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).

Scalable Inference Serving publishes 9 API contracts indexed on the APIs.io network, including Health API, Inference API, Metadata API, and 6 more. Tagged areas include Artificial Intelligence, CNCF, Deployment, Inference, and Kubernetes.

The Scalable Inference Serving catalog on APIs.io includes 1 JSON-LD context and 2 Spectral governance rulesets.

Scalable Inference Serving’s developer surface includes authentication, getting-started guide, engineering blog, and 13 more developer resources.

📡 Profile data on GitHub

33.4/100 thin ▬ flat Agent 30/100 agent ready Heartbeat 100/100 alive saas Full breakdown ↓
scored 2026-11-13 · rubric v0.28.0 · Show this score on your site →

AccessEnterpriseApproval

1 published contract

Artificial Intelligence CNCF Deployment Inference Kubernetes LLM Machine Learning Model Serving MLOps Scalability

What this lets a business do 1

Business capabilities this provider's published APIs can perform, derived from its own contracts. Browse all capabilities →

Information & Data Management

BC-610

InferenceModels

POST /v2/models/{model_name}/infer GET /v2/models/{model_name} GET /v2/models/{model_name}/versions/{model_version}

Kin Score

Kin Score How this is scored →
scored 2026-11-13 · rubric v0.28.0

Composite quality — 33.4/100 · thin

Contract Quality 10.4 / 19

Developer Ergonomics 6.1 / 15

Access Clarity 1.3 / 15

Operational Transparency 2.3 / 10

Contract Governance 2.5 / 9

Discoverability 3.6 / 8

Regulatory Posture 1.5 / 15

Create-or-Update Ergonomics 0.0 / 10

Agent readiness — 30/100 · agent ready

Machine-Readable Contract 18 / 18

Agentic Access Contract 10 / 10

Documented Reversibility 0 / 6

MCP Server 0 / 12

Machine-Readable Auth 10 / 10

Idempotency 0 / 9

Stable Error Semantics 8 / 8

Request/Response Examples 7 / 7

Rate-Limit Signaling 7 / 7

Typed Event Surface 0 / 6

Agent Skills 0 / 5

Well-Known Catalog 0 / 4

Consent & Bot Identity 0 / 3

A2A Agent Card 0 / 8

Dry-Run / Simulate Mode 0 / 4

Delegated User Identity 0 / 6

Protected Resource Metadata 0 / 5

Registration Without a Human 0 / 6

Agentic Commerce Well-Known Document 0 / 5

Pagination 5 / 5

Distinct Operations 3 / 3

MCP Tool Quality 6 / 6

Regulatory Posture applies to this provider. Its tags matched the Horizontal (data, software, accessibility, platform) regime, so Regulatory Posture carries 15 points of the composite. If this regime is wrong for your business, say so on your provider repo — the applicability map is public and we will correct it.

Create-or-Update Ergonomics applies to this provider. This API accepts writes, so it carries 10 points of the composite. It is scored from the published contracts themselves: whether a caller can create-or-update in one call, whether the write accepts a key the caller already holds, and whether the response says which branch ran. Without that, every write needs a search-and-branch in front of it, and the first time that check is skipped a duplicate record is created. Scored against the observed mean rather than raw — a provider at the catalog average is unchanged by this facet, not penalised by it.

The six quality facets above are damped to 75 points between them, because the conditional facet above carries the other 25. That is why each facet's contribution is shown against a damped maximum: raising a quality facet moves the composite by 75% of its nominal weight, not 100%. The full arithmetic is at apis.io/rating/.

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Agent readiness — checks not met, largest first (exact, from this profile):

MCP Server +12 pts — deployment.mode + probe verdict on the mcp/ manifest, not the pointer; an unprobed slug falls back to authorship Ask about this →
Idempotency +9 pts — OpenAPI mutating operations declare an idempotency key — a header, a request-body property, or a named de-duplication token whose own description says so (verified); else common[].type matches /idempoten/i (documented) Ask about this →
A2A Agent Card +8 pts — a2a/-a2a.yml manifest, graded on conformance.grade against A2A 1.0.0 Ask about this →
Registration Without a Human +6 pts — a `registration_endpoint` in the served RFC 8414 / OIDC discovery document Ask about this →
Delegated User Identity +6 pts — a served discovery document listing authorization_code in grant_types_supported (served), else an oauth2 authorizationCode flow or openIdConnect declared in an OpenAPI (documented) Ask about this →
Typed Event Surface +6 pts — AsyncAPI document, OR webhooks/callbacks objects in the as-published OpenAPI, OR common[].type includes Webhooks Ask about this →

7 more unmet checks in the Agent Readiness table above.

