MLflow
Open Source AI Platform for Agents, LLMs & Models
- Also known as
- mlflow
What is MLflow?
Open source platform for debugging, evaluating, monitoring, and optimizing AI agents and LLM applications with production-grade tracing, evaluation, prompt management, and experiment tracking. Also supports full machine learning lifecycle management including model training and deployment.
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
- Agent / RAG framework
- 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
- Self-hosted
Integrations (3)
Independently observed- OpenTelemetry
- LLM providers (any)
- Agent frameworks (any)
MLflow alternatives
Other mlops & llmops tools we track, ranked by the same independent score.
- OllamaThe easiest way to build with open modelslow · 22%
- OllamaThe easiest way to build with open modelslow · 45%
- BasetenInference is everythinglow · 47%
- PortkeyProduction Stack for Gen AI Builderslow · 46%
- Weights & BiasesThe AI developer platformlow · 25%
- LangChainObserve, Evaluate, and Deploy Reliable AI Agentsmedium · 55%
The Vioscale 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 |
|---|---|---|---|---|
| Capabilities | 100 | 32.00 | 3200.0 | ✓ |
| Package Downloads | 91 | 26.00 | 2354.3 | ✓ |
| Pricing Transparency | 80 | 16.00 | 1280.0 | ✓ |
| Github Activity | 63 | 18.00 | 1135.8 | ✓ |
| Price Level | 100 | 10.00 | 1000.0 | ✓ |
| Release Cadence | 94 | 10.00 | 944.4 | ✓ |
| Github Stars | 84 | 5.00 | 418.5 | ✓ |
| Integrations | 17 | 18.00 | 311.7 | ✓ |
| Reliability | 0 | 12.00 | 0.0 | - |
| Security Posture | 0 | 12.00 | 0.0 | - |
| Stackoverflow Activity | 0 | 12.00 | 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 · 2026-08-01 · 65% |
Adoption
Features
| Attribute | Value | Evidence |
|---|---|---|
| Capabilities | {"role":"framework","evaluation":true,"managed_cloud":false,"model_serving":true,"self_hostable":true,"multi_provider":true,"vpc_deployment":true,"otel_compatible":true,"llm_observability":true,"prompt_management":true,"framework_agnostic":true,"experiment_tracking":true} | mediumsource · 2026-08-01 · 60% |
Integrations
| Attribute | Value | Evidence |
|---|---|---|
| Count | 3 | mediumsource · 2026-08-01 · 60% |
Language
| Attribute | Value | Evidence |
|---|---|---|
| Primary | Python | highsource · 2026-08-01 · 98% |
License
| Attribute | Value | Evidence |
|---|---|---|
| Spdx | Apache-2.0 | highsource · 2026-08-01 · 99% |
Pricing
Release
| Attribute | Value | Evidence |
|---|---|---|
| Cadence days | 10 | mediumsource · 2026-08-01 · 70% |