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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.

liteLLM logo
liteLLM
✓ verifiedFreemium

Open-source AI gateway giving dev teams unified access, fallbacks and spend tracking across 100+ LLMs.

703K visits/mo
ApX Machine Learning logo
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
Glean logo
Glean
✓ verifiedPaid

Enterprise Work AI platform for company-wide search, an AI assistant and building governed agents across 250+ connectors.

3.2M visits/mo
Fireworks AI logo
Fireworks AI
✓ verifiedPaid

Developer platform for fast serverless inference and training of open generative models, billed per token or GPU-second.

611K visits/mo1.3K saves
Pricing
Open Source: $0 (self-hosted, 100+ providers)

Free trial available

Basic: $0/mo (free forever)
Pro: $19/mo
Pro+: $59/mo

No public pricing

On-Demand H100/H200: $7/GPU-hour
On-Demand B200: $10/GPU-hour
On-Demand B300: $12/GPU-hour
Fine-tuning (LoRA SFT, models up to 16B): from $0.50 per 1M training tokens
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
  • 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
  • Enterprise search across company apps
  • Personal AI assistant grounded in work data
  • Agent builder, orchestration and governance
  • 250+ connectors and actions
  • Enterprise knowledge graph and hybrid search
  • Security controls for scaling AI
  • Serverless per-token inference with OpenAI/Anthropic-compatible APIs
  • On-demand dedicated and reserved GPU deployments
  • Fine-tuning and reinforcement-learning training pipelines
  • Large library of open LLM, vision, image and audio models
  • Optimized inference engine for throughput and latency
Use cases
  • Giving developers governed access to many LLMs
  • Attributing and controlling LLM spend
  • Keeping apps running during provider outages
  • Estimating GPU memory before training or inference
  • Comparing and selecting LLMs
  • Learning ML and LLM engineering
  • Modeling production deployment costs
  • Search across all company knowledge
  • Answer employee questions with grounded AI
  • Build and deploy custom AI agents
  • Automate cross-system workflows
  • Serving open models in production apps and agents
  • Fine-tuning models on private data
  • Powering code assistants, chatbots and RAG at scale
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