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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
hCaptcha logo
hCaptcha
✓ verifiedFreemium

Privacy-focused CAPTCHA and bot/fraud-detection service, a drop-in reCAPTCHA alternative for websites and apps.

4.4M visits/mo
Maxim AI logo
Maxim AI
✓ verifiedFreemium

End-to-end evaluation and observability platform for building, testing, and monitoring AI agents and LLM apps.

102K visits/mo
ApX Machine Learning logo
ApX Machine Learning
✓ verifiedFreemium

Tools, model specs and courses for LLM engineers-VRAM calculator, benchmarks and model directory-with free and paid tiers.

355K visits/mo
Pricing

No public pricing

Basic: Free
Pro: $139/month billed monthly, $99/month billed yearly
Enterprise: Contact sales

Free trial available

Developer: $0 (3 seats, 10k logs/mo)
Professional: $29/seat/mo (100k logs/mo)
Business: $49/seat/mo (500k logs/mo)

Free trial available

Basic: $0/mo (free forever)
Pro: $19/mo
Pro+: $59/mo
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
  • AI bot detection
  • Transaction fraud protection
  • Account-takeover (ATO) defense
  • Pull-based SMS MFA
  • Private Learning ML risk models
  • Two-line reCAPTCHA migration
  • Hundreds of integrations
  • Prompt IDE, versioning, and deployment
  • Agent simulation and evaluation
  • Production tracing and observability
  • Pre-built and custom evaluators
  • Human-in-the-loop evaluation
  • Bifrost LLM gateway
  • VRAM/GPU-memory calculator for LLMs
  • LLM performance rankings and benchmarks
  • Model directory and comparison
  • AI/ML courses and learning roadmap
  • Calculator API and exportable cost reports
  • Engineering blog and guides
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
  • Blocking bots and spam signups
  • Preventing account takeover
  • Reducing transaction and payment fraud
  • Stopping credential stuffing
  • Testing and comparing prompts and models
  • Evaluating and simulating AI agents
  • Monitoring agents in production
  • Running human evaluation pipelines
  • Estimating GPU memory before training or inference
  • Comparing and selecting LLMs
  • Learning ML and LLM engineering
  • Modeling production deployment costs
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