Kin Score — facets with the most composite points still available (weight × headroom):

Access Clarity scores 8.5 — up to +18.3 composite points Ask about this →
Developer Ergonomics scores 40.5 — up to +11.9 composite points Ask about this →
Contract Quality scores 55.4 — up to +11.2 composite points Ask about this →

Each check links to what satisfies it. Publish the artifact, or tell us where it already lives, and the score moves on the next scoring run.

Not sure how this applies to your stack? Ask about any one item. Kin Lane reads every question, looks at your profile and your situation, and replies with what a fix would take — scoped to that one area, priced up front, yours whether you ship it or not. Ask a question →

Improve this rating by publishing the missing artifacts — every area above can be raised, and the full rubric is at apis.io/rating/. Every facet and dimension name above is a link: it opens that measurement's own page — what it means, the exact checks that feed it, how the whole catalog distributes on it, and the providers at the top of it. This rating is computed from the provider profile at artifacts.apis.io/scalable-inference-serving/apis.yml: open an issue on github.com/api-evangelist/scalable-inference-serving to ask a question or point us at an artifact you publish. Submit an artifact on GitHub — free → Manage your own listing — the Influence plan, $499/mo →

Standards implemented 2

Standards this provider implements or carries. De facto ones are profiled and measured by the API Commons; formal ones come from the standards directory and are linked by the provider's own tags. The evidence column says how each claim was established — not that it was made.

The OpenAI Interface

declared — publishes a spec that declares them

2 core of 338 operations graded · api-commons/models

OpenAPI

measured — observed by APIs.io (published)

APIs 9

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

Plan details, limits and sources →

Rate limits

Contracted limits: negotiated under commercial / partner agreement; not publicly published varies

Compiled by API Evangelist, not yet checked against Scalable Inference Serving's documentation. Rate-limit details →

Pricing Plans 1

Published pricing tiers and plan structures.

Scalable Inference Serving Plans Pricing

1 plans

PLANS

Rate Limits 1

Documented rate limits and quota policies.

Scalable Inference Serving Rate Limits

1 limits

RATE LIMITS

FinOps 1

Cost, billing, and metering signals for API financial operations.

Scalable Inference Serving Finops

FINOPS

Semantic Vocabularies 1

JSON-LD contexts and semantic vocabularies used across these APIs.

Scalable Inference Serving Context

12 classes · 11 properties

JSON-LD

Spectral Rules 2

Spectral governance rulesets for linting and validating these APIs. Counts are the severity each rule declares, not results of running them.

Scalable Inference Serving — kserve open inference protocol rules

17 rules · 5 error-severity 9 warn-severity 3 info-severity

SPECTRAL

Scalable Inference Serving — jsonschema rules

6 rules · 5 warn-severity 1 info-severity

SPECTRAL

4 properties

JSON SCHEMA

Scroll for all 15

JSON Structure 2

JSON Structure definitions describing this provider's data shapes.

Kserve Inference Request Structure

0 properties

JSON STRUCTURE

Scalable Inference Serving Structure

0 properties

JSON STRUCTURE

Examples 9

Example request and response payloads for these APIs.

Kserve Check Server Liveness Example

2 fields

EXAMPLE

Kserve Get Model Metadata Example

Get Started 1

Portal, sign-up, and the first successful call

GettingStarted

GettingStarted

Documentation 3

Reference material describing how the API behaves

OpenAPI

OpenAPI

JSONSchema

JSONSchema

JSONSchema

JSONSchema

Agent Surfaces 1

MCP servers, agent skills, and machine-readable catalogs

AgenticAccess

AgenticAccess

Design & Contract 3

Pagination, idempotency, versioning, errors, and events

SpectralRuleset

SpectralRuleset

JSONLD

JSONLD

Vocabulary

Vocabulary

Build 1

SDKs, sample code, and the tooling you integrate with

GitHubOrganization

GitHubOrganization

Access & Security 1

Authentication, authorization, and security posture

Authentication

Authentication

Operate 1

Status, limits, changes, and where to get help

RateLimits

RateLimits

Commercial 2

Pricing, plans, and the legal terms of use

Plans

Plans

FinOps

FinOps

Company 1

The organization behind the API

Blog

Blog

Other 2

Properties that don't map to a standard resource type

CapabilityMap

CapabilityMap

CNCF Landscape

CNCF Landscape

Source (apis.yml)

