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Side-by-side features, use cases and pricing — because the right pick depends on your job and budget, not just the ranking.
⇄ Comparison dimension — pick the market you're actually shopping in
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Weights & Biases
✓ verifiedFreemium
Weights & Biases is a widely used MLOps platform for experiment tracking, model management and evaluating AI applications.
2.5M visits/mo
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LangWatch
✓ verifiedFreemium
Platform to test, evaluate and observe LLM and voice AI agents, with prompt management and red-teaming for production.
23K visits/mo6.4K saves
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Openlit
✓ verifiedFreemium
Open-source, OpenTelemetry-native platform for LLM observability, tracing, evaluation and prompt management.
9.1K visits/mo
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metaflow.org
✓ verifiedFree
Open-source Python framework, born at Netflix, for building, scaling, and deploying real-world ML, AI, and data science workflows.
20K visits/mo
Pricing
No public pricing
Developer: €0 (50k events/mo)
Growth: €29/core-seat/mo (+ €5 per 100k events)
Self-Hosted: $0 (Apache 2.0, no usage limits)
No public pricing
Core features
- ✦Experiment tracking and visualization for ML training runs
- ✦Model and artifact versioning and management
- ✦Hyperparameter optimization tooling
- ✦Collaborative dashboards and reports for ML teams
- ✦LLM application tracing and evaluation tooling
- ✦Scenario-based agent testing
- ✦LLM evaluation and quality scoring
- ✦Observability for cost and latency
- ✦Prompt management with GitHub sync
- ✦Voice AI simulation
- ✦LLM red-teaming and governance
- ✦OpenTelemetry-native distributed tracing
- ✦Token usage and cost tracking
- ✦LLM evaluations (online/offline)
- ✦Prompt management and versioning
- ✦GPU and vector-DB monitoring
- ✦60+ LLM/framework integrations
- ✦Self-hostable via Docker; export to Grafana/Datadog
- ✦Plain-Python workflow orchestration
- ✦Automatic versioning and experiment tracking
- ✦Scale-out compute with GPUs and parallel instances
- ✦One-command deployment to production
- ✦Runs on AWS, Azure, GCP, or Kubernetes
- ✦Event-based triggering of workflows
Use cases
- →ML engineers tracking and comparing training experiments
- →Research teams versioning datasets and model checkpoints
- →Teams building and evaluating LLM-powered applications
- →Organizations collaborating on machine learning projects
- →Catch agent issues before production
- →Evaluate and monitor LLM quality
- →Test voice AI agents at scale
- →Trace and debug LLM applications
- →Monitor AI cost and performance
- →Evaluate prompts and models
- →Add observability without code changes
- →Developing and debugging ML pipelines locally
- →Scaling model training to cloud GPUs
- →Deploying experiments to production unchanged
- →Building reactive, event-driven data systems
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