Why does this page say Polygon / Massive?
The Polygon URL currently resolves to Massive-branded pages. This page keeps the familiar Polygon search phrase while using Massive as the visible current branding.
Market data comparison
Cornerstones vs Polygon / Massive for AI agents
Compare Polygon/Massive and Cornerstones for AI agent market workflows: direct financial data APIs, WebSockets, flat files, and libraries versus provider-safe market context for agents.
Who each is for
| Product | Good fit |
|---|---|
| Polygon / Massive | Builders that want direct financial data infrastructure, documented APIs, WebSockets, flat files, client libraries, and low-level integration choices in their own stack. |
| Cornerstones | Teams that need AI agents to consume market context through a product boundary with plan-aware access, safe docs, and response semantics. |
Raw API vs agent-native context
| Topic | Polygon / Massive | Cornerstones |
|---|---|---|
| Primary surface | Direct REST, WebSocket, flat-file, library, and related data-access surfaces for applications that own their market-data integration. | Agent-facing market context with account boundaries, source and freshness hints, quality notes, and docs designed for autonomous readers. |
| Workflow responsibility | The application team decides which endpoints to call, how to store keys, and how to translate raw market data into agent instructions. | The product layer packages market truth and capability boundaries so agents can reason from stable public docs and contracts. |
Coverage
- Massive publicly presents financial data infrastructure across stocks, options, indices, currencies, futures, APIs, WebSockets, flat files, and libraries.
- Cornerstones presents coverage as agent workflow surfaces: access matrix, context packets, response contracts, charts, options intelligence, event evidence, macro inputs, and orderflow or liquidity tiers where enabled.
- Cornerstones comparison pages avoid claiming wider or narrower raw exchange coverage than direct data platforms unless that claim is explicitly supported elsewhere.
Safety, auth, and provider leakage
- Direct data-platform integration can be appropriate when credentials and endpoint choices stay inside controlled application code.
- Cornerstones is designed so public clients and prompts can discuss product capabilities without exposing private provider credentials or backend adapter topology.
- Cornerstones separates product keys, plan boundaries, and read-only context from execution systems and private data infrastructure.
AI agent workflow fit
- Choose direct APIs, WebSockets, or flat files when a human-owned application controls schema mapping and data governance.
- Choose Cornerstones when agents need a safer market-context layer that explains what they can read and how to interpret it.
- Cornerstones is meant to reduce prompt-level leakage and make plan, freshness, and quality state legible to autonomous workflows.
When to choose Cornerstones
- You want agent-readable docs and response contracts rather than raw endpoint instructions embedded in prompts.
- You need market context packaged for coding agents, trading-research agents, or workflow orchestrators with clear account limits.
- You want customer-facing docs to stay product-level while private provider and adapter details remain server-side.
Source note
The Polygon URL currently presents Massive branding at massive.com. Massive publicly describes JSON/CSV data, client libraries, REST and WebSocket APIs, flat files, SQL access, and products for stocks, options, indices, currencies, and futures.
Cornerstones product boundaries: access matrix, context packets, response contract.
FAQ
Is Cornerstones a raw market-data infrastructure provider like Massive?
No. Cornerstones is positioned as an agent-native market context product layer. It does not try to describe itself as a direct substitute for every raw API, WebSocket, or flat-file workflow.
When is a direct data API the right choice?
Use direct APIs when your application team owns endpoint selection, credentials, storage, schema mapping, and downstream interpretation.
When is Cornerstones the right choice?
Use Cornerstones when AI agents need product-level market context, plan-aware access, response contracts, and provider-safe docs.