MosaicML

MosaicML was a San Francisco-based foundation model training company founded in 2021 by Naveen Rao and Hanlin Tang to make large-scale model training faster and cheaper through algorithmic and systems efficiency. Databricks acquired MosaicML in July 2023 for approximately $1.3 billion and folded the team and platform into Databricks Mosaic AI Research. The MosaicML training platform is now delivered as Databricks Mosaic AI Training (pretraining and finetuning), and the original mosaicml.com domain redirects to the Databricks Mosaic Research microsite. MosaicML's surviving open-source artifacts include Composer (a PyTorch training library with built-in speedup recipes), Streaming (a cloud-native dataset format for efficient distributed training), LLM Foundry (the training code behind Databricks' DBRX foundation model), the Diffusion training stack, and the MCLI command line and Python SDK that orchestrate Pretraining and Finetuning jobs against the managed training service. The Mosaic AI Training service itself is accessed exclusively through the MCLI command line, the mosaicml-cli Python SDK, and Databricks workspace integrations — there is no publicly published REST OpenAPI surface for the training control plane. Commercial access requires a Databricks account; pricing is consumption-based via Databricks DBU billing rather than an independent MosaicML pricing page.

MosaicML is profiled on the APIs.io network. Tagged areas include Artificial Intelligence, Foundation Models, Model Training, Pretraining, and Finetuning.

MosaicML’s developer surface includes documentation, GitHub presence, code examples, engineering blog, pricing, and 16 more developer resources.

🌐 Visit website 📡 Source on GitHub

13.0/100 minimal ▬ flat Agent 7/100 human only Full breakdown ↓
scored 2026-07-27 · rubric v0.5

0 APIs

Artificial IntelligenceFoundation ModelsModel TrainingPretrainingFinetuningLLMGenerative AIPyTorchDistributed TrainingGPUDatabricksDBRXComposerStreamingLLM Foundry

Kin Score

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

Composite quality — 13.0/100 · minimal

Contract Quality 0.0 / 25

Developer Ergonomics 3.5 / 20

Commercial Clarity 2.1 / 20

Operational Transparency 0.7 / 13

Governance 0.0 / 12

Discoverability 6.8 / 10

Agent readiness — 7/100 · 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 7 / 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/mosaicml: open an issue to ask a question, or submit a pull request to add artifacts. Want it done for you? Prioritized profiling — $2,500 →

Documentation 1

Reference material describing how the API behaves

Documentation

Documentation

Agent Surfaces 1

MCP servers, agent skills, and machine-readable catalogs

LLMFoundry

LLMFoundry

Build 3

SDKs, sample code, and the tooling you integrate with

SDKs

SDKs

GitHub

GitHub

Examples

Examples

Access & Security 2

Authentication, authorization, and security posture

VulnerabilityDisclosure

VulnerabilityDisclosure

DomainSecurity

DomainSecurity

Learn 1

Tutorials, courses, talks, and written guidance

PretrainingAPI

PretrainingAPI

Commercial 1

Pricing, plans, and the legal terms of use

Pricing

Pricing

Company 5

The organization behind the API

Website

LegacyWebsite

Blog

Twitter

LinkedIn

Other 7

Properties that don't map to a standard resource type

Product

FinetuningAPI

Composer

Streaming

ComposerRepo

StreamingRepo

Acquisition

Scroll for all 7

Source (apis.yml)

aid: mosaicml
name: MosaicML
description: MosaicML was a San Francisco-based foundation model training company founded in 2021 by Naveen Rao and Hanlin
  Tang to make large-scale model training faster and cheaper through algorithmic and systems efficiency. Databricks acquired
  MosaicML in July 2023 for approximately $1.3 billion and folded the team and platform into Databricks Mosaic AI Research.
  The MosaicML training platform is now delivered as Databricks Mosaic AI Training (pretraining and finetuning), and the original
  mosaicml.com domain redirects to the Databricks Mosaic Research microsite. MosaicML's surviving open-source artifacts include
  Composer (a PyTorch training library with built-in speedup recipes), Streaming (a cloud-native dataset format for efficient
  distributed training), LLM Foundry (the training code behind Databricks' DBRX foundation model), the Diffusion training
  stack, and the MCLI command line and Python SDK that orchestrate Pretraining and Finetuning jobs against the managed training
  service. The Mosaic AI Training service itself is accessed exclusively through the MCLI command line, the mosaicml-cli Python
  SDK, and Databricks workspace integrations — there is no publicly published REST OpenAPI surface for the training control
  plane. Commercial access requires a Databricks account; pricing is consumption-based via Databricks DBU billing rather than
  an independent MosaicML pricing page.
type: Index
accessModel:
  pricing: unknown
  onboarding: unknown
  trial: false
  try_now: false
  public: false
  label: Unknown
  confidence: low
  source: []
  generated: '2026-07-22'
  method: derived
position: Provider
access: 3rd-Party
image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/icons/mosaicml.png
tags:
- Artificial Intelligence
- Foundation Models
- Model Training
- Pretraining
- Finetuning
- LLM
- Generative AI
- PyTorch
- Distributed Training
- GPU
- Databricks
- DBRX
- Composer
- Streaming
- LLM Foundry
url: https://raw.githubusercontent.com/api-evangelist/mosaicml/refs/heads/main/apis.yml
created: '2026-05-25'
modified: '2026-05-25'
specificationVersion: '0.20'
apis: []
common:
- type: VulnerabilityDisclosure
  url: security/mosaicml-vulnerability-disclosure.yml
- type: DomainSecurity
  url: security/mosaicml-domain-security.yml
- type: Website
  url: https://www.databricks.com/research/mosaic
- type: LegacyWebsite
  url: https://www.mosaicml.com
- type: Product
  url: https://www.databricks.com/product/machine-learning/mosaic-ai-model-training
- type: Documentation
  url: https://docs.mosaicml.com/en/latest/
- type: SDKs
  url: https://docs.mosaicml.com/projects/mcli/en/latest/
- type: PretrainingAPI
  url: https://docs.mosaicml.com/projects/mcli/en/latest/training/pretraining_api.html
- type: FinetuningAPI
  url: https://docs.mosaicml.com/projects/mcli/en/latest/finetuning/finetuning_api.html
- type: Composer
  url: https://docs.mosaicml.com/projects/composer/en/stable/
- type: Streaming
  url: https://docs.mosaicml.com/projects/streaming/en/stable/
- type: GitHub
  url: https://github.com/mosaicml
- type: LLMFoundry
  url: https://github.com/mosaicml/llm-foundry
- type: ComposerRepo
  url: https://github.com/mosaicml/composer
- type: StreamingRepo
  url: https://github.com/mosaicml/streaming
- type: Examples
  url: https://github.com/mosaicml/examples
- type: Blog
  url: https://www.databricks.com/blog/category/ai/databricks-ai
- type: Acquisition
  url: https://www.databricks.com/blog/2023/06/26/databricks-acquires-mosaicml-leading-generative-ai-platform.html
- type: Pricing
  url: https://www.databricks.com/product/pricing
- type: Twitter
  url: https://twitter.com/DbrxMosaicAI
- type: LinkedIn
  url: https://www.linkedin.com/company/databricks
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com