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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.

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
Lightning  AI logo
Lightning AI
✓ verifiedFreemium

Cloud platform from the makers of PyTorch Lightning for building, training and deploying AI in browser-based GPU Studios.

467K visits/mo3.8K saves
MuAPI logo
MuAPI
✓ verifiedPaid

Unified pay-per-generation API for 500+ image, video and audio models like FLUX, Kling and Seedance at low cost.

411K visits/mo
Replicate AI logo
Replicate AI
✓ verifiedPaid

Pay-per-use cloud API to run, fine-tune, and deploy thousands of open-source and proprietary AI models with one line of code.

1.3M visits/mo17K saves
Pricing
Basic: Free
Pro: $139/month billed monthly, $99/month billed yearly
Enterprise: Contact sales

Free trial available

No public pricing

No public pricing

CPU (Small): $0.000025/sec ($0.09/hr)
Nvidia A100 80GB: $0.0014/sec ($5.04/hr)
Nvidia H100: $0.001525/sec ($5.49/hr)

Free trial available

Core features
  • 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
  • Browser-based Lightning Studios with on-demand GPUs
  • PyTorch Lightning training framework
  • Model training, fine-tuning and deployment
  • Collaborative, shareable ML environments
  • Scalable multi-GPU/multi-node compute
  • Single API for 500+ image, video and audio models
  • Pay-per-generation billing with no subscription
  • No charge on failed tasks
  • Workflows, agents and studio tools
  • MCP and CLI integrations, white-label option
  • One-line API calls to run community and proprietary AI models
  • Support for image, video, speech, and LLM generation models
  • Fine-tuning and custom model deployment via Cog
  • Per-second usage billing on shared or dedicated hardware
  • Automatic scaling for high-traffic private models
  • Thousands of community-published models with production APIs
Use cases
  • Blocking bots and spam signups
  • Preventing account takeover
  • Reducing transaction and payment fraud
  • Stopping credential stuffing
  • Prototype and train ML models in the cloud
  • Fine-tune and deploy foundation models
  • Run reproducible AI experiments collaboratively
  • Building apps on top of many generative models via one API
  • Generating images, video and audio at scale
  • Cutting model API costs versus direct providers
  • Deploying white-label AI generation studios
  • Developers embedding image/video/speech generation into an app via API
  • Teams deploying and scaling their own fine-tuned models
  • Builders comparing outputs from multiple AI models in one playground
  • Companies avoiding GPU infrastructure management for ML inference
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