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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.

WaveSpeedAI logo
WaveSpeedAI
✓ verifiedFree trial

Pay-per-use API hub aggregating 1000+ image, video, and audio generation models for developers building AI media pipelines.

2.2M visits/mo
Vast ai logo
Vast ai
✓ verifiedPaid

GPU rental marketplace with per-second billing across thousands of GPUs, aimed at AI training, inference, and fine-tuning workloads.

1.4M visits/mo
Helicone logo
Helicone
✓ verifiedFreemium

LLM observability platform and AI gateway that lets teams route, log, debug and analyze their model requests.

100K visits/mo
honeyhive.ai logo
honeyhive.ai
✓ verifiedFreemium

Observability and evaluation platform for production LLM agents, built on OpenTelemetry for tracing, monitoring and testing.

24K 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
Silver: $100 top-up (higher rate limits)
Gold: $1,000 top-up (higher rate limits)
Ultra: $10,000 top-up (highest rate limits)

Free trial available

No public pricing

Hobby: Free (10,000 requests/mo)
Pro: $79/mo (unlimited seats)
Team: $799/mo (SOC-2 & HIPAA)

Free trial available

Developer: $0 (10K events/month, up to 5 users, 30-day retention)
Basic: Free (20k inferences/mo, 1 member, 5 projects)
Core features
  • Unified API access to 1000+ image/video/audio generation models
  • Pay-per-use pricing billed per image or per second of video
  • Includes chat/LLM model access (Claude, GPT, Gemini, etc.) priced per token
  • Account tiers unlock higher GPU limits and concurrency
  • CLI and desktop app for building workflows
  • Enterprise options with dedicated support and custom deployment
  • On-demand GPU cloud with per-second billing
  • Interruptible instances at discounted rates for batch/fault-tolerant jobs
  • Reserved capacity with 1, 3, or 6-month terms for steady workloads
  • Serverless deployment with autoscale-to-zero for inference endpoints
  • Dedicated multi-node clusters with InfiniBand for large-scale training
  • Python SDK and CLI plus REST API for programmatic provisioning
  • Access to 68+ GPU types across 40+ data centers
  • Pre-configured templates for popular open-source models
  • Request logging and LLM observability
  • AI gateway with routing and automatic fallbacks
  • Caching and rate limiting
  • Session, user and custom-property analytics
  • Prompts, playground and datasets for testing
  • Integrations with OpenAI, Anthropic, Azure and more
  • OpenTelemetry-native distributed tracing across 100+ LLMs and frameworks
  • Online evaluation via LLM-as-a-judge or code
  • Offline experiments and regression detection
  • Annotation queues for expert review
  • Alerts and drift detection
  • Prompt management, CLI and docs MCP server
  • 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
  • Integrating AI image/video generation into an app via API
  • Building automated content pipelines needing multiple AI models
  • Testing and comparing many generative models from one account
  • Scaling AI media production with volume-based account tiers
  • Accessing both media-generation and LLM APIs from one platform
  • ML engineers training or fine-tuning models on rented GPUs
  • Startups running inference at scale without owning hardware
  • Developers needing quick, low-cost access to specific GPU types
  • Teams building AI agents that autonomously provision compute
  • Monitoring and debugging LLM apps
  • Analyzing model usage and cost
  • Caching responses to cut spend
  • Managing prompts and testing datasets
  • Debugging multi-agent systems
  • Monitoring live agent quality at scale
  • Catching regressions before release
  • Human review of edge cases
  • Aligning automated evaluators with domain experts
  • 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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