name: Scalable Inference Serving
description: 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).
deliveryModel:
  model: saas
  license_evidence:
    not_product:
    - bentoml/bentoml
    - kserve/kserve
    - mlflow/mlflow
    - ray-project/ray
    - triton-inference-server/server
  open_source: unknown
  commercial: true
  callable_host: false
  label: Hosted service · you call their endpoint
  confidence: low
  source:
  - openapi
  - pricing
  - repository-not-product
  generated: '2026-09-25'
  method: derived
accessModel:
  pricing: enterprise
  onboarding: approval
  trial: false
  try_now: false
  public: false
  label: Enterprise · Requires approval
  confidence: low
  source:
  - plans
  generated: '2026-11-13'
  method: derived
image: https://kserve.github.io/website/images/KServe.png
url: https://raw.githubusercontent.com/api-evangelist/scalable-inference-serving/refs/heads/main/apis.yml
created: '2024-01-01'
modified: '2026-05-19'
specificationVersion: '0.23'
tags:
- Artificial Intelligence
- CNCF
- Deployment
- Inference
- Kubernetes
- LLM
- Machine Learning
- Model Serving
- MLOps
- Scalability
tags_raw:
- AI
- CNCF
- Deployment
- Inference
- Kubernetes
- LLM
- Machine Learning
- Model Serving
- MLOps
- Scalability
- Machine-Learning
apis:
- name: BentoML REST API
  description: 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 OpenAPI/Swagger documentation, supports adaptive batching,
    and integrates with KServe for Kubernetes deployment. BentoML 1.0 introduced the Runner abstraction for parallelizing
    inference workloads with adaptive batching and independent scaling of pre/post-processing from model inference.
  image: https://www.bentoml.com/favicon.ico
  humanUrl: https://www.bentoml.com/
  baseUrl: https://api.bentoml.example.com
  tags:
  - Batching
  - Inference
  - Model Serving
  - Open Source
  - Python
  - REST API
  tags_raw:
  - Batching
  - Inference
  - Model Serving
  - Open Source
  - Python
  - REST API
  - Open-Source
  properties:
  - type: Documentation
    url: https://docs.bentoml.com/en/latest/
  - type: GitHub
    url: https://github.com/bentoml/BentoML
  - type: GettingStarted
    url: https://docs.bentoml.com/en/latest/get-started/quickstart.html
  - type: Pricing
    url: https://www.bentoml.com/pricing
  - type: APIReference
    url: https://docs.bentoml.com/en/latest/reference/index.html
  contact:
  - type: Community
    url: https://l.bentoml.com/join-slack
  - type: GitHubIssues
    url: https://github.com/bentoml/BentoML/issues
- name: vLLM OpenAI-Compatible API
  description: 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 API allowing seamless migration from OpenAI endpoints. In
    2026, vLLM integrates with KServe via LLMInferenceService and llm-d for production-grade distributed LLM inference. Powers
    major LLM deployments at scale.
  image: https://docs.vllm.ai/en/stable/_static/logo/vllm-logo-text-light.png
  humanUrl: https://docs.vllm.ai/
  baseUrl: https://vllm.example.com/v1
  tags:
  - GPU
  - Inference
  - KV Cache
  - LLM
  - Model Serving
  - Open Source
  - OpenAI-Compatible
  tags_raw:
  - GPU
  - Inference
  - KV Cache
  - LLM
  - Model Serving
  - Open Source
  - OpenAI-Compatible
  - Open-Source
  properties:
  - type: Documentation
    url: https://docs.vllm.ai/en/stable/
  - type: GitHub
    url: https://github.com/vllm-project/vllm
  - type: APIReference
    url: https://docs.vllm.ai/en/stable/serving/openai_compatible_server.html
  - type: ChangeLog
    url: https://github.com/vllm-project/vllm/releases
  contact:
  - type: GitHubIssues
    url: https://github.com/vllm-project/vllm/issues
  - type: Slack
    url: https://vllm-dev.slack.com/
- name: NVIDIA Triton Inference Server HTTP API
  description: NVIDIA Triton Inference Server is an open-source inference serving software that implements the KServe Open
    Inference Protocol (V2). Supports TensorRT, ONNX, TensorFlow, PyTorch, and Python backends. Provides dynamic batching,
    model ensembles, model analyzers, and GPU/CPU inference. Used extensively in production ML pipelines requiring maximum
    throughput.
  image: https://developer.nvidia.com/favicon.ico
  humanUrl: https://developer.nvidia.com/triton-inference-server
  baseUrl: https://triton.example.com
  tags:
  - GPU
  - Inference
  - Model Serving
  - NVIDIA
  - Open Source
  - TensorRT
  - Triton
  tags_raw:
  - GPU
  - Inference
  - Model Serving
  - NVIDIA
  - Open Source
  - TensorRT
  - Triton
  - Open-Source
  properties:
