ML/LLMOps platform
MLflow is an open-source platform for managing the ML and LLM lifecycle, including experiment tracking, model registry/deployment, and LLM observability/evaluation/prompt management.
Same problem, same approach. Swapping one for another is a config change, not a rewrite.
Choose Opik when you want a dedicated LLM/RAG tracing and automated-eval platform with dashboards, and can skip MLflow's classic ML model registry.
Debug, evaluate, and monitor your LLM applications, RAG systems, and agentic workflows with comprehensive tracing, automated evaluations, and production-ready dashboards.
Pick Phoenix when you want a Python AI observability/eval platform that runs well in notebooks, and don't need MLflow's full ML lifecycle.
AI Observability & Evaluation
Choose Langfuse when you need dedicated LLM/agent tracing, evals, and prompt management, and can forgo MLflow's classic ML experiment tracking and registry.
🪢 Open source AI engineering platform: LLM evals, observability, metrics, prompt management, playground, datasets. Integrates with OpenTelemetry, LangChain, OpenAI SDK, LiteLLM, and more. 🍊YC W23
Solves the same problem with a different architecture or at a different layer. Expect to rewrite the integration.
Choose it for general OTel-native logs/metrics/traces across your whole stack; it lacks ML experiment tracking, model registry, and prompt/eval tooling.
SigNoz is an open-source, OpenTelemetry-native observability platform for your team and their AI agents. Get logs, metrics, and traces in one tool with features like APM, distributed tracing, log management, infra monitoring, etc. Combined with SigNoz MCP and a native AI teammate (in SigNoz Cloud) it helps you build more resilient apps.
Use it for a Rust-based LLM gateway with bundled observability/evaluation/optimization, accepting an archived repo and no classic ML tracking.
TensorZero is an open-source LLMOps platform that unifies an LLM gateway, observability, evaluation, optimization, and experimentation.
Use it if you want proxy-based LLM observability/gateway with one-line integration, trading away ML experiment tracking and model registry.
🧊 Open source LLM observability platform. One line of code to monitor, evaluate, and experiment. YC W23 🍓
Pick it for a Go-based agent development/debugging/evaluation lifecycle, not for MLflow's Python ML tracking and model deployment breadth.
Next-generation AI Agent Optimization Platform: Cozeloop addresses challenges in AI agent development by providing full-lifecycle management capabilities from development, debugging, and evaluation to monitoring.
Choose it when LLM evaluation and agent testing are the priority and you can use a TypeScript platform instead of MLflow's full lifecycle.
The platform for LLM evaluations and AI agent testing
Pick it for an agent-specific observability platform with a TypeScript stack, accepting a narrower scope than MLflow's ML+LLM lifecycle.
Laminar - open-source observability platform purpose-built for AI agents. YC S24.
Choose it for OpenTelemetry-native AI agent tracing/evaluation without MLflow's model registry, experiment tracking, and deployment features.
Open source platform for AI Engineering: OpenTelemetry-native LLM Observability, GPU Monitoring, Guardrails, Evaluations, Prompt Management, Vault, Playground. 🚀💻 Integrates with 50+ LLM Providers, VectorDBs, Agent Frameworks and GPUs.
Choose it for pipeline-first ML/LLM orchestration; it is complementary to a tracker/registry rather than a drop-in MLflow replacement.
ZenML 🙏: One AI Platform from Pipelines to Agents. https://zenml.io.
Use it for workflow/DAG orchestration and deployment of ML/LLM pipelines rather than as a drop-in experiment tracker or model registry.
Build, Manage and Deploy AI/ML Systems
Pick it for an MIT-licensed Python library that adds LLM eval/tracking into existing code, not a full ML lifecycle platform.
Evaluation and Tracking for LLM Experiments and AI Agents
Choose it when you specifically need a lightweight LLM API gateway with provider routing, cost tracking, and guardrails, not MLflow's full lifecycle platform.
The fastest, litest AI Gateway. Rust core with Python SDK. Call 100+ LLM APIs in OpenAI (or native) format with cost tracking, guardrails, load balancing, and logging [Bedrock, Azure, OpenAI, Anthropic, OpenAI, VertexAI, vLLM, Nvidia NIM]
An earlier or more limited way to do the job. Still the right call when you need something small, proven, or CPU-only.
Use it if prompt design/versioning/delivery is your main need and you can forgo MLflow's broader experiment tracking and model registry.
🕹️ Open-source, developer-first LLMOps platform designed to streamline prompt design, version management, instant delivery, collaboration, troubleshooting, observability and more.
Use it if you need a lightweight Python SDK for agent tracing/monitoring/eval, accepting a narrower scope and slower release cadence.
Python SDK for Agent AI Observability, Monitoring and Evaluation Framework. Includes features like agent, llm and tools tracing, debugging multi-agentic system, self-hosted dashboard and advanced analytics with timeline and execution graph view
These projects were analysed and named mlflow among their alternatives. The relationship is not symmetric — how mlflow rates them is a separate judgement, made when mlflow is analysed in its own right.
calls mlflow “Choose when your team already uses MLflow and wants LLM tracing/evals inside a broader ML lifecycle platform.”
🪢 Open source AI engineering platform: LLM evals, observability, metrics, prompt management, playground, datasets. Integrates with OpenTelemetry, LangChain, OpenAI SDK, LiteLLM, and more. 🍊YC W23
calls mlflow “Choose if you already run MLflow for ML lifecycle and want its newer LLM tracing/eval modules.”
Debug, evaluate, and monitor your LLM applications, RAG systems, and agentic workflows with comprehensive tracing, automated evaluations, and production-ready dashboards.
calls mlflow “Choose if you already standardize on MLflow for ML lifecycle and want LLM eval/tracking in the same system.”
AI Observability & Evaluation