RelationalAI

RelationalAI is a decision-intelligence platform delivered as a Native App inside Snowflake. Its core product, Rel, builds a semantic model from data in Snowflake and reasons over it — combining graph analytics, rules, predictive graph neural networks, and prescriptive optimization — to answer high-stakes business questions (supply chain, pricing, inventory, demand forecasting) with transparent, validatable reasoning steps. Developers work through PyRel (the `relationalai` Python SDK), the `rai` CLI, and per-language HTTP SDKs (JavaScript, Julia, Java), with queries compiled to the protobuf Logical Query Protocol (LQP) and executed on RelationalAI engines running in Snowflake. RelationalAI publishes its own coding-agent Skills to drive the workflow.

RelationalAI is profiled on the APIs.io network. Tagged areas include Company, Decision Intelligence, Knowledge Graph, Semantic Layer, and Snowflake.

RelationalAI’s developer surface includes documentation, API reference, getting-started guide, engineering blog, pricing, signup flow, CLI, and 20 more developer resources.

🌐 Visit website 📡 Profile data on GitHub

52.7/100 developing ▬ flat Agent 19/100 agent aware Front door AI 2/6 trace Heartbeat 54/100 slowing saas Full breakdown ↓
scored 2026-11-06 · rubric v0.27.0 · Show this score on your site →

AccessPaid

0 APIs

Company Decision Intelligence Knowledge Graph Semantic Layer Snowflake Graph Analytics Machine Learning Optimization Reasoning Artificial Intelligence

Kin Score

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

Composite quality — 52.7/100 · developing

Contract Quality 5.1 / 21

Developer Ergonomics 12.5 / 17

Access Clarity 17.0 / 17

Operational Transparency 2.0 / 11

Contract Governance 1.9 / 10

Discoverability 4.7 / 9

Regulatory Posture 4.1 / 15

Agent readiness — 19/100 · agent aware

Machine-Readable Contract 18 / 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 5 / 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):

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
Rate-Limit Signaling +7 pts — OpenAPI responses document X-RateLimit-* / RateLimit headers (verified), else a rate_limits artifact or RateLimits link (documented)

10 more unmet checks in the Agent Readiness table above.

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

Contract Quality scores 24.0 — up to +19.0 composite points
Operational Transparency scores 18.4 — up to +10.6 composite points
Contract Governance scores 18.2 — up to +9.8 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/relationalai/apis.yml: open an issue on github.com/api-evangelist/relationalai 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 →

Markets this provider is in provisional

The Kin Score Market layer reads a provider inside its market: its position, and how deep its integrations go. Depth is only comparable within a position. Provisional and unweighted: not part of the Kin Score or agent readiness.

Market	Position	Depth	Registered MCP server	Declares compatibility
Agent Skills (SKILL.md packages)	distribution	19.2 (thin)	-	1

Registered MCP server and declares compatibility are report only — not weighted. Undeclared means we found nothing we can read, so there is no number: we never infer integrations.

Standards implemented 1

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.

Agent Skills

measured — observed by APIs.io (published)

RelationalAI API pricing

Prices published by RelationalAI, read 2026-10-07 from www.relational.ai/pricing.

Plan	Price
Standard	$2.00/ Rel Unit (USD) Includes: Semantic Model, Natural Language Reasoning
Enterprise	$3.00/ Rel Unit (USD) Includes: Early access environment, Query acceleration, Prescriptive reasoning
Business Critical	$4.00/ Rel Unit (USD) Includes: Customer-managed keys (tri-secret secure), Private connectivity
Virtual Private	Contact sales Includes: Everything in Business Critical, Complete isolation

Plan details, limits and sources →

Billed by: rel unit · FinOps profile →

Pricing Plans 1

Published pricing tiers and plan structures.

Relationalai Plans Pricing

4 plans

PLANS

FinOps 1

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

Relationalai Finops

FINOPS

Security Posture 3

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

Relationalai Authentication

snowflake-connection · 1 scheme

SECURITY

Relationalai Domain Security

TLSv1.3 · HSTS · DMARC

SECURITY

Relationalai Trust Center

SOC 2, ISO 27001, HIPAA, GDPR

SECURITY

Resources

Get Started 3

Portal, sign-up, and the first successful call

DeveloperPortal

DeveloperPortal

GettingStarted

GettingStarted

SignUp

SignUp

Documentation 2

Reference material describing how the API behaves

Documentation

Documentation

APIReference

APIReference

Agent Surfaces 3

MCP servers, agent skills, and machine-readable catalogs

AgentSkill

AgentSkill

LLMsTxt

LLMsTxt

WellKnown

WellKnown

Design & Contract 3

Pagination, idempotency, versioning, errors, and events

Conventions

Conventions

Lifecycle

Lifecycle

Conformance

Conformance

Build 4

SDKs, sample code, and the tooling you integrate with

GitHubOrganization

Packages

SDKs

CLI

Access & Security 4

Authentication, authorization, and security posture

TrustCenter

Compliance

DomainSecurity

Authentication

Operate 1

Status, limits, changes, and where to get help

ChangeLog

ChangeLog

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

Other 1

Properties that don't map to a standard resource type

Protobuf

Protobuf

Source (apis.yml)

