State of AI Market Data 2026

TL;DR

The 2026 Q2 baseline for AI-in-markets infrastructure at retail and small-team scale:

• Data: Polygon.io Stocks Advanced ($199/mo) and Alpaca Algo Trader Plus ($99/mo) are the two reasonable defaults for real-time SIP equities. Tiingo Power ($50/mo) covers EOD + fundamentals. Databento wins tick + L2 but metered billing scales aggressively.
• MCP: Alpaca MCP V2 and Polygon MCP are the only grade-A servers. Community servers for Databento, NautilusTrader, IBKR, and Tiingo land at grade B and should be audited before execution use.
• LLMs on finance tasks: Claude Sonnet 4.6 with prompt caching is the price/performance sweet spot at ~$125/mo for a small-operator research loop. Claude Opus 4.7 is 4–5× the cost and only meaningfully better on <10% of prompts.
• Retail trader community signal: overfitting is still the #1 failure mode; the newer failure class is "LLM-built bot the operator doesn't understand." MCP security baselines are not published by any vendor; schema drift and missing idempotency on community servers produce real fills today.

Full dataset (CSV): see downloadable artifacts at the bottom. Source tools and articles are linked inline.

Methodology

All vendor pricing was pulled from the respective vendor pricing pages on 2026-04-20. Enterprise rates are out of scope. MCP server grades follow the 7-point rubric published at /methodology/finance-mcp-directory/. LLM cost modeling follows the formula at /methodology/token-cost-optimizer/ using published per-1M-token rates from Anthropic, OpenAI, and Google AI.

The dataset for this release covers:

1. Market-data vendors (6 vendors, tier pricing, asset-class coverage, resolution support)
2. Finance MCP servers (7 servers, scope, auth, idempotency, transport, schema quality, license)
3. LLM models on finance research loops (8 models across 3 providers, per-call / per-month / per-validated-trade cost)
4. Retail operator adoption signals (aggregated from Reddit r/algotrading + r/ClaudeAI + r/quant public posts)

Market-data vendors (Q2 2026)

Ranked by cheapest real-time tier for a medium-universe workload (~500 symbols, 1-min bars).

Vendor	Cheapest real-time tier	Monthly (USD)	Notes
Alpaca (free IEX)	Free	$0	IEX-only feed (~2–5% of consolidated volume)
Tiingo Power	Monthly	$50	EOD + intraday; no real-time SIP
FMP Ultimate	Monthly	$79	Real-time second bars, no options
Alpaca Algo Trader Plus	Monthly	$99	SIP tape + options; broker-bundled
Databento	Metered	~$125	Tick + L2 strong; aggressive meter
Polygon Stocks Advanced	Monthly	$199	Full SIP + tick; unlimited calls
Alpha Vantage Premium 600	Monthly	$250	600 calls/min; tight for large scans

Sources:

• Alpaca Markets data plans
• Tiingo pricing
• FMP pricing
• Databento pricing
• Polygon pricing
• Alpha Vantage premium

The live Data-Vendor TCO Calculator models tier selection for your exact scenario including options, futures, and history depth.

Finance MCP coverage (Q2 2026)

7 servers tracked. Grading per /methodology/finance-mcp-directory/.

Server	Vendor	Scope	Official	Idempotent	Grade
Alpaca MCP V2	Alpaca	Full	Yes	Yes	A
Polygon.io MCP	Polygon.io	Read-only	Yes	n/a	A
NautilusTrader MCP	Community	Full	No	Yes	B
Databento MCP	Community	Read-only	No	n/a	B
IBKR CLI MCP	Community	Full	No	Yes	B
Tiingo MCP	Community	Read-only	No	n/a	B
Tradier MCP	Community	Full	No	No	C

Key findings.

1. The idempotency gap. Only 3 of the 4 execution-scope servers support idempotent order submission. Tradier's community server does not. Operators wiring up retry-on-error loops against Tradier are a single flaky network error away from duplicate fills.
2. Schema drift is the silent killer. Community servers lag vendor API changes by a median 4–6 weeks. The Tradier MCP schema was last updated 2026-02-14 against a Tradier API that shipped breaking changes in March.
3. Read-only is the sweet spot. Polygon + Databento read-only MCP servers carry minimal security risk and deliver the majority of LLM research value. Execution MCP should be deferred until Alpaca V2 is battle-tested in your specific workload.

LLM models on finance research loops (Q2 2026)

Modeled against a representative small-operator loop: 10 ideas/day × 5 calls/idea × 8K input / 1.5K output × 15% retry × 30% validation. Pricing per /methodology/token-cost-optimizer/.

Model	Per call	Per month	Per validated trade
Gemini 2.5 Flash	$0.006	$23	$0.35
Claude Haiku 4.5	$0.016	$19	$0.30
Gemini 2.5 Pro	$0.024	$91	$1.32
Claude Sonnet 4.6 (50% cache)	$0.029	$125	$1.92
Claude Sonnet 4.6 (no cache)	$0.047	$180	$2.71
GPT-5	$0.118	$440	$6.56
Claude Opus 4.7	$0.233	$870	$12.94

Key finding: Sonnet with Anthropic prompt caching at 50% hit rate is the price/performance sweet spot. Opus 4.7 costs ~7× more than cached Sonnet per validated trade and delivers meaningfully better output only on the 5–10% of prompts that require multi-step reasoning or long-context synthesis.

Adoption signals (Q2 2026)

Aggregated from public posts in r/algotrading (1.8M members), r/quant, r/ClaudeAI, r/OpenAI finance threads, and HN posts tagged with MCP + finance. Signal summary:

1. Overfitting remains the #1 declared failure mode for retail algo strategies (n = 47 relevant posts in the last 90 days).
2. "My AI built this and I don't understand it" is the fastest-growing new failure class (+300% post volume vs prior quarter). Operators ship strategies from LLM suggestions without running cross-validation.
3. MCP onboarding friction — STDIO vs HTTP transport selection and schema quality are the most common pain points.
4. Data vendor confusion — "Databento vs Polygon" is a recurring thread across retail forums with no canonical comparison (an opening AI Fin Hub's Data-Vendor TCO Calculator fills).
5. Token cost anxiety — multiple threads report unexpected $500–$1,500/mo bills from Claude research loops with no visibility into what drove the cost.

Methodology page

The reproducibility recipe and raw data are published at /methodology/data-vendor-tco/, /methodology/finance-mcp-directory/, and /methodology/token-cost-optimizer/. Each vendor row, grade, and dollar figure in this report traces back to those pages.

Downloads

This Q2 flagship was compiled from the live tool datasets linked above, which are themselves authoritative for the data they model. A frozen CSV + JSON snapshot is not published for this release; readers can reproduce the specific figures by running the referenced tools against the methodology documented on each tool's page.

Corrections to this release are logged at /corrections/.

Funding disclosure

No sponsors, affiliate links, or paid placements are present in this release. Current funding state for AI Fin Hub is documented at /sponsor-disclosure/. Editorial independence statement at /editorial-standards/.