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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.

Weights & Biases logo
Weights & Biases
✓ verifiedFreemium

Weights & Biases is a widely used MLOps platform for experiment tracking, model management and evaluating AI applications.

2.5M visits/mo
Groq logo
Groq
✓ verifiedFreemium

Fast, low-cost AI inference provider running LLMs on custom LPU chips via GroqCloud's pay-as-you-go API.

3.6M visits/mo
Openlayer logo
Openlayer
✓ verifiedFreemium

AI governance and observability platform with 100+ automated tests and real-time guardrails to evaluate and monitor ML/LLM systems.

24K visits/mo
Pricing

No public pricing

GPT-OSS 20B: $0.075 per 1M input tokens ($0.30 per 1M output)
GPT-OSS 120B: $0.15 per 1M input tokens
Basic: Free (20k inferences/mo, 1 member, 5 projects)
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
  • LPU custom inference hardware
  • GroqCloud tokens-as-a-service API
  • High-speed, low-latency inference
  • Pay-as-you-go token pricing
  • Free API key to start
  • Broad open-model support
  • 100+ automated AI tests
  • Offline evaluation and CI/CD for AI
  • Real-time observability and tracing
  • Guardrails against PII leaks, injection, hallucination
  • Data-quality and drift monitoring
  • Compliance/governance alignment
  • Git, SDK, CLI and REST API integration
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
  • Running LLM inference at high speed
  • Cutting inference costs at scale
  • Powering low-latency AI chat apps
  • Serving models via a hosted API
  • Evaluate models before production
  • Monitor live AI systems for issues
  • Prevent unsafe or non-compliant outputs
  • Catch data drift and quality problems
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