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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liteLLM
✓ verifiedFreemium
Open-source AI gateway giving dev teams unified access, fallbacks and spend tracking across 100+ LLMs.
703K visits/mo
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honeyhive.ai
✓ verifiedFreemium
Observability and evaluation platform for production LLM agents, built on OpenTelemetry for tracing, monitoring and testing.
24K visits/mo
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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
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Helicone
✓ verifiedFreemium
LLM observability platform and AI gateway that lets teams route, log, debug and analyze their model requests.
100K visits/mo
Pricing
Open Source: $0 (self-hosted, 100+ providers)
Free trial available
Developer: $0 (10K events/month, up to 5 users, 30-day retention)
Basic: $0/mo (free forever)
Pro: $19/mo
Pro+: $59/mo
Hobby: Free (10,000 requests/mo)
Pro: $79/mo (unlimited seats)
Team: $799/mo (SOC-2 & HIPAA)
Free trial available
Core features
- ✦Unified access to 100+ LLMs in OpenAI format
- ✦Cost/spend tracking per key, user and team
- ✦Budgets and rate limiting
- ✦Automatic provider fallbacks and retries
- ✦Virtual keys and team management
- ✦Logging and observability integrations
- ✦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
- ✦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
- ✦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
Use cases
- →Giving developers governed access to many LLMs
- →Attributing and controlling LLM spend
- →Keeping apps running during provider outages
- →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
- →Estimating GPU memory before training or inference
- →Comparing and selecting LLMs
- →Learning ML and LLM engineering
- →Modeling production deployment costs
- →Monitoring and debugging LLM apps
- →Analyzing model usage and cost
- →Caching responses to cut spend
- →Managing prompts and testing datasets
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