One API for AI model intelligence.
Compare provider deployments using normalized pricing, capabilities, lifecycle, constraints, and evidence. Your provider or gateway still executes model traffic.
Why ALLM
A model name is only one layer of identity. A model release describes what a lab built; a deployment describes how a provider exposes it in a region through a specific API surface. Prices, limits, and capabilities attach to the deployment.
API basics
- Base URL
https://api.use-allm.com- Media type
application/jsonfor success;application/problem+jsonfor errors.- Authentication
- Bearer API key. Public catalog reads may be anonymous at lower limits.
- Versioning
- The major API version is part of every path, for example
/v1/deployments.
Data model
model family → model release → deployment → rate card
deployment → lifecycle milestones → evidence
deployment → capability assertion → evidence
catalog release → immutable snapshot → lockfile
deployment → lifecycle milestones → evidence
deployment → capability assertion → evidence
catalog release → immutable snapshot → lockfile
Your first intelligence query
Install the TypeScript SDK, create a free API key, then query deployments by the capabilities your product needs.
TypeScript
import { ALLM } from "@allm/sdk";
const allm = new ALLM({ apiKey: process.env.ALLM_API_KEY });
const deployments = await allm.deployments.list({
model: "anthropic/claude-haiku-4.5",
capability: "tool_calling",
});JSON response
{
"data": [
{
"id": "dep_anthropic_claude_haiku_4_5_messages_global",
"model_id": "anthropic/claude-haiku-4.5",
"provider_id": "anthropic",
"region": "global",
"capabilities": {
"tool_calling": {
"status": "supported",
"verification_status": "official_declared"
}
}
}
],
"meta": { "total": 3, "limit": 100, "offset": 0, "has_more": false }
}Persist decision metadata
When a result affects production routing or cost, store the deployment ID and
x-allm-catalog-release together.Where to go next
- Complete the quickstart with TypeScript, Python, or cURL.
- Build an end-to-end workflow from the production use cases.
- Understand the difference between models and deployments.
- Learn how to interpret deprecation and end-of-life.
- Learn how to interpret capability evidence.
- Browse every endpoint in the API reference.