Cerebrium

Achieves 2-4 second cold starts for GPU workloads by capturing and restoring GPU memory and model state snapshots, avoiding full model reloading on container initialization. Uses gVisor-based container isolation to maintain security without performance overhead. Snapshots are stored and restored atomically, enabling instant model availability for bursty inference traffic without warm-up time.

Charges for GPU compute at sub-second granularity ($0.000164-$0.00167/second depending on GPU tier) with automatic scaling from zero to tier-specific concurrency limits (5 GPUs hobby, 30 GPUs standard, unlimited enterprise). Scales containers up/down based on request queue depth and resource utilization without manual capacity planning. Combines per-second metering with dynamic resource allocation to eliminate reserved capacity costs.

Supports deploying multiple versions of an inference endpoint simultaneously with traffic splitting (e.g., 90% to v1, 10% to v2) for gradual rollouts and A/B testing. Automatically routes requests based on version weights and monitors metrics per version. Enables rollback to previous versions without downtime.

Securely stores API keys, database credentials, and model weights paths as encrypted secrets, injecting them into containers at runtime as environment variables. Supports per-deployment secret scoping and rotation without redeployment. Integrates with external secret managers (AWS Secrets Manager, HashiCorp Vault) via OpenTelemetry or custom code.

Provides persistent storage ($0.05/GB/month after 100GB free) accessible from inference containers via S3-compatible API (boto3, AWS SDK). Supports reading model weights, datasets, and checkpoints; writing inference results, logs, and training checkpoints. Integrates with Cerebrium's cost tracking for transparent storage billing.

Integrates with GitHub, GitLab, and other Git providers to automatically build and deploy inference endpoints on code commits. Supports branch-based deployments (e.g., main → production, develop → staging) and automatic rollback on deployment failure. Manages build caching and deployment versioning.

Deploys containerized inference workloads across 4 geographic regions (us-east-1, eu-west-2, eu-north-1, ap-south-1) with automatic failover and region-specific data isolation. Workloads can be pinned to a single region to satisfy GDPR/HIPAA data residency requirements, or replicated across regions for low-latency global access. Uses region-local GPU pools (2500+ total capacity) to minimize inference latency and egress costs.

Deploys vLLM-based LLM serving endpoints that expose OpenAI API-compatible interfaces (chat completions, embeddings, token counting) without requiring custom API code. Automatically handles model loading, quantization, and batching. Supports streaming responses, function calling, and multi-turn conversations. Integrates with Cerebrium's GPU snapshotting for fast model initialization.

Deploys real-time voice agents using Pipecat framework with native Twilio integration for inbound/outbound calls. Handles audio streaming, speech-to-text (via Deepgram), LLM inference, and text-to-speech synthesis in a single containerized pipeline. Manages WebSocket connections for real-time bidirectional audio and automatically scales concurrent voice sessions.

Deploys arbitrary Python code (Flask, FastAPI, custