MosaicML website screenshot

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 Fine-Tuning.

MosaicML’s developer surface includes getting-started guide, CLI, changelog, documentation, GitHub presence, code examples, engineering blog, and 28 more developer resources.

20.9/100 emerging ▬ flat Agent 1/100 human only saas Full breakdown ↓
scored 2026-09-08 · rubric v0.20.0
0 APIs
Artificial IntelligenceFoundation ModelsModel TrainingPretrainingFine-TuningLLMGenerative AIPyTorchDistributed TrainingGPUDatabricksDBRXComposerStreamingLLM Foundry

Kin Score

Kin Score Kin Score How this is scored →
scored 2026-09-08 · rubric v0.20.0
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.
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 github.com/api-evangelist/mosaicml: open an issue to ask a question, or submit a pull request to add artifacts. Submit an artifact on GitHub — free → Manage your own listing — the Influence plan, $499/mo →

Security Posture 2

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

Mosaicml Domain Security

TLSv1.3 · HSTS · DNSSEC · DMARC

SECURITY

Mosaicml Vulnerability Disclosure

security.txt · contact published

SECURITY

Resources

Get Started 2

Portal, sign-up, and the first successful call

Documentation 2

Reference material describing how the API behaves

Agent Surfaces 3

MCP servers, agent skills, and machine-readable catalogs

Build 8

SDKs, sample code, and the tooling you integrate with

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Access & Security 2

Authentication, authorization, and security posture

Learn 1

Tutorials, courses, talks, and written guidance

Operate 3

Status, limits, changes, and where to get help

Commercial 2

Pricing, plans, and the legal terms of use

Company 5

The organization behind the API

Other 7

Properties that don't map to a standard resource type

Scroll for all 7

Source (apis.yml)

apis.yml Raw ↑
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
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: unknown
  onboarding: unknown
  trial: false
  try_now: false
  public: false
  label: Unknown
  confidence: low
  source: []
  generated: '2026-07-22'
  method: derived
position: Producing
access: 3rd-Party
image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/icons/mosaicml.png
tags:
- Artificial Intelligence
- Foundation Models
- Model Training
- Pretraining
- Fine-Tuning
- LLM
- Generative AI
- PyTorch
- Distributed Training
- GPU
- Databricks
- DBRX
- Composer
- Streaming
- LLM Foundry
tags_raw:
- 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: '0.23'
apis: []
common:
- type: IssueTracker
  url: https://github.com/mosaicml/composer/issues
- type: Releases
  url: https://github.com/mosaicml/composer/releases
- type: CodeOfConduct
  url: https://github.com/mosaicml/composer/blob/main/CODE_OF_CONDUCT.md
- type: ContributionGuide
  url: https://github.com/mosaicml/composer/blob/main/CONTRIBUTING.md
- type: License
  url: https://github.com/mosaicml/composer/blob/main/LICENSE
- type: DeveloperPortal
  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: SourceCode
  url: https://github.com/mosaicml/composer
- type: Packages
  url: packages/mosaicml-packages.yml
- type: CLI
  url: cli/mosaicml-cli.yml
- type: ChangeLog
  url: changelog/mosaicml-changelog.yml
- type: LLMsTxt
  url: llms/mosaicml-llms.txt
- type: WellKnown
  url: well-known/mosaicml-well-known.yml
- 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
x-parent: databricks
x-relationship: acquisition
x-acquired: 2023

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