Scalable Inference Serving
A collection of APIs, frameworks, and platforms for scalable machine learning model inference serving, deployment, and management. This includes the KServe Open Inference Protocol (the CNCF standard for model serving on Kubernetes), BentoML (developer packaging and serving), vLLM (high-throughput LLM inference), NVIDIA Triton Inference Server, and supporting observability and registry tools. KServe recently joined CNCF as an incubating project (November 2025).
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 Scalable Inference Serving the way a machine reads it — 48 machine-readable artifacts across 1 API, 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 — Scalable Inference Serving scores 42.7/100 (developing), with a separate agent-readiness read of 31/100 (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. Every facet and dimension name is a link: it opens that measurement's page on APIs.io, where the rating runs across the whole catalog — the exact checks that feed it, how every profiled provider distributes on it, and who is at the top of it.
Kin Score
How this is scored →scored 2026-09-08 · rubric v0.20.0
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How we profile Scalable Inference Serving
Each block below is one kind of artifact we hold for Scalable Inference Serving. 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.
BentoML REST API
BentoML is an open-source unified inference platform for deploying and scaling AI models. It auto-generates RESTful APIs from Python service definitions, provides built-in OpenA...
vLLM OpenAI-Compatible API
vLLM is a high-throughput and memory-efficient inference engine for LLMs, implementing PagedAttention for efficient KV cache management. vLLM exposes an OpenAI-compatible REST A...
NVIDIA Triton Inference Server HTTP API
NVIDIA Triton Inference Server is an open-source inference serving software that implements the KServe Open Inference Protocol (V2). Supports TensorRT, ONNX, TensorFlow, PyTorch...
MLflow Model Registry REST API
MLflow is an open source platform for managing the ML lifecycle, including experiment tracking, reproducibility, and deployment. The MLflow REST API manages experiments, runs, m...
Ray Serve REST API
Ray Serve is a scalable model serving library built on Ray, designed for building online inference APIs. Supports composable deployments, autoscaling, HTTP ingress, gRPC, WebSoc...
Scalable Inference Serving Health API
Server and model liveness and readiness probes
Scalable Inference Serving Inference API
Model inference request endpoints
Scalable Inference Serving Metadata API
Server and model metadata endpoints
Scalable Inference Serving Models API
Model management and metadata operations
Scroll within the panel for all 9 ·
Open Collections 6
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).
API Collection
OPEN COLLECTIONKServe Open Inference Protocol API
OPEN COLLECTIONKServe Open Inference Protocol Health API
OPEN COLLECTIONKServe Open Protocol Health Inference API
OPEN COLLECTIONKServe Open Inference Protocol Health Metadata API
OPEN COLLECTIONKServe Open Inference Protocol Health Models 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 things the Kin Score reads for access clarity — renamed from commercial clarity in rubric 0.12, because a free statutory interface has access terms and no commercial ones.
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.
Scalable Inference Serving 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.
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.
Spectral 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.
Scalable Inference Serving API Rules
SPECTRALScalable Inference Serving API Rules
SPECTRALJSON Schema 15
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.
Inference Request
JSON SCHEMAModel Metadata
JSON SCHEMAErrorResponse
JSON SCHEMAInferenceRequest
JSON SCHEMAInferenceResponse
JSON SCHEMAModelMetadataResponse
JSON SCHEMAModelReadyResponse
JSON SCHEMARequestInput
JSON SCHEMARequestOutput
JSON SCHEMAResponseOutput
JSON SCHEMAServerLiveResponse
JSON SCHEMAServerMetadataResponse
JSON SCHEMAServerReadyResponse
JSON SCHEMATensorDatatype
JSON SCHEMATensorMetadata
JSON SCHEMAScroll within the panel for all 15 ·
JSON Structure 2
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.
Kserve Inference Request Structure
JSON STRUCTUREScalable Inference Serving Structure
JSON STRUCTUREExamples 9
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.
Kserve Run Inference Example
EXAMPLEScroll within the panel for all 9 ·
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 Scalable Inference Serving — 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 1
Portal, sign-up, and the first successful call
Documentation 3
Reference material describing how the API behaves
Agent Surfaces 1
MCP servers, agent skills, and machine-readable catalogs
Design & Contract 3
Pagination, idempotency, versioning, errors, and events
Build 1
SDKs, sample code, and the tooling you integrate with
Access & Security 1
Authentication, authorization, and security posture
Company 1
The organization behind the API
Other 1
Properties that don't map to a standard resource type
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This is an independent, third-party profile of Scalable Inference Serving, published by API Evangelist. We do not operate, host, resell, or support these APIs, and we are not affiliated with or endorsed by the company unless stated above. Everything here is built from publicly available information — the company's own site, developer portal, documentation, public repositories, and the specifications it publishes for public use. Nothing is obtained by breaching a system, defeating an access control, or using credentials.
The Kin Score and Agent Readiness rating are independently calculated assessments of a company's public API artifacts, scored against a published rubric. They are not certifications, endorsements, security assessments, or audits.
Corrections, re-scores, and removal are free — no partnership or purchase required, and you do not need to justify the request. A removed company is recorded as unrated, never scored zero for having asked. Acknowledgement within one business day; removal within two.
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