Datapad

Datapad is an autonomous AI data analyst platform that lets business teams analyze their data by asking questions in natural language. It connects to 50+ data sources — SQL databases, BigQuery, Snowflake, Google Ads, Facebook Ads, Shopify, HubSpot and more — and uses Text2SQL and Python code generation (powered by models such as Claude and ChatGPT) to turn questions into queries, dashboards, and insights, with a Slack bot for conversational analytics. Datapad is a San Francisco company backed by a16z. It is an end-user SaaS product and does not currently publish a public developer REST/GraphQL API, SDKs, or webhook surface; this profile captures its identity, security, and compliance posture.

Datapad is profiled on the APIs.io network. Tagged areas include Company, Analytics, Artificial Intelligence, Data, and Business Intelligence.

Datapad’s developer surface includes documentation, engineering blog, pricing, signup flow, support, and 13 more developer resources.

🌐 Visit website 📡 Profile data on GitHub

33.0/100 thin ▬ flat Agent 0/100 human only Front door AI 6/6 dominant Heartbeat 1/100 dormant saas Full breakdown ↓
scored 2026-11-06 · rubric v0.27.0 · Show this score on your site →

AccessPaid

0 APIs

Company Analytics Artificial Intelligence Data Business Intelligence Natural-Language Dashboards Software-as-a-Service

Kin Score

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

Composite quality — 33.0/100 · thin

Contract Quality 0.0 / 21

Developer Ergonomics 2.8 / 17

Access Clarity 15.7 / 17

Operational Transparency 1.5 / 11

Contract Governance 1.9 / 10

Discoverability 4.7 / 9

Regulatory Posture 4.1 / 15

Agent readiness — 0/100 · human only

Machine-Readable Contract 0 / 18

Agentic Access Contract 0 / 10

Documented Reversibility 0 / 6

MCP Server 0 / 12

Machine-Readable Auth 0 / 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
Machine-Readable Auth +10 pts — a served openid-configuration/oauth-authorization-server carrying `issuer` (served), else the securitySchemes class in the provider OpenAPIs — bound (mutualTLS, DPoP, Signature) > negotiable (oauth2 authorizationCode/clientCredentials, openIdConnect) > bearer (http bearer, apiKey)
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

13 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
Developer Ergonomics scores 16.7 — up to +16.7 composite points
Operational Transparency scores 13.2 — up to +11.3 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/datapad/apis.yml: open an issue on github.com/api-evangelist/datapad 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
Commerce Platform Ecosystems (Shopify, WooCommerce, Stripe Apps)	builds-on	17.2 (thin)	-	-

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.

Datapad API pricing

Prices published by Datapad, read 2026-10-07 from datapad.io/pricing.

Plan	Price
Basic	$30 Includes: 1 User, Unlimited Messages, Unlimited File Uploads, 1 Data Source except Database
Pro	$50 per user / month Includes: Unlimited Messages, Unlimited File Uploads, Unlimited Dashboards, 10 Data Sources except Databases
Scale	$1000 per workspace / month Includes: Unlimited Data Sources including Databases, Priority support, Workflow & Automation, Slack Bot, Auto
Enterprise	Custom Includes: Everything in Scale, Bring Your Own Cloud, Dedicated Support, Custom Services, Advanced Security & C

Plan details, limits and sources →

Pricing Plans 1

Published pricing tiers and plan structures.

