MLflow
An open-source platform for managing the full lifecycle of AI models, agents, and language models from development to production
- Vendor
- Databricks, Inc.
- Also known as
- mlflow
What is MLflow?
A comprehensive, open-source platform that provides experiment tracking, model registry, LLM tracing, prompt management, and deployment capabilities across the complete machine learning and AI lifecycle
MLflow pricing
We don't have MLflow's full plan breakdown yet (its pricing page resisted automated reading). Here's what we could confirm. Always check live pricing for exact numbers.
What MLflow does
The capabilities that matter for mlops & llmops tools, normalised so it lines up with every alternative. “-” means we haven't confirmed it, not that it's missing.
- Tool role
- End-to-end ML platform
- Self-hostable / OSS core
- ✓
- Managed cloud available
- ✓
- On-prem / VPC deployment
- ✓
- LLM tracing / observability
- ✓
- Evaluation (offline / LLM-judge / human)
- ✓
- Prompt management + versioning
- ✓
- Experiment tracking / model registry
- ✓
- Model serving / inference endpoint
- ✓
- OpenTelemetry / OpenLLMetry compatible
- ✓
- Framework-agnostic
- ✓
- Multi-provider model support
- ✓
- No-train-on-customer-data guarantee
- -
Platform & deployment
Independently observed- CLI
- Web
- Cloud / SaaS
- Self-hosted
Integrations (28)
Independently observed- Databricks
- AWS S3
- Google Cloud Storage
- Azure Storage
- AzureML
- Kubernetes
- LangChain
- Pydantic AI
- Anthropic Claude
- OpenAI
- Google Gemini
- SAP AI Core
- JFrog
- Aliyun
- PostgreSQL
- MySQL
- MSSQL
- Claude Code
- OpenAI Codex
- Gemini
- Anthropic
- Ollama
- OpenClaw
- Qwen Code
- LiteLLM
- Amazon S3
- OpenTelemetry/OTLP
- OpenAI-compatible endpoints
Security & compliance
Known vulnerabilities: 82 (29 in the last 12 months), max severity CRITICAL sourcea count reflects scale & disclosure, not quality
MLflow FAQ
Common questions about MLflow, answered from independent, dated evidence.
What is MLflow?
A comprehensive, open-source platform that provides experiment tracking, model registry, LLM tracing, prompt management, and deployment capabilities across the complete machine learning and AI lifecycle It is indexed under MLOps & LLMOps Tools.
Source: https://mlflow.org
Is MLflow free to use?
MLflow is open source, so it can be self-hosted and used at no licence cost. It is released under the Apache-2.0 licence. Pricing changes often, so verify at source before relying on it.
Source: https://mlflow.org
What platforms does MLflow support?
MLflow supports the web and a command-line interface. Platforms we have not confirmed are simply not listed here rather than ruled out.
Source: https://mlflow.org
Can MLflow be self-hosted?
Yes. MLflow can be deployed cloud / SaaS and self-hosted, so it does not have to run on the vendor's infrastructure.
Source: https://mlflow.org
What does MLflow integrate with?
We have confirmed 24 integrations for MLflow, including Databricks, AWS S3, Google Cloud Storage, Azure Storage, AzureML, Kubernetes, LangChain and Pydantic AI, plus 16 more. This is what we could verify from public sources, so the vendor may support others we have not indexed.
Source: https://mlflow.org
Is MLflow open source?
Yes. MLflow is published under the Apache-2.0 licence, a permissive licence that generally allows commercial use and modification. Licence terms can change between releases, so verify against the repository for the version you intend to use.
Source: https://github.com/mlflow/mlflow
MLflow alternatives
Other mlops & llmops tools we track, ranked by the same independent score.
- PortkeyAn API gateway for routing requests across thousands of language models with built-in safety guardrailshigh · 76%
- LangChainA platform for building, testing, and operating AI agents at scalemedium · 70%
- BasetenAn inference platform for deploying and running AI models at scalemedium · 73%
- Together AICloud platform for deploying and running open-source AI models with optimized inferencemedium · 61%
- OllamaA platform for running open-source language models locally or in the cloud with cost-effective access.medium · 57%
- RayDistributed computing infrastructure for scaling machine learning applicationsmedium · 65%
Compare MLflow
Side by side against other mlops & llmops tools, attribute by attribute, with a source on every value.
The vioscaleAI score: one lens on the evidence
Not user reviews and not a paid placement: a confidence-weighted blend of the independent signals below (adoption, activity, security posture, and more), which you can sort and re-weight yourself. Vendors can correct their listing but can never move their rank, and stars are weighted low as a vanity metric. It is one way to read the evidence for MLflow, not the verdict.
| Signal | Score | Weight | Contribution | Evidence |
|---|---|---|---|---|
| Package downloads | 91 | 0.14 | 12.4 | ✓ |
| Capabilities | 100 | 0.07 | 7.0 | ✓ |
| Development activity | 63 | 0.09 | 5.9 | ✓ |
| Release cadence | 94 | 0.05 | 4.9 | ✓ |
| Dependent projects | 62 | 0.06 | 3.9 | ✓ |
| Integrations | 31 | 0.08 | 2.4 | ✓ |
| Security score | 55 | 0.04 | 2.3 | ✓ |
| Stars | 84 | 0.03 | 2.2 | ✓ |
| Security posture | 0 | 0.06 | 0.0 | - |
| Developer Q&A activity | 0 | 0.06 | 0.0 | - |
Computed . Re-weight it by intent, or see the full method.
All data & sourcesshow ↓
Every value we hold, with its source, retrieval date, and confidence. This is the evidence behind the score: don't trust it, verify it.
Activity
| Attribute | Value | Evidence |
|---|---|---|
| Commits last 30d | 100 | mediumsource · 2026-09-07 · 65% |
Adoption
Content
| Attribute | Value | Evidence |
|---|---|---|
| Faq | 6 items | mediumsource · 2026-09-10 · 66% |
Features
| Attribute | Value | Evidence |
|---|---|---|
| Capabilities | Role: platform · Evaluation: Yes · Open source: Yes · Managed cloud: Yes · Model serving: Yes · Pricing model: free_open_source | mediumsource · 2026-09-07 · 60% |
Integrations
| Attribute | Value | Evidence |
|---|---|---|
| Count | 11 | mediumsource · 2026-09-07 · 60% |
Language
| Attribute | Value | Evidence |
|---|---|---|
| Primary | Python | highsource · 2026-09-07 · 90% |
License
| Attribute | Value | Evidence |
|---|---|---|
| Spdx | Apache-2.0 | highsource · 2026-09-07 · 95% |
Pricing
Release
Security
| Attribute | Value | Evidence |
|---|---|---|
| Trust center | https://mlflow.org/articles/tags/ai-compliance-policies/ | mediumsource · 2026-09-07 · 60% |
| Scorecard | 5.5 | highsource · 2026-09-07 · 90% |
| Vulnerabilities | Count: 82 · Source: https://advisories.ecosyste.ms/api/v1/advisories?ecosystem=pypi&package_name=mlflow&per_page=100 · Last 12m: 29 · Max severity: CRITICAL | highsource · 2026-09-07 · 90% |
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