PostgresML

PostgresML brings machine learning and AI into PostgreSQL. Using the open-source pgml extension it runs embedding generation, LLM inference, vector search, and classic supervised learning (regression, classification, clustering) directly inside the database, so applications index, filter, and rank vectors and generate fact-based, real-time outputs without operating a separate ML or vector stack. It is consumed as SQL over the Postgres wire protocol and through the first-party Korvus SDK (Python, JavaScript, Rust, and C bindings), which unifies the entire RAG pipeline into a single database query. PostgresML Cloud offers managed serverless and dedicated Postgres databases with the extensions pre-installed, and PgCat provides connection pooling, sharding, and failover. There is no REST/HTTP API; authentication is a standard PostgreSQL connection string. Backed by Amplify Partners.

PostgresML is profiled on the APIs.io network. Tagged areas include Company, Artificial Intelligence, Machine Learning, Vector Search, and Embeddings.

PostgresML’s developer surface includes documentation, engineering blog, pricing, signup flow, support, authentication, and 12 more developer resources.

🌐 Visit website 📡 Profile data on GitHub

30.6/100 thin ▬ flat Agent 3/100 human only Front door AI 6/6 dominant Heartbeat 0/100 dormant saas Full breakdown ↓
scored 2026-11-06 · rubric v0.27.0 · Show this score on your site →

AccessPaid

0 APIs

Company Artificial Intelligence Machine Learning Vector Search Embeddings PostgreSQL RAG LLM Database

Kin Score

Kin Score How this is scored →
scored 2026-11-06 · rubric v0.27.0

Composite quality — 30.6/100 · thin

Contract Quality 0.0 / 21

Developer Ergonomics 7.7 / 17

Access Clarity 13.0 / 17

Operational Transparency 0.3 / 11

Contract Governance 0.0 / 10

Discoverability 4.7 / 9

Regulatory Posture 2.7 / 15

Agent readiness — 3/100 · human only

Machine-Readable Contract 0 / 18

Agentic Access Contract 0 / 10

Documented Reversibility 0 / 6

MCP Server 0 / 12

Machine-Readable Auth 10 / 10

Idempotency 0 / 9

Stable Error Semantics 0 / 8

Request/Response Examples 0 / 7

Rate-Limit Signaling 0 / 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 could not be measured. We hold no machine-readable contract for this provider to read, so there is nothing to measure a write surface against. Excluded rather than scored zero: never-measured and measured-empty are different facts. Publishing an OpenAPI is what makes this facet — and several others — scorable at all.

The six quality facets above are damped to 85 points between them, because the conditional facet above carries the other 15. That is why each facet's contribution is shown against a damped maximum: raising a quality facet moves the composite by 85% 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):

Machine-Readable Contract +18 pts — api_specs includes an OpenAPI document
MCP Server +12 pts — deployment.mode + probe verdict on the mcp/ manifest, not the pointer; an unprobed slug falls back to authorship
Agentic Access Contract +10 pts — agentic_access artifact present (x-agentic-access), graded by author
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)
Stable Error Semantics +8 pts — OpenAPI $refs a shared error schema across 4xx/5xx responses (verified), else common[].type includes ErrorCatalog / ErrorCodes / DeclineCodes
A2A Agent Card +8 pts — a2a/-a2a.yml manifest, graded on conformance.grade against A2A 1.0.0

12 more unmet checks in the Agent Readiness table above.

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

Contract Quality scores 0.0 — up to +25.0 composite points
Operational Transparency scores 2.6 — up to +12.7 composite points
Contract Governance scores 0.0 — up to +12.0 composite points

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.

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/postgresml/apis.yml: open an issue on github.com/api-evangelist/postgresml 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 →

PostgresML API pricing

Prices published by PostgresML, read 2026-10-07 from postgresml.org/pricing.

Plan	Price
Serverless	From $7.50 per query hour Includes: Pay-per-use, Burst GPU capacity, Access curated models, Support on Discord
Dedicated	From $0.60 per instance hour Includes: Committed use discounts, Dedicated hardware, any HuggingFace model, deploy on major clouds, private
Enterprise	Custom pricing Includes: VPC deployments, multiple GPUs, custom SLAs, premium support
Tables & index storage	$0.25/GB per month
Retrieval, filtering, ranking & other queries	$7.50 per hour Includes: Embeddings, LLMs, Fine tuning, Machine learning included w/ queries

Plan details, limits and sources →

Pricing Plans 1

Published pricing tiers and plan structures.

Postgresml Plans Pricing

5 plans

PLANS

Security Posture 2

Authentication, domain security, vulnerability disclosure, and trust-center signals.

