Mosaic Ml

MosaicML - now Databricks Mosaic Research - is a machine learning systems company that builds open-source tooling for training and deploying large neural networks and generative AI models faster and at lower cost. Its flagship open-source projects include Composer (a PyTorch library of training speedup methods), LLM Foundry (code for training and finetuning large language models), and Streaming (a library for streaming large datasets from cloud object stores into PyTorch training). MosaicML also shipped a managed training and inference platform driven by the mosaicml-cli (mcli) command-line tool; following the 2023 Databricks acquisition that platform is now offered as Databricks Mosaic AI. The company does not publish a standalone public REST/OpenAPI surface - its developer surface is Python packages, a CLI, and its open-source GitHub repositories.

Mosaic Ml is profiled on the APIs.io network. Tagged areas include Company, Machine Learning, Artificial Intelligence, Generative AI, and Large Language Models.

Mosaic Ml’s developer surface includes documentation, getting-started guide, engineering blog, CLI, changelog, and 9 more developer resources.

🌐 Visit website 📡 Source on GitHub
18.2/100 emerging ▬ flat Agent 4/100 human only Full breakdown ↓
scored 2026-07-27 · rubric v0.5
0 APIs
CompanyMachine LearningArtificial IntelligenceGenerative AILarge Language ModelsModel TrainingDeep LearningMLOpsOpen SourcePyTorch

Kin Score

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scored 2026-07-27 · rubric v0.5
Composite quality — 18.2/100 · emerging
Contract Quality 0.0 / 25
Developer Ergonomics 8.7 / 20
Commercial Clarity 0.0 / 20
Operational Transparency 2.7 / 13
Governance 0.0 / 12
Discoverability 6.8 / 10
Agent readiness — 4/100 · 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 4 / 4
Consent & Bot Identity 0 / 3
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Security Posture 1

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

Mosaic Ml Domain Security

TLSv1.3 · HSTS

SECURITY

Resources

Get Started 2

Portal, sign-up, and the first successful call

DeveloperPortal
DeveloperPortal
 GettingStarted
GettingStarted

Documentation 1

Reference material describing how the API behaves

Documentation
Documentation

Agent Surfaces 2

MCP servers, agent skills, and machine-readable catalogs

LLMsTxt
LLMsTxt
 WellKnown
WellKnown

Build 5

SDKs, sample code, and the tooling you integrate with

GitHubOrganization
GitHubOrganization
 SourceCode
SourceCode
 Packages
Packages
 SDKs
SDKs
 CLI
CLI

Access & Security 1

Authentication, authorization, and security posture

DomainSecurity
DomainSecurity

Operate 1

Status, limits, changes, and where to get help

ChangeLog
ChangeLog

Company 2

The organization behind the API

Website
Website
 Blog
Blog

Source (apis.yml)

apis.yml Raw ↑ 
aid: mosaic-ml
name: Mosaic Ml
description: MosaicML - now Databricks Mosaic Research - is a machine learning systems company that builds open-source tooling
  for training and deploying large neural networks and generative AI models faster and at lower cost. Its flagship open-source
  projects include Composer (a PyTorch library of training speedup methods), LLM Foundry (code for training and finetuning
  large language models), and Streaming (a library for streaming large datasets from cloud object stores into PyTorch training).
  MosaicML also shipped a managed training and inference platform driven by the mosaicml-cli (mcli) command-line tool; following
  the 2023 Databricks acquisition that platform is now offered as Databricks Mosaic AI. The company does not publish a standalone
  public REST/OpenAPI surface - its developer surface is Python packages, a CLI, and its open-source GitHub repositories.
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://avatars.githubusercontent.com/u/75143706?v=4
url: https://raw.githubusercontent.com/api-evangelist/mosaic-ml/refs/heads/main/apis.yml
x-type: company
x-source: vc-portfolio
x-backed-by:
- dcvc
x-tier: stub
x-tier-reason: portfolio-lead
specificationVersion: '0.20'
created: '2026-07-17'
modified: '2026-07-20'
tags:
- Company
- Machine Learning
- Artificial Intelligence
- Generative AI
- Large Language Models
- Model Training
- Deep Learning
- MLOps
- Open Source
- PyTorch
apis: []
common:
- type: DomainSecurity
  url: security/mosaic-ml-domain-security.yml
- type: Website
  url: https://www.mosaicml.com
- type: DeveloperPortal
  url: https://docs.mosaicml.com/en/latest/
- type: Documentation
  url: https://docs.mosaicml.com/en/latest/
- type: GettingStarted
  url: https://docs.mosaicml.com/projects/composer/en/latest/getting_started/installation.html
- type: GitHubOrganization
  url: https://github.com/mosaicml
- type: Blog
  url: https://www.databricks.com/research/mosaic
- type: SourceCode
  url: https://github.com/mosaicml/composer
- type: Packages
  url: packages/mosaic-ml-packages.yml
- type: SDKs
  url: packages/mosaic-ml-packages.yml
- type: CLI
  url: cli/mosaic-ml-cli.yml
- type: ChangeLog
  url: changelog/mosaic-ml-changelog.yml
- type: LLMsTxt
  url: llms/mosaic-ml-llms.txt
- type: WellKnown
  url: well-known/mosaic-ml-well-known.yml
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
- FN: APIs.json
  email: info@apis.io
x-enrichment:
  date: '2026-07-20'
  status: enriched
  artifacts_added: 6
  pass: local-v1