Hyperbolic
Hyperbolic is an open-access AI cloud and decentralized GPU marketplace serving 200,000+ builders with affordable inference and bare-metal compute. The platform combines a serverless OpenAI-compatible inference API spanning 25+ open-source LLMs (including the only public Llama-3.1-405B-Base in BF16), image and audio models, with an on-demand GPU rental marketplace aggregating idle H100 / H200 / A100 / RTX 4090 capacity from third-party suppliers at 3-10x lower cost than hyperscalers. Reserved clusters, dedicated endpoints, an OpenAI-drop-in Python and TypeScript SDK, a Go CLI, an MCP server, the Hyperbolic AgentKit, the open-source Hyper-dOS distributed operating system, and Coinbase x402 crypto payments round out the stack.
Solid contracts, transparent operations, and an easy start — typically complete on four or five facets with one clear soft spot.
API Evangelist profiles Hyperbolic the way a machine reads it — 60 machine-readable artifacts across 6 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 — Hyperbolic scores 68.6/100 (strong), with a separate agent-readiness read of 55/100 (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.
How we profile Hyperbolic
Each block below is one kind of artifact we hold for Hyperbolic. 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 6
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.
Hyperbolic GPU Marketplace API
Decentralized on-demand GPU compute marketplace renting idle H100, H200, A100, and RTX 4090 capacity from third-party suppliers. Pricing starts at $0.50/GPU/hr (RTX 4090), $1.39...
Hyperbolic Audio Generation API
Text-to-speech audio endpoint
Hyperbolic Chat Completions API
Generate chat-style completions from open-source LLMs
Hyperbolic Completions API
Legacy base-model text completion endpoint
Hyperbolic Image Generation API
Text-to-image diffusion endpoint
Hyperbolic Models API
List available inference models
Postman Collections 5
A runnable collection turns the contract into something a developer can execute in seconds. We profile them because the fastest way to trust an API is to make a real call against it.
Ready-to-run Postman collections for exercising this provider's APIs.
Hyperbolic Completions API
POSTMANHyperbolic Models API
POSTMANOpen Collections 5
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).
Hyperbolic Audio Generation API
OPEN COLLECTIONHyperbolic Chat Completions API
OPEN COLLECTIONHyperbolic Completions API
OPEN COLLECTIONHyperbolic Image Generation API
OPEN COLLECTIONHyperbolic Models API
OPEN COLLECTIONArazzo Workflows 9
Real integrations are rarely a single call. Arazzo describes the multi-step sequences — auth, then create, then confirm — so both a human and an agent can follow the choreography, not just the endpoints.
Multi-step API workflows described with the Arazzo specification.
Hyperbolic Chat To Image
Use an LLM to craft a vivid image prompt, then render it with a diffusion model.
ARAZZOHyperbolic Chat To Speech
Generate an assistant reply with an LLM, then narrate it with text-to-speech.
ARAZZOHyperbolic Discover Model And Chat
List the live model catalog, pick a chat model, and run a chat completion against it.
ARAZZOHyperbolic Discover Model And Complete
List the catalog and run a base-model text completion against a non-instruct model.
ARAZZOHyperbolic Generate And Describe Image
Render an image with diffusion, then describe it with a vision LLM and narrate the caption.
ARAZZOHyperbolic Image Prompt QA Loop
Render an image, judge it with a vision model, and re-render once if it fails QA.
ARAZZOHyperbolic Multimodal Story
List models, write a short story, illustrate it, and narrate it across four endpoints.
ARAZZOHyperbolic Research Summarize And Narrate
Confirm a reasoning model, summarize a topic with it, and narrate the summary.
ARAZZOHyperbolic Tool Calling Roundtrip
Run an OpenAI-compatible tool call and feed the tool result back for a final answer.
ARAZZOScroll within the panel for all 9 ·
Pricing 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.
Hyperbolic Ai 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.
Hyperbolic Ai Finops
FINOPSFeatures 21
The notable capabilities this provider advertises, captured as structured features so they can be searched and compared instead of read one landing page at a time.
Notable capabilities this provider offers.
Scroll within the panel for all 21 ·
Semantic 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.
Hyperbolic Ai 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.
Hyperbolic API Rules
SPECTRALHyperbolic API Rules
SPECTRALJSON Schema 1
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.
Hyperbolic Chat Completion
JSON SCHEMAExamples 4
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.
Security 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 Hyperbolic — 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 5
Portal, sign-up, and the first successful call
Documentation 8
Reference material describing how the API behaves
Scroll within the panel for all 8 ·
Agent Surfaces 1
MCP servers, agent skills, and machine-readable catalogs
Design & Contract 11
Pagination, idempotency, versioning, errors, and events
Scroll within the panel for all 11 ·
Build 12
SDKs, sample code, and the tooling you integrate with
Scroll within the panel for all 12 ·
Access & Security 2
Authentication, authorization, and security posture
Learn 1
Tutorials, courses, talks, and written guidance
Operate 6
Status, limits, changes, and where to get help
Commercial 4
Pricing, plans, and the legal terms of use
Company 7
The organization behind the API
Scroll within the panel for all 7 ·
Other 3
Properties that don't map to a standard resource type
← All providers · Data indexed from github.com/api-evangelist/hyperbolic-ai · machine-readable index on apis.io