Datapad Plans Pricing

4 plans

PLANS

Security Posture 3

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

Datapad Domain Security

TLSv1.3 · HSTS · DMARC

SECURITY

Datapad Vulnerability Disclosure

no policy recorded

SECURITY

Datapad Trust Center

SOC 2, GDPR

SECURITY

Resources

Get Started 2

Portal, sign-up, and the first successful call

SignUp

SignUp

Login

Documentation 1

Reference material describing how the API behaves

Documentation

Documentation

Agent Surfaces 1

MCP servers, agent skills, and machine-readable catalogs

LLMsTxt

LLMsTxt

Design & Contract 1

Pagination, idempotency, versioning, errors, and events

Conformance

Conformance

Build 1

SDKs, sample code, and the tooling you integrate with

GitHubOrganization

GitHubOrganization

Access & Security 5

Authentication, authorization, and security posture

TrustCenter

TrustCenter

VulnerabilityDisclosure

VulnerabilityDisclosure

DomainSecurity

DomainSecurity

Security

Security

Compliance

Compliance

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: datapad
name: Datapad
description: Datapad is an autonomous AI data analyst platform that lets business teams analyze their data by asking questions
  in natural language. It connects to 50+ data sources — SQL databases, BigQuery, Snowflake, Google Ads, Facebook Ads, Shopify,
  HubSpot and more — and uses Text2SQL and Python code generation (powered by models such as Claude and ChatGPT) to turn questions
  into queries, dashboards, and insights, with a Slack bot for conversational analytics. Datapad is a San Francisco company
  backed by a16z. It is an end-user SaaS product and does not currently publish a public developer REST/GraphQL API, SDKs,
  or webhook surface; this profile captures its identity, security, and compliance posture.
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: low
  source:
  - plans
  - security
  generated: '2026-11-06'
  method: derived
image: https://datapad.io/images/og-image.png
url: https://raw.githubusercontent.com/api-evangelist/datapad/refs/heads/main/apis.yml
x-type: company
x-source: vc-portfolio
x-backed-by:
- a16z
x-tier: stub
x-tier-reason: portfolio-lead
specificationVersion: '0.23'
created: '2026-07-17'
modified: '2026-07-18'
tags:
- Company
- Analytics
- Artificial Intelligence
- Data
- Business Intelligence
- Natural-Language
- Dashboards
- Software-as-a-Service
tags_raw:
- Company
- Analytics
- Artificial Intelligence
- Data
- Business Intelligence
- Natural Language
- Dashboards
- SaaS
apis: []
common:
- type: Plans
  url: plans/datapad-plans-pricing.yml
- type: TrustCenter
  url: security/datapad-trust-center.yml
- type: VulnerabilityDisclosure
  url: security/datapad-vulnerability-disclosure.yml
- type: DomainSecurity
  url: security/datapad-domain-security.yml
- type: Security
  url: https://datapad.io/security
- type: Website
  url: https://datapad.io
- type: Documentation
  url: https://datapad.io/docs
- type: Blog
  url: https://datapad.io/blog
- type: Pricing
  url: https://datapad.io/pricing
- type: SignUp
  url: https://app.datapad.io
- type: Login
  url: https://app.datapad.io
- type: TermsOfService
  url: https://datapad.io/terms-of-service
- type: PrivacyPolicy
  url: https://datapad.io/privacy-policy
- type: Support
  url: mailto:hello@datapad.io
- type: GitHubOrganization
  url: https://github.com/datapad
- type: Integrations
  url: https://datapad.io/integrations
- type: Compliance
  url: https://datapad.io/security
- type: Conformance
  url: conformance/datapad-conformance.yml
- type: LLMsTxt
  url: llms/datapad-llms.txt
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
- FN: APIs.json
  email: info@apis.io
x-enrichment:
  date: '2026-07-19'
  status: backfilled
  pass: local-v1
  note: backfilled from .gitignore signal + verified work evidence

Datapad API — frequently asked questions

How much does the Datapad API cost?

Datapad publishes its prices; its Basic plan is $30 as of 2026-10-07. APIs.io lists 4 pricing plans for Datapad, with each plan's price, limits and inclusions under Pricing on this page.

Is the Datapad API ready for AI agents?

Datapad has a Kin Score of 33.0 out of 100 (thin) and an Agent Readiness score of 0.0 (human-only), scored by API Evangelist on 2026-11-06.

Work with this as data

Every provider here is available over the APIs.io API and to AI agents over MCP.

MCP server

One button, every client — Claude, Cursor, VS Code and the rest.

https://apis.io/mcp

Tools for providers

9 MCP tools reach this

find_providersBrowse and filter every provider in the catalog.
get_provider_artifactsEvery artifact this provider publishes, grouped by type.
get_provider_operationsEvery operation across all of their OpenAPIs — one call instead of parsing every spec.
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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Every operation they expose

curl "https://apis.io/api/v1/providers/datapad/operations?limit=25"

How their score was established

curl "https://apis.io/api/v1/providers/datapad/evidence"

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