. It does not necessarily mean the vendor lacks it.

Which to connect, and in what order

Start from the question your agent has to answer most often.

If it mostly needs numbers, such as prices, statements and ratios across thousands of tickers, start with a market data API, or with the free sources. FMP has the widest catalog and a free tier to test with. Financial Datasets is built specifically for agents and is a good fit if you only need US stocks. If your names fall outside Distilla’s coverage, one of these is the better choice.

If it builds or checks models, add Daloopa if your firm can license it. Source-linked KPIs and guidance are what you want when a figure has to match the filing exactly. Distilla’s AI-extracted segments and industry KPIs cover much of the same ground at self-serve prices, with the caveat below.

If it has to explain markets or monitor a watchlist, add Distilla. Why did a stock move, what changed on the last call, what are the sell-side and podcast views, what drives the business, what happened across my 25 holdings this week: these are the questions a pure data API leaves to the model.

A common stack for an agent that answers investor questions is one market data API plus Distilla. Add Daloopa if the agent also produces audit-grade models. Whatever you connect, tell the agent which tool owns which job, for example: “Use FMP for prices and statements, Distilla for why a stock moved.” Agents route better with explicit instructions.

Honest limits

Distilla. Company-level coverage is about 1,200 companies today, expanding across the US and Asia, against roughly 2,000 to 3,000 liquid stocks in those markets and tens of thousands of tickers at Financial Datasets and FMP. Check that your names are covered. Its prices are daily, not intraday. Its sell-side and podcast content is summaries of views, not the original reports or recordings, and its explanations are written by a language model from linked events, so key figures should be checked against the filing. Its AI-extracted segments and KPIs are not a substitute for the hyperlinked, audit-grade KPI models Daloopa builds for funds.

Financial Datasets. By its own documentation, non-US markets are not yet available, and we could not find earnings call transcripts among its endpoints.

FMP. We could not find a guidance endpoint in its documentation, and transcripts sit on its highest plan.

Daloopa. Pricing is through sales, and its document and price tools are focused on US companies.

All three other providers. We could not find a documented way to explain a price move. If your agent needs that, it has to build the explanation itself from news, filings and prices.

FAQ

What is the best financial data API for AI agents?

It depends on the layer. For prices and statements across a wide universe, Financial Datasets and FMP. For source-linked fundamentals at a fund, Daloopa. For financials plus narrative research and company knowledge, including explained price moves, for about 1,200 covered companies, Distilla. Many agents use two of them together.

Is Distilla a market data API?

Not primarily. Distilla is a research layer. It does return daily prices, statements, consensus estimates, valuation multiples and AI-extracted segments for the companies it covers, but its focus is on narrative research, company knowledge and explaining why stocks moved.

Can Financial Datasets or FMP explain why a stock moved?

We could not find a documented feature for it in either. Both provide news, filings and prices that an agent can use to work out an explanation on its own.

Does Distilla replace Daloopa?

For many analysts, Distilla’s AI-extracted segments and industry-specific KPIs cover what they need at a self-serve price. Daloopa’s strength remains audit-grade, source-linked KPIs and guidance for financial models at funds. If a figure must tie out to the filing line by line, Daloopa is the specialist.

Do these APIs work with Claude and ChatGPT?

All four offer MCP servers. Distilla’s is read-only at https://api.distilla.ai/mcp and works with Claude, ChatGPT, Claude Code, Cursor and WorkBuddy, with a free plan and paid plans from $9.90/month.

Is this investment advice?

No. Distilla is a research tool, not investment advice. The Adobe example illustrates how the tools differ and is not a recommendation.

About Distilla

Distilla is the fundamental research layer for AI agents: why stocks move, not just where. It brings institutional-grade research data to individual investors and small teams through an MCP server and HTTP API that gives Claude, ChatGPT and your own agents quantitative data, qualitative research and structured company knowledge in one queryable, read-only layer. The same data powers an enterprise product used by institutional investors.

•           Quantitative: financial statements, analyst consensus, valuation history and company metrics, including AI-extracted segments and industry-specific KPIs.

•           Qualitative: earnings-call and filing commentary, about 200K summaries of sell-side views and investor podcast episodes.

•           Company intelligence: 1,000+ company knowledge topics, business drivers updated quarterly, company events (about 50,000 added weekly), products, peers and sector relationships, and 438K+ explained price moves of 3% or more.

Company-level coverage is about 1,200 companies today, expanding across the US, Japan, Hong Kong, Korea and China A-shares; sell-side and podcast summaries extend beyond that core universe. Read-only MCP server at https://api.distilla.ai/mcp, with the same lookups over an HTTP API. Works with Claude, ChatGPT, Claude Code, Cursor, WorkBuddy and Doubao. Free plan, with paid plans from $9.90/month. Distilla is a research tool, not investment advice. Figures are as of 7 October 2026.