Applied Compute

Applied Compute is a San Francisco AI company founded in 2025 by former OpenAI researchers, building "Specific Intelligence" for the enterprise. It operates a cloud platform for training, inference, and continuous improvement of custom, open AI models and long-horizon, tool-using agents grounded in a company's own proprietary data. Rather than selling a generic model, Applied Compute embeds engineers with customer teams to fine-tune models, mine production data, run online/reinforcement learning, and deploy autonomous agents that operate inside the customer's environment with observability over rollouts. The company emerged from stealth in October 2025 and is backed by Kleiner Perkins, Benchmark, Sequoia Capital, Lux Capital, Greenoaks, Neo, Elad Gil, and others at a reported ~$1.3B valuation. Named customers include DoorDash, Cognition, Harvey, and Mercor.

Applied Compute is profiled on the APIs.io network. Tagged areas include Company, Artificial Intelligence, Machine Learning, LLM, and Model Training.

Applied Compute’s developer surface includes documentation, engineering blog, and 9 more developer resources.

🌐 Visit website 📡 Source on GitHub

15.6/100 emerging ▬ flat Agent 0/100 human only Full breakdown ↓
scored 2026-07-27 · rubric v0.5

0 APIs

CompanyArtificial IntelligenceMachine LearningLLMModel TrainingInferenceAI AgentsEnterprise AIFine-TuningReinforcement Learning

Kin Score

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

Composite quality — 15.6/100 · emerging

Contract Quality 0.0 / 25

Developer Ergonomics 3.9 / 20

Commercial Clarity 4.2 / 20

Operational Transparency 0.7 / 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/applied-compute: 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 1

Portal, sign-up, and the first successful call

DeveloperPortal

DeveloperPortal

Documentation 1

Reference material describing how the API behaves

Documentation

Documentation

Build 1

SDKs, sample code, and the tooling you integrate with

GitHubOrganization

GitHubOrganization

Access & Security 1

Authentication, authorization, and security posture

DomainSecurity

DomainSecurity

Commercial 2

Pricing, plans, and the legal terms of use

TermsOfService

TermsOfService

PrivacyPolicy

PrivacyPolicy

Company 5

The organization behind the API

Website

Website

Blog

Blog

Careers

Careers

LinkedIn

Twitter

Twitter

Source (apis.yml)

aid: applied-compute
name: Applied Compute
description: Applied Compute is a San Francisco AI company founded in 2025 by former OpenAI researchers, building "Specific
  Intelligence" for the enterprise. It operates a cloud platform for training, inference, and continuous improvement of custom,
  open AI models and long-horizon, tool-using agents grounded in a company's own proprietary data. Rather than selling a generic
  model, Applied Compute embeds engineers with customer teams to fine-tune models, mine production data, run online/reinforcement
  learning, and deploy autonomous agents that operate inside the customer's environment with observability over rollouts.
  The company emerged from stealth in October 2025 and is backed by Kleiner Perkins, Benchmark, Sequoia Capital, Lux Capital,
  Greenoaks, Neo, Elad Gil, and others at a reported ~$1.3B valuation. Named customers include DoorDash, Cognition, Harvey,
  and Mercor.
url: https://raw.githubusercontent.com/api-evangelist/applied-compute/refs/heads/main/apis.yml
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://mintcdn.com/appliedcompute/pJSHZ0UHJbHRexgo/logo/light.svg
x-type: company
x-source: kleiner-perkins-portfolio
x-tier: stub
x-tier-reason: portfolio-lead
specificationVersion: '0.20'
created: '2026-07-17'
modified: '2026-07-17'
tags:
- Company
- Artificial Intelligence
- Machine Learning
- LLM
- Model Training
- Inference
- AI Agents
- Enterprise AI
- Fine-Tuning
- Reinforcement Learning
apis: []
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
- FN: APIs.json
  email: info@apis.io
common:
- type: DomainSecurity
  url: security/applied-compute-domain-security.yml
- type: Website
  url: https://www.appliedcompute.com
- type: DeveloperPortal
  url: https://docs.appliedcompute.com
- type: Documentation
  url: https://docs.appliedcompute.com
- type: Blog
  url: https://www.appliedcompute.com/blog
- type: GitHubOrganization
  url: https://github.com/Applied-Compute
- type: TermsOfService
  url: https://www.appliedcompute.com/legals/terms-of-service
- type: PrivacyPolicy
  url: https://www.appliedcompute.com/legals/privacy-policy
- type: Careers
  url: https://jobs.ashbyhq.com/Applied%20Compute
- type: LinkedIn
  url: https://www.linkedin.com/company/appliedcompute
- type: Twitter
  url: https://x.com/appliedcompute
x-enrichment:
  date: '2026-07-19'
  status: backfilled
  pass: local-v1
  note: backfilled from .gitignore signal + verified work evidence