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Unified API gateway that routes requests to 400+ LLMs across 70+ providers with failover and no subscription.
Open-source AI gateway giving dev teams unified access, fallbacks and spend tracking across 100+ LLMs.
Enterprise AI observability and security platform to monitor, evaluate, and govern agentic and ML systems with guardrails.
Weights & Biases is a widely used MLOps platform for experiment tracking, model management and evaluating AI applications.
Cloud platform from the makers of PyTorch Lightning for building, training and deploying AI in browser-based GPU Studios.
Free trial available
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No public pricing
- ✦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
- ✦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
- ✦End-to-end agentic and ML observability
- ✦Real-time guardrails (hallucination, PII, jailbreak)
- ✦Continuous evaluations and custom judges
- ✦Root-cause analysis and decision lineage
- ✦AI governance, risk, and compliance controls
- ✦Flexible SaaS, VPC, or on-prem deployment
- ✦Experiment tracking and visualization for ML training runs
- ✦Model and artifact versioning and management
- ✦Hyperparameter optimization tooling
- ✦Collaborative dashboards and reports for ML teams
- ✦LLM application tracing and evaluation tooling
- ✦Browser-based Lightning Studios with on-demand GPUs
- ✦PyTorch Lightning training framework
- ✦Model training, fine-tuning and deployment
- ✦Collaborative, shareable ML environments
- ✦Scalable multi-GPU/multi-node compute
- →Accessing many LLMs through one integration
- →Adding provider redundancy to AI apps
- →Comparing model price and performance
- →Powering agents and AI-native products
- →Giving developers governed access to many LLMs
- →Attributing and controlling LLM spend
- →Keeping apps running during provider outages
- →Monitoring production AI agents
- →Enforcing safety guardrails on LLM apps
- →Evaluating and debugging model behavior
- →Governance and compliance for enterprise AI
- →ML engineers tracking and comparing training experiments
- →Research teams versioning datasets and model checkpoints
- →Teams building and evaluating LLM-powered applications
- →Organizations collaborating on machine learning projects
- →Prototype and train ML models in the cloud
- →Fine-tune and deploy foundation models
- →Run reproducible AI experiments collaboratively