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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.

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CometAPI logo
CometAPI
✓ verifiedPaid

Unified API to 500+ AI models (OpenAI, Anthropic, Google, etc.) with OpenAI-compatible calls priced ~20% below official rates.

363K visits/mo4.8K saves
Modal logo
Modal
✓ verifiedFreemium

Serverless AI cloud for running inference, training and sandboxes on GPUs with fast cold starts and pay-per-use billing.

988K 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
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
Pricing
Pay-as-you-go, min top-up $10
GPT-5.6: $4 / 1M tokens
Claude Sonnet 5: $1.6 / 1M tokens

Free trial available

Starter: $0/mo + compute ($30 free credit)
Team: $250/mo + compute
Basic: Free
Pro: $139/month billed monthly, $99/month billed yearly
Enterprise: Contact sales

Free trial available

No public pricing

Core features
  • One key for 500+ models
  • OpenAI-compatible API
  • Pay-as-you-go credits (~20% below list)
  • Multimodal: text, image, video, audio
  • Usage analytics and budget alerts
  • Integrations (Claude Code, n8n, Zapier, etc.)
  • Serverless GPU compute defined in Python
  • Sub-second container cold starts
  • Autoscale 0 to 1000+ GPUs
  • Inference, training and batch workloads
  • Secure sandboxes for untrusted code
  • Built-in logging and observability
  • 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
  • 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
Use cases
  • Consolidating multi-provider AI billing
  • Switching models without re-integration
  • Powering apps and automation pipelines
  • Benchmarking models in one playground
  • Deploying and scaling model inference
  • Fine-tuning and training models
  • Running batch/parallel AI jobs
  • Executing untrusted code in sandboxes
  • Blocking bots and spam signups
  • Preventing account takeover
  • Reducing transaction and payment fraud
  • Stopping credential stuffing
  • 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
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