aid: relationalai
name: RelationalAI
description: RelationalAI is a decision-intelligence platform delivered as a Native App inside Snowflake. Its core product,
  Rel, builds a semantic model from data in Snowflake and reasons over it — combining graph analytics, rules, predictive graph
  neural networks, and prescriptive optimization — to answer high-stakes business questions (supply chain, pricing, inventory,
  demand forecasting) with transparent, validatable reasoning steps. Developers work through PyRel (the `relationalai` Python
  SDK), the `rai` CLI, and per-language HTTP SDKs (JavaScript, Julia, Java), with queries compiled to the protobuf Logical
  Query Protocol (LQP) and executed on RelationalAI engines running in Snowflake. RelationalAI publishes its own coding-agent
  Skills to drive the workflow.
url: https://raw.githubusercontent.com/api-evangelist/relationalai/refs/heads/main/apis.yml
x-type: company
x-source: vc-portfolio
x-backed-by:
- menlo-ventures
x-tier: stub
x-tier-reason: portfolio-lead
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://cdn.prod.website-files.com/68c66131e7dc76329c719913/68df015bc18ca537629f59b3_og-home-relationalai.jpg
specificationVersion: '0.23'
created: '2026-07-17'
modified: '2026-07-21'
tags:
- Company
- Decision Intelligence
- Knowledge Graph
- Semantic Layer
- Snowflake
- Graph Analytics
- Machine Learning
- Optimization
- Reasoning
- Artificial Intelligence
tags_raw:
- Company
- Decision Intelligence
- Knowledge Graph
- Semantic Layer
- Snowflake
- Graph Analytics
- Machine Learning
- Optimization
- Reasoning
- AI
- Machine-Learning
apis: []
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
- FN: APIs.json
  email: info@apis.io
common:
- type: Plans
  url: plans/relationalai-plans-pricing.yml
- type: Website
  url: https://relational.ai
- type: DeveloperPortal
  url: https://docs.relational.ai/
- type: Documentation
  url: https://docs.relational.ai/
- type: APIReference
  url: https://docs.relational.ai/api
- type: GettingStarted
  url: https://docs.relational.ai/build/setup
- type: Blog
  url: https://relational.ai/blog
- type: GitHubOrganization
  url: https://github.com/RelationalAI
- type: Pricing
  url: https://relational.ai/pricing
- type: SignUp
  url: https://relational.ai/get-in-touch
- type: TermsOfService
  url: https://cdn.prod.website-files.com/68c66131e7dc76329c719913/68d6bfd57445ffd406a54fec_relationalai-tos.pdf
- type: PrivacyPolicy
  url: https://relational.ai/privacy-policy
- type: TrustCenter
  url: security/relationalai-trust-center.yml
- type: Compliance
  url: https://trust.relational.ai/
- type: DomainSecurity
  url: security/relationalai-domain-security.yml
- type: Packages
  url: packages/relationalai-packages.yml
- type: SDKs
  url: packages/relationalai-packages.yml
- type: CLI
  url: cli/relationalai-cli.yml
- type: AgentSkill
  url: skills/_index.yml
- type: Authentication
  url: authentication/relationalai-authentication.yml
- type: Conventions
  url: conventions/relationalai-conventions.yml
- type: Lifecycle
  url: lifecycle/relationalai-lifecycle.yml
- type: ChangeLog
  url: changelog/relationalai-changelog.yml
- type: Conformance
  url: conformance/relationalai-conformance.yml
- type: Protobuf
  url: grpc/relationalai-lqp.yml
- type: LLMsTxt
  url: llms/relationalai-llms.txt
- type: WellKnown
  url: well-known/relationalai-well-known.yml
x-enrichment:
  date: '2026-07-21'
  status: enriched
  artifacts_added: 14
  pass: local-v1

RelationalAI API — frequently asked questions

How do I authenticate with the RelationalAI API?

RelationalAI uses bearer-token authentication (snowflake-connection · 1 scheme). The details are profiled on APIs.io as Relationalai Authentication.

How much does the RelationalAI API cost?

RelationalAI publishes its prices; its Standard plan is $2.00/ Rel Unit (USD) as of 2026-10-07. APIs.io lists 4 pricing plans for RelationalAI, with each plan's price, limits and inclusions under Pricing on this page.

Is the RelationalAI API ready for AI agents?

RelationalAI has a Kin Score of 52.7 out of 100 (developing) and an Agent Readiness score of 19.1 (agent-aware), 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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