Amazon DeepRacer
AWS DeepRacer is an autonomous 1/18th scale race car designed to test reinforcement learning (RL) models by racing on a physical track. It provides a fully autonomous driving platform that enables developers to get hands-on experience with machine learning through a fun and engaging racing experience.
Solid contracts, transparent operations, and an easy start — typically complete on four or five facets with one clear soft spot.
API Evangelist profiles Amazon DeepRacer the way a machine reads it — 49 machine-readable artifacts across 4 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 — Amazon DeepRacer scores 66.7/100 (strong), with a separate agent-readiness read of 48/100 (agent ready). 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 Amazon DeepRacer
Each block below is one kind of artifact we hold for Amazon DeepRacer. 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 4
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.
Amazon DeepRacer Cars API
Manage DeepRacer physical vehicles and their configurations
Amazon DeepRacer Leaderboards API
Manage racing leaderboards and submissions
Amazon DeepRacer Models API
Manage reinforcement learning models for autonomous racing
Amazon DeepRacer Tracks API
Manage virtual and physical racing tracks
Open Collections 1
Open, tool-agnostic collections carry the same runnable value as Postman without locking you to one client — the portable, forkable form of the same exercise.
Open, tool-agnostic API collections (OpenAPI-derived and Bruno).
Amazon DeepRacer API
OPEN COLLECTIONPricing Plans 1
Pricing is part of the interface. Machine-readable plans tell you what a tier costs and includes before you commit — one of the six things the Kin Score reads for commercial clarity.
Published pricing tiers and plan structures.
Rate Limits 1
Rate limits are the difference between a demo that works and a production integration that doesn't fall over. Publishing them is an operational-transparency signal — and a hard requirement for any agent that plans its own throughput.
Documented rate limits and quota policies.
Amazon Deepracer Rate Limits
RATE LIMITSFinOps 1
Cost, billing, and metering signals let a buyer model the financial operations of an API before it's live. We profile them for the same reason we profile pricing: the money is part of the contract.
Cost, billing, and metering signals for API financial operations.
Amazon Deepracer Finops
FINOPSSemantic Vocabularies 1
JSON-LD contexts give the data shared meaning across APIs. We profile them because semantics are what let a machine reconcile 'customer' here with 'customer' somewhere else.
JSON-LD contexts and semantic vocabularies used across these APIs.
Amazon Deepracer Context
JSON-LDSpectral Rules 2
Governance rulesets we run against this provider's specs — the automated checks behind parts of the score. Profiling them makes the quality bar explicit and re-runnable, not a matter of opinion.
Amazon DeepRacer API Rules
SPECTRALAmazon DeepRacer API Rules
SPECTRALJSON Schema 11
Standalone JSON Schema definitions describe the data models behind the API. We profile them so the shapes are validatable on their own — useful long after a single request is forgotten.
Standalone JSON Schema definitions for this provider's data models.
Car
JSON SCHEMAError
JSON SCHEMALeaderboard
JSON SCHEMALeaderboardSubmission
JSON SCHEMAListCarsResponse
JSON SCHEMAListLeaderboardSubmissionsResponse
JSON SCHEMAListLeaderboardsResponse
JSON SCHEMAListModelsResponse
JSON SCHEMAListTracksResponse
JSON SCHEMAModel
JSON SCHEMATrack
JSON SCHEMAScroll within the panel for all 11 ·
JSON Structure 11
JSON Structure captures the data shapes in a form built for tooling — a complement to JSON Schema that keeps the model machine-legible.
JSON Structure definitions describing this provider's data shapes.
Car Structure
JSON STRUCTUREError Structure
JSON STRUCTURELeaderboard Structure
JSON STRUCTURELeaderboard Submission Structure
JSON STRUCTUREList Cars Response Structure
JSON STRUCTUREList Leaderboard Submissions Response Structure
JSON STRUCTUREList Leaderboards Response Structure
JSON STRUCTUREList Models Response Structure
JSON STRUCTUREList Tracks Response Structure
JSON STRUCTUREModel Structure
JSON STRUCTURETrack Structure
JSON STRUCTUREScroll within the panel for all 11 ·
Examples 11
Real request and response payloads are what turn a spec from abstract into obvious — and they're one of the twelve things an agent needs to call an API correctly on the first try.
Example request and response payloads for these APIs.
Car Example
EXAMPLEError Example
EXAMPLELeaderboard Example
EXAMPLEList Cars Response Example
EXAMPLEList Models Response Example
EXAMPLEList Tracks Response Example
EXAMPLEModel Example
EXAMPLETrack Example
EXAMPLEScroll within the panel for all 11 ·
Security Posture 4
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 Amazon DeepRacer — 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 4
Portal, sign-up, and the first successful call
Documentation 1
Reference material describing how the API behaves
Agent Surfaces 1
MCP servers, agent skills, and machine-readable catalogs
Build 1
SDKs, sample code, and the tooling you integrate with
Access & Security 4
Authentication, authorization, and security posture
Operate 3
Status, limits, changes, and where to get help
Commercial 2
Pricing, plans, and the legal terms of use
Company 2
The organization behind the API
← All providers · Data indexed from github.com/api-evangelist/amazon-deepracer · machine-readable index on apis.io