Cerebrium

Achieves 3.8-8.2 second cold starts for GPU workloads by capturing and restoring memory and GPU state snapshots rather than rebuilding containers from scratch. Uses proprietary snapshot serialization to preserve model weights and runtime state, enabling near-instant resumption of inference without recompilation or model reloading. Automatically manages snapshot lifecycle across deployments and regions.

Charges for GPU compute in granular per-second increments (e.g., H100 at $0.000944/sec) rather than per-request or reserved hourly blocks, with automatic scale-out/scale-in based on concurrent request volume. Scales from 0 to 2500+ GPUs across multiple clouds without manual capacity planning. Billing stops immediately when workload completes, eliminating idle GPU costs.

Supports custom domain names (CNAME) for inference endpoints and inter-cluster routing for multi-region deployments. Enables private networking between services without exposing endpoints publicly. Automatic SSL/TLS certificate provisioning and renewal for custom domains.

Integrates with CI/CD systems to automatically deploy new model versions on code commits or manual triggers. Supports deployment configuration in version control (TOML or YAML) and automated rollout with gradual traffic shifting. Tracks deployment history and enables rollback to previous versions via CLI or API.

Handles preemption events (e.g., spot instance interruptions, resource reclamation) with configurable grace periods for graceful shutdown. Allows applications to save state, flush buffers, and complete in-flight requests before termination. Automatic retry and rescheduling of preempted workloads with exponential backoff.

Provides native integrations with partner services like Deepgram (speech-to-text) and Rime (data validation) with pre-configured authentication and simplified API calls. Eliminates boilerplate for service initialization and error handling. Automatic credential management via Cerebrium's credential store.

Deploys inference endpoints across 4+ regions (us-east-1, eu-west-2, eu-north-1, ap-south-1) with automatic request routing to nearest region for low-latency responses. Supports data residency requirements and graceful failover to alternate regions on primary region outage. Snapshot replication across regions enables consistent cold-start performance globally.

Hosts vLLM-based LLM inference endpoints that expose OpenAI API-compatible interfaces (chat completions, embeddings, etc.) without requiring custom code rewrites. Automatically manages model loading, batching, and GPU memory optimization through vLLM's kernel-level optimizations. Supports streaming responses and async requests with configurable concurrency limits.

Deploys arbitrary Docker containers without SDK requirements or code modifications — users provide Dockerfile or pull from private registries (ECR, Docker Hub, etc.). Cerebrium orchestrates container startup, GPU attachment, networking, and scaling. Supports ASGI-compatible web frameworks (FastAPI, Starlette) and custom Python entry points with automatic port binding and health checks.

Supports streaming responses via WebSocket and Server-Sent Events (SSE) for real-time applications like voice agents, live video processing, and chat interfaces. Maintains persistent connections and streams tokens/frames incrementally without buffering full responses. Integrates with Pipecat framework for voice agent orchestration and supports async request handling for non-blocking I/O.

Executes long-running Python scripts and training jobs remotely with explicit hardware selection (e.g., 8x H100 GPUs) via CLI command `cerebrium run script.py::function --hardware HOPPER_100:8`. Manages job lifecycle, resource allocation, and result retrieval without requiring containerization. Supports distributed training across multiple GPUs with automatic environment setup.

Integrates OpenTelemetry for distributed tracing, metrics collection, and logging with native support for exporting to external monitoring platforms. Provides real-time in-app logging dashboard with per-request visibility, automatic instrumentation of HTTP requests/responses, and custom metric emission. Tracks scaling events, system performance, and inference latency with configurable sampling rates.

Supports gradual rollout of new model versions with configurable traffic splitting (e.g., 10% to new version, 90% to stable version) and automatic rollback on error detection. Enables A/B testing and canary deployments without manual traffic management. Maintains multiple endpoint versions simultaneously with independent scaling and resource allocation.

Provides persistent file storage accessible across deployments and requests, billed at $0.05/GB/month with first 100GB free. Supports file uploads, downloads, and inter-request persistence for model weights, datasets, and application state. Automatic cleanup of orphaned files and quota management per deployment.