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 12 more developer resources.

🌐 Visit website 📡 Source on GitHub

23.9/100 emerging ▬ flat Agent 0/100 human only Full breakdown ↓
scored 2026-07-27 · rubric v0.5

0 APIs

CompanyAnalyticsArtificial IntelligenceDataBusiness IntelligenceNatural LanguageDashboardsSaaS

Kin Score

Kin Score How this is scored →
scored 2026-07-27 · rubric v0.5

Composite quality — 23.9/100 · emerging

Contract Quality 0.0 / 25

Developer Ergonomics 3.0 / 20

Commercial Clarity 12.1 / 20

Operational Transparency 2.1 / 13

Governance 0.0 / 12

Discoverability 6.8 / 10

Agent readiness — 0/100 · human only

Machine-Readable Contract 0 / 18

Agentic Access Contract 0 / 15

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

Improve this rating by publishing the missing artifacts — every area above can be raised, and the full rubric is at apis.io/rating/. This rating is computed from github.com/api-evangelist/datapad: open an issue to ask a question, or submit a pull request to add artifacts. Want it done for you? Prioritized profiling — $2,500 →

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 3

Pricing, plans, and the legal terms of use

Pricing

Pricing

TermsOfService

TermsOfService

PrivacyPolicy

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.
accessModel:
  pricing: unknown
  onboarding: unknown
  trial: false
  try_now: false
  public: false
  label: Unknown
  confidence: low
  source: []
  generated: '2026-07-22'
  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: '0.20'
created: '2026-07-17'
modified: '2026-07-18'
tags:
- Company
- Analytics
- Artificial Intelligence
- Data
- Business Intelligence
- Natural Language
- Dashboards
- SaaS
apis: []
common:
- 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