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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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Latitude
✓ verifiedFreemium
Open-source AI-agent observability platform for tracing sessions, clustering failures and running evals on live traffic.
57K 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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LangWatch
✓ verifiedFreemium
Platform to test, evaluate and observe LLM and voice AI agents, with prompt management and red-teaming for production.
23K visits/mo6.4K saves
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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
Pricing
No public pricing
Basic: $0/mo (free forever)
Pro: $19/mo
Pro+: $59/mo
Developer: €0 (50k events/mo)
Growth: €29/core-seat/mo (+ €5 per 100k events)
Open Source: $0 (self-hosted, 100+ providers)
Free trial available
Core features
- ✦Agent trace capture and conversation intelligence
- ✦Semantic and exact-text search across all traces
- ✦Automatic issue discovery with Slack/email/webhook alerts
- ✦OpenTelemetry-compatible SDK with no lock-in
- ✦Automated evals and golden dataset generation
- ✦Failure-mode clustering and MCP server integration
- ✦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
- ✦Scenario-based agent testing
- ✦LLM evaluation and quality scoring
- ✦Observability for cost and latency
- ✦Prompt management with GitHub sync
- ✦Voice AI simulation
- ✦LLM red-teaming and governance
- ✦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
Use cases
- →Monitoring AI agents in production
- →Debugging and triaging agent failures
- →Building regression evals from real traffic
- →Getting alerted on new or escalating issues
- →Estimating GPU memory before training or inference
- →Comparing and selecting LLMs
- →Learning ML and LLM engineering
- →Modeling production deployment costs
- →Catch agent issues before production
- →Evaluate and monitor LLM quality
- →Test voice AI agents at scale
- →Giving developers governed access to many LLMs
- →Attributing and controlling LLM spend
- →Keeping apps running during provider outages
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