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Developer-focused GPU cloud offering on-demand pods, serverless inference and multi-node clusters at per-second pricing for AI workloads.
AI governance and observability platform with 100+ automated tests and real-time guardrails to evaluate and monitor ML/LLM systems.
Open-source AI-agent observability platform for tracing sessions, clustering failures and running evals on live traffic.
Open-source AI gateway giving dev teams unified access, fallbacks and spend tracking across 100+ LLMs.
Open-source AI-native API gateway for routing, protecting and caching LLM/agent traffic, with a paid managed cloud.
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- ✦On-demand GPU pods across 30+ GPU types and 31 regions
- ✦Serverless GPU endpoints with sub-200ms cold starts
- ✦Zero idle cost billing for inference workloads
- ✦Multi-node clusters for distributed training
- ✦Persistent network storage for full pipelines
- ✦Real-time logs, monitoring and autoscaling from 0 to hundreds of workers
- ✦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
- ✦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
- ✦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
- ✦Unified proxy and protocol conversion across 100+ LLMs
- ✦Model-level fallback and routing
- ✦Semantic and exact-match AI caching
- ✦Token tracking and quota controls
- ✦Content-safety and data-protection filtering
- ✦MCP service hosting and plugin marketplace
- →Renting GPUs for model training and fine-tuning
- →Deploying low-latency real-time inference APIs
- →Running AI agents that need to scale instantly
- →Processing compute-heavy batch or distributed workloads
- →Evaluate models before production
- →Monitor live AI systems for issues
- →Prevent unsafe or non-compliant outputs
- →Catch data drift and quality problems
- →Monitoring AI agents in production
- →Debugging and triaging agent failures
- →Building regression evals from real traffic
- →Getting alerted on new or escalating issues
- →Giving developers governed access to many LLMs
- →Attributing and controlling LLM spend
- →Keeping apps running during provider outages
- →Centralizing access to multiple LLM providers
- →Building and governing AI agent/MCP services
- →Controlling token spend across teams
- →Adding caching and safety to LLM calls