Postgresml Authentication

connection-string · 1 scheme

SECURITY

Postgresml Domain Security

TLSv1.3

SECURITY

Resources

Get Started 2

Portal, sign-up, and the first successful call

DeveloperPortal

DeveloperPortal

SignUp

SignUp

Documentation 1

Reference material describing how the API behaves

Documentation

Documentation

Agent Surfaces 2

MCP servers, agent skills, and machine-readable catalogs

WellKnown

WellKnown

LLMsTxt

LLMsTxt

Design & Contract 1

Pagination, idempotency, versioning, errors, and events

Conventions

Conventions

Build 3

SDKs, sample code, and the tooling you integrate with

GitHubOrganization

GitHubOrganization

Packages

Packages

SDKs

SDKs

Access & Security 2

Authentication, authorization, and security posture

Authentication

Authentication

DomainSecurity

DomainSecurity

Operate 1

Status, limits, changes, and where to get help

Support

Support

Commercial 4

Pricing, plans, and the legal terms of use

Plans

Pricing

TermsOfService

PrivacyPolicy

Company 2

The organization behind the API

Website

Website

Blog

Blog

Source (apis.yml)

aid: postgresml
deliveryModel:
  model: saas
  open_source: false
  commercial: true
  callable_host: false
  label: Hosted service · you call their endpoint
  confidence: medium
  source:
  - pricing
  generated: '2026-08-28'
  method: derived
accessModel:
  pricing: paid
  onboarding: unknown
  trial: false
  try_now: false
  public: false
  label: Paid
  confidence: medium
  source:
  - plans
  - authentication
  - security
  generated: '2026-11-06'
  method: derived
image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/icons/postgresml.png
name: PostgresML
description: PostgresML brings machine learning and AI into PostgreSQL. Using the open-source pgml extension it runs embedding
  generation, LLM inference, vector search, and classic supervised learning (regression, classification, clustering) directly
  inside the database, so applications index, filter, and rank vectors and generate fact-based, real-time outputs without
  operating a separate ML or vector stack. It is consumed as SQL over the Postgres wire protocol and through the first-party
  Korvus SDK (Python, JavaScript, Rust, and C bindings), which unifies the entire RAG pipeline into a single database query.
  PostgresML Cloud offers managed serverless and dedicated Postgres databases with the extensions pre-installed, and PgCat
  provides connection pooling, sharding, and failover. There is no REST/HTTP API; authentication is a standard PostgreSQL
  connection string. Backed by Amplify Partners.
url: https://raw.githubusercontent.com/api-evangelist/postgresml/refs/heads/main/apis.yml
x-type: company
x-source: vc-portfolio
x-backed-by:
- amplify-partners
x-tier: stub
x-tier-reason: portfolio-lead
specificationVersion: '0.23'
created: '2026-07-17'
modified: '2026-07-20'
tags:
- Company
- Artificial Intelligence
- Machine Learning
- Vector Search
- Embeddings
- PostgreSQL
- RAG
- LLM
- Database
tags_raw:
- Company
- Ai Ml
- Machine Learning
- Vector Search
- Embeddings
- PostgreSQL
- RAG
- LLM
- Database
- Machine-Learning
apis: []
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
- FN: APIs.json
  email: info@apis.io
common:
- type: Plans
  url: plans/postgresml-plans-pricing.yml
- type: Website
  url: https://postgresml.org
- type: DeveloperPortal
  url: https://postgresml.org/docs
- type: Documentation
  url: https://postgresml.org/docs
- type: Blog
  url: https://postgresml.org/blog
- type: Pricing
  url: https://postgresml.org/pricing
- type: SignUp
  url: https://postgresml.org/signup
- type: TermsOfService
  url: https://postgresml.org/tos
- type: PrivacyPolicy
  url: https://postgresml.org/privacy
- type: Support
  url: https://discord.gg/DmyJP3qJ7U
- type: GitHubOrganization
  url: https://github.com/postgresml
- type: Packages
  url: packages/postgresml-packages.yml
- type: SDKs
  url: packages/postgresml-packages.yml
- type: Authentication
  url: authentication/postgresml-authentication.yml
- type: Conventions
  url: conventions/postgresml-conventions.yml
- type: DomainSecurity
  url: security/postgresml-domain-security.yml
- type: WellKnown
  url: well-known/postgresml-well-known.yml
- type: LLMsTxt
  url: llms/postgresml-llms.txt
x-enrichment:
  date: '2026-07-20'
  status: enriched
  artifacts_added: 6
  pass: local-v1

PostgresML API — frequently asked questions

How do I authenticate with the PostgresML API?

PostgresML uses bearer-token authentication (connection-string · 1 scheme). The details are profiled on APIs.io as Postgresml Authentication.

How much does the PostgresML API cost?

PostgresML publishes its prices; its Serverless plan is From $7.50 per query hour as of 2026-10-07. APIs.io lists 5 pricing plans for PostgresML, with each plan's price, limits and inclusions under Pricing on this page.

Is the PostgresML API ready for AI agents?

PostgresML has a Kin Score of 30.6 out of 100 (thin) and an Agent Readiness score of 2.5 (human-only), scored by API Evangelist on 2026-11-06.

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get_provider_toolsEvery MCP tool they ship, with the operation each wraps.
get_provider_evidenceHow each part of their score was established. Free — the basis for a claim should not sit behind it.
get_provider_ratingPRO — composite, band, trend and facet scores.
apis_io_searchSTART HERE — APIs, providers and tags for one query, each with its total.
resolveTurn a domain, URL or GitHub org into the provider it belongs to.
find_cohortsEvery scored population of providers in the catalog.

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