Confident AI vs MLflow (2026)
Updated March 27, 2026
vs
Overview
Rating
10.0 / 10
Rating
10.0 / 10
Best For
Developers who run LLM evals in CI with DeepEval and want a hosted platform for tracing, datasets, and online evals
Best For
ML engineers and AI teams, especially those in the Databricks ecosystem
Product Summary
Confident AI is the LLM evaluation and observability platform from the creators of DeepEval. Compare its pricing, features, pros and cons, and alternatives.
Product Summary
MLflow is an open-source AI engineering platform for agents, LLMs, and ML models, with tracing, evals, prompt management, and an AI gateway.
Starting Price
$0
Starting Price
Free
Free Trial
No
Free Trial
No
Free Version
Yes
Free Version
Yes
Website
Website
Key features
Core capabilities each platform advertises.
- DeepEval open-source evaluation framework
- LLM-as-a-judge metrics
- CI/CD regression testing
- LLM tracing and online evals
- OpenTelemetry-based tracing for LLMs and agents
- 50+ built-in eval metrics and LLM judges
- Prompt versioning and automatic prompt optimization
- AI Gateway with routing, rate limiting, and fallbacks
- Agent server for deploying agents on FastAPI
Strengths and tradeoffs
What each tool does well, and the limitations to keep in mind.
Pros
- Open-source DeepEval runs locally without the platform
- Per-organization pricing with unlimited seats from Starter up
- Tracing, evals, and annotation in one workspace
- Red teaming and governance in the same platform on Enterprise
Cons
- No built-in gateway, so routing needs a separate tool
- Free plan limited to two seats, one project, and five test runs a week
- Online evals and annotation queues start at $200/month
- On-prem deployment and HIPAA are Enterprise only
Pros
- Apache 2.0 license with Linux Foundation governance
- Tracing, evals, prompts, and a gateway in one open-source project
- Covers classic ML and GenAI in the same platform
- Managed options on Databricks, SageMaker AI, and Azure ML
- Auth and role-based access built into the open-source server
Cons
- Self-hosting means running the tracking server, database, and artifact store yourself
- Auth and role-based access are opt-in and need configuring on self-hosted servers
- Managed versions bill through a cloud platform's compute rather than a standalone plan
- The broader ML scope can mean more to learn for teams that only need LLM tooling
Confident AI or MLflow — which should you choose?
Choose Confident AI if you wantChoose if you want
- Unit testing LLM applications
- Automated evaluation in CI/CD pipelines
- RAG evaluation with custom metrics
- Regression testing for prompts
Choose MLflow if you wantChoose if you want
- Tracing and evaluating LLM apps and agents
- Versioning and optimizing prompts
- Routing LLM traffic through a self-hosted gateway
- Tracking classic ML experiments alongside GenAI work
- Running AI tooling inside Databricks
Compare Confident AI and MLflow on your own traffic
10KFree traces/mo
1,000+Models
5 minSetup
Respan lets you trace LLM and agent calls across any model or framework, A/B test prompts on production traffic, and route requests across 1,000+ models through one gateway.
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