Adaptive ML
Adaptive ML builds Adaptive Engine, an enterprise platform for developing, evaluating, and serving specialized open-source large language models via reinforcement-learning post-training. The three-phase workflow (Adapt, Evaluate, Serve) lets teams fine-tune smaller models to outperform commercial APIs, measure them with custom AI judges and graders, and feed production signals back into training. Adaptive Engine is self-hosted (Kubernetes/Helm) and exposes an OpenAI-compatible REST API for chat completions and embeddings, plus interactions, comparisons, outcomes, dataset/recipe management, and chunked uploads. Tooling includes the Python adaptive-sdk and adaptive-harmony libraries and the adpt CLI. Adaptive ML was acquired by Datadog.
Real signal across most facets with visible, nameable gaps — the contract exists but is thin, or the portal is good while governance and commercial terms are absent.
API Evangelist profiles Adaptive ML the way a machine reads it — 14 machine-readable artifacts across 9 APIs, pulled from the provider's own public surface and indexed so a developer, an analyst, or an AI agent can evaluate it against every other provider on the network.
Every provider in the network is reduced to the same set of machine-readable artifacts — OpenAPI contracts, event specifications, GraphQL schemas, runnable collections, pricing and rate-limit signals, security posture, OAuth scopes, and the agent surfaces (MCP servers and skills) that let software drive the API on its own. We profile them because the interface is the part of a company you can actually inspect: it is a truer signal of what a provider does than any marketing page. From those artifacts we compute the Kin Score — Adaptive ML scores 48.5/100 (developing), with a separate agent-readiness read of 75/100 (agent native). The full breakdown is below, followed by every artifact we hold — each card links through to its machine-readable definition on apis.io.
Kin Score
This is the API Evangelist rating — a single, repeatable read computed from the artifacts on this page. Green fill is points earned; the red track is points possible, so every bar shows earned-versus-possible at a glance.
Kin Score
How this is scored →scored 2026-07-27 · rubric v0.5
How we profile Adaptive ML
Each block below is one kind of artifact we hold for Adaptive ML. For each we say what it is and why it earns a place in the profile, then list every one we've indexed — capped at two rows, scroll within the panel for the rest.
APIs 9
Each API is captured as its own OpenAPI definition — every operation, parameter, and response. This is the single most useful machine-readable description of what an API does, and it's what lets us score, lint, mock, and generate against it without asking the provider for anything.
Individual APIs this provider publishes, each with its own machine-readable definition.
Adaptive ML artifacts::rest API
The artifacts::rest API from Adaptive ML — 1 operation(s) for artifacts::rest.
Adaptive ML Chunked Upload API
Upload large files in chunks
Adaptive ML Completions API
The Completions API from Adaptive ML — 1 operation(s) for completions.
Adaptive ML Datasets API
The Datasets API from Adaptive ML — 1 operation(s) for datasets.
Adaptive ML Embeddings API
The Embeddings API from Adaptive ML — 1 operation(s) for embeddings.
Adaptive ML Feedback API
The Feedback API from Adaptive ML — 2 operation(s) for feedback.
Adaptive ML image::rest API
The image::rest API from Adaptive ML — 1 operation(s) for image::rest.
Adaptive ML Interactions API
Load interactions in the db
Adaptive ML Recipes API
Recipe operations
Scroll within the panel for all 9 ·
MCP Servers 1
Model Context Protocol servers expose these APIs directly to AI agents. We profile them because agent-native access is the fastest-growing way this provider's capabilities actually get used.
Model Context Protocol servers that expose these APIs to AI agents.
adaptive-ml-mcp.yml
MCP SERVEREvent Specifications 1
Not every API is request/response. AsyncAPI describes the event-driven and streaming side — the webhooks and channels — so the asynchronous half of the interface is documented the same way the synchronous half is.
AsyncAPI definitions for this provider's event-driven and streaming APIs.
Adaptive Ml Webhooks
ASYNCAPISecurity Posture 2
Authentication, domain security, vulnerability disclosure, and trust-center signals — the evidence that a provider takes security seriously enough to document it. We profile it because you can't govern what you can't see.
Authentication, domain security, vulnerability disclosure, and trust-center signals.
Agentic Access 1
An x-agentic-access contract marks which operations are safe for an agent to run on its own and which need a human in the loop. It is the difference between an API an agent can use and one it can use safely.
Recommended x-agentic-access execution contracts for AI agents.
Resources
Every other property we hold for Adaptive ML — documentation, portals, status pages, policies, and corporate surface — grouped by the job it does, following the integrator's arc from getting started to running in production.
Get Started 3
Portal, sign-up, and the first successful call
Documentation 2
Reference material describing how the API behaves
Agent Surfaces 5
MCP servers, agent skills, and machine-readable catalogs
Design & Contract 6
Pagination, idempotency, versioning, errors, and events
Build 4
SDKs, sample code, and the tooling you integrate with
Access & Security 2
Authentication, authorization, and security posture
Operate 2
Status, limits, changes, and where to get help
Commercial 1
Pricing, plans, and the legal terms of use
Company 2
The organization behind the API
Other 1
Properties that don't map to a standard resource type
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