Compare tools
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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OpenRouter
✓ verifiedFreemium
Unified API gateway that routes requests to 400+ LLMs across 70+ providers with failover and no subscription.
17M visits/mo
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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
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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
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Kiro AI
✓ verifiedFreemium
Kiro is a spec-driven agentic coding tool for IDE, CLI and web that turns prompts into specs and catches bugs with property-based tests.
3.8M visits/mo
Pricing
Free: $0 (free models only, 50 requests/day)
Pay-as-you-go: 5.5% platform fee on inference
GPT-OSS 20B: $0.075 per 1M input tokens ($0.30 per 1M output)
GPT-OSS 120B: $0.15 per 1M input tokens
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
Free: $0/mo (50 credits)
Pro: $20/user/mo (1,000 credits)
Pro+: $40/user/mo (2,000 credits)
Pro Max: $100/user/mo (5,000 credits)
Power: $200/user/mo (10,000 credits)
Core features
- ✦One unified, OpenAI-compatible API for 400+ models
- ✦Automatic provider failover for higher uptime
- ✦Edge routing for low latency
- ✦Custom data and provider policies
- ✦Pay-as-you-go credits usable across any model
- ✦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
- ✦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
- ✦Spec-driven development (requirements, design, tasks)
- ✦Parallel agents, local or cloud
- ✦Property-based and correctness testing
- ✦Works in IDE, CLI, web and mobile
- ✦Multiple models (Claude, open-weight, Auto)
- ✦Headless CLI for CI/CD
- ✦Context from tools like Figma and Terraform
Use cases
- →Accessing many LLMs through one integration
- →Adding provider redundancy to AI apps
- →Comparing model price and performance
- →Powering agents and AI-native products
- →Running LLM inference at high speed
- →Cutting inference costs at scale
- →Powering low-latency AI chat apps
- →Serving models via a hosted API
- →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
- →Turning prompts into maintainable, spec-matched code
- →Catching bugs unit tests miss
- →Reviewing PRs and fixing bugs in CI/CD
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