  - type: Documentation
    url: https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/
  - type: GitHub
    url: https://github.com/triton-inference-server/server
  - type: GettingStarted
    url: https://github.com/triton-inference-server/tutorials
  - type: APIReference
    url: https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/customization_guide/inference_protocols.html
  contact:
  - type: GitHubIssues
    url: https://github.com/triton-inference-server/server/issues
  - type: Forums
    url: https://forums.developer.nvidia.com/c/ai-data-science/deep-learning/triton-inference-server/
- name: MLflow Model Registry REST API
  description: MLflow is an open source platform for managing the ML lifecycle, including experiment tracking, reproducibility,
    and deployment. The MLflow REST API manages experiments, runs, metrics, parameters, artifacts, and the Model Registry
    for versioning and staging model deployments. CNCF-adjacent; used with KServe for model lifecycle management.
  image: https://mlflow.org/favicon.ico
  humanUrl: https://mlflow.org/
  baseUrl: https://mlflow.example.com/api/2.0
  tags:
  - Experiment Tracking
  - Machine Learning
  - Model Registry
  - MLOps
  - Open Source
  - Versioning
  tags_raw:
  - Experiment Tracking
  - Machine Learning
  - Model Registry
  - MLOps
  - Open Source
  - Versioning
  - Machine-Learning
  - Open-Source
  properties:
  - type: Documentation
    url: https://mlflow.org/docs/latest/rest-api.html
  - type: GitHub
    url: https://github.com/mlflow/mlflow
  - type: GettingStarted
    url: https://mlflow.org/docs/latest/getting-started/intro-quickstart/
  - type: APIReference
    url: https://mlflow.org/docs/latest/rest-api.html
  contact:
  - type: Community
    url: https://github.com/mlflow/mlflow/discussions
  - type: GitHubIssues
    url: https://github.com/mlflow/mlflow/issues
- name: Ray Serve REST API
  description: Ray Serve is a scalable model serving library built on Ray, designed for building online inference APIs. Supports
    composable deployments, autoscaling, HTTP ingress, gRPC, WebSockets, and request batching. Integrates with any ML framework.
    The Ray Serve dashboard and REST API manage deployments, replicas, routes, and application status.
  image: https://www.ray.io/favicon.ico
  humanUrl: https://docs.ray.io/en/latest/serve/index.html
  baseUrl: https://ray-serve.example.com
  tags:
  - Auto-Scaling
  - Inference
  - Machine Learning
  - Model Serving
  - Open Source
  - Python
  - Ray
  tags_raw:
  - Autoscaling
  - Inference
  - Machine Learning
  - Model Serving
  - Open Source
  - Python
  - Ray
  - Machine-Learning
  - Open-Source
  properties:
  - type: Documentation
    url: https://docs.ray.io/en/latest/serve/index.html
  - type: GitHub
    url: https://github.com/ray-project/ray
  - type: GettingStarted
    url: https://docs.ray.io/en/latest/serve/getting_started.html
  - type: APIReference
    url: https://docs.ray.io/en/latest/serve/api/index.html
  contact:
  - type: Community
    url: https://discuss.ray.io/
  - type: GitHubIssues
    url: https://github.com/ray-project/ray/issues
- aid: scalable-inference-serving:scalable-inference-serving-health-api
  name: Scalable Inference Serving Health API
  description: Server and model liveness and readiness probes
  tags:
  - Health
  properties:
  - type: OpenAPI
    url: openapi/scalable-inference-serving-health-api-openapi.yml
  - type: Documentation
    url: https://kserve.github.io/website/docs/intro
  - type: GitHub
    url: https://github.com/kserve/kserve
  - type: ChangeLog
    url: https://github.com/kserve/kserve/releases
  - type: GettingStarted
    url: https://kserve.github.io/website/docs/get_started/
  - type: SwaggerUI
    url: https://kserve.github.io/website/latest/reference/swagger-ui/
- aid: scalable-inference-serving:scalable-inference-serving-inference-api
  name: Scalable Inference Serving Inference API
  description: Model inference request endpoints
  tags:
  - Inference
  properties:
  - type: OpenAPI
    url: openapi/scalable-inference-serving-inference-api-openapi.yml
  - type: Documentation
    url: https://kserve.github.io/website/docs/intro
  - type: GitHub
    url: https://github.com/kserve/kserve
  - type: ChangeLog
    url: https://github.com/kserve/kserve/releases
  - type: GettingStarted
    url: https://kserve.github.io/website/docs/get_started/
  - type: SwaggerUI
    url: https://kserve.github.io/website/latest/reference/swagger-ui/
- aid: scalable-inference-serving:scalable-inference-serving-metadata-api
  name: Scalable Inference Serving Metadata API
  description: Server and model metadata endpoints
  tags:
  - Metadata
  properties:
  - type: OpenAPI
    url: openapi/scalable-inference-serving-metadata-api-openapi.yml
  - type: Documentation
    url: https://kserve.github.io/website/docs/intro
  - type: GitHub
    url: https://github.com/kserve/kserve
  - type: ChangeLog
    url: https://github.com/kserve/kserve/releases
  - type: GettingStarted
    url: https://kserve.github.io/website/docs/get_started/
  - type: SwaggerUI
    url: https://kserve.github.io/website/latest/reference/swagger-ui/
- aid: scalable-inference-serving:scalable-inference-serving-models-api
  name: Scalable Inference Serving Models API
  description: Model management and metadata operations
  tags:
  - Models
  properties:
  - type: OpenAPI
    url: openapi/scalable-inference-serving-models-api-openapi.yml
  - type: Documentation
    url: https://kserve.github.io/website/docs/intro
  - type: GitHub
    url: https://github.com/kserve/kserve
  - type: ChangeLog
    url: https://github.com/kserve/kserve/releases
  - type: GettingStarted
    url: https://kserve.github.io/website/docs/get_started/
  - type: SwaggerUI
    url: https://kserve.github.io/website/latest/reference/swagger-ui/
common:
- type: Plans
  url: plans/scalable-inference-serving-plans-pricing.yml
- type: RateLimits
  url: rate-limits/scalable-inference-serving-rate-limits.yml
- type: FinOps
  url: finops/scalable-inference-serving-finops.yml
- type: CapabilityMap
  url: capabilities/scalable-inference-serving-capability-edges.yml
  name: Scalable Inference Serving Business Capability Map
- type: AgenticAccess
  url: agentic-access/scalable-inference-serving-agentic-access.yml
- type: Authentication
  url: https://kserve.github.io/website/docs/intro
- type: GettingStarted
  url: https://kserve.github.io/website/docs/get_started/
- type: GitHubOrganization
  url: https://github.com/kserve
- type: CNCF Landscape
  url: https://landscape.cncf.io/card-mode?project=incubating
- type: Blog
  url: https://kserve.github.io/website/blog/
- type: OpenAPI
  url: https://raw.githubusercontent.com/api-evangelist/scalable-inference-serving/main/openapi/kserve-open-inference-protocol-openapi.yml
- type: SpectralRuleset
  url: https://raw.githubusercontent.com/api-evangelist/scalable-inference-serving/main/rules/kserve-open-inference-protocol-rules.yml
- type: JSONSchema
  url: https://raw.githubusercontent.com/api-evangelist/scalable-inference-serving/main/json-schema/kserve-inference-request-schema.json
- type: JSONSchema
  url: https://raw.githubusercontent.com/api-evangelist/scalable-inference-serving/main/json-schema/kserve-model-metadata-schema.json
- type: JSONLD
  url: https://raw.githubusercontent.com/api-evangelist/scalable-inference-serving/main/json-ld/scalable-inference-serving-context.jsonld
- type: Vocabulary
  url: https://raw.githubusercontent.com/api-evangelist/scalable-inference-serving/main/vocabulary/scalable-inference-serving-vocabulary.yml
maintainers:
- name: API Evangelist
  email: kin@apievangelist.com
  url: https://apievangelist.com

Scalable Inference Serving API — frequently asked questions

Does Scalable Inference Serving have an API?

Yes. APIs.io lists 9 APIs from Scalable Inference Serving, including BentoML REST API, vLLM OpenAI-Compatible API and NVIDIA Triton Inference Server HTTP API, and 6 more.

Is there an OpenAPI specification for the Scalable Inference Serving API?

Yes. APIs.io indexes 4 OpenAPI specifications for Scalable Inference Serving, each linked for download from this page.

How do I authenticate with the Scalable Inference Serving API?

Scalable Inference Serving uses bearer-token authentication.

How much does the Scalable Inference Serving API cost?

APIs.io lists 1 pricing plan for Scalable Inference Serving, with each plan's price, limits and inclusions under Pricing on this page.

Is the Scalable Inference Serving API ready for AI agents?

Scalable Inference Serving has a Kin Score of 33.4 out of 100 (thin) and an Agent Readiness score of 30.2 (agent-ready), scored by API Evangelist on 2026-11-13.

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curl "https://apis.io/api/v1/providers/scalable-inference-serving/operations?limit=25"

How their score was established

curl "https://apis.io/api/v1/providers/scalable-inference-serving/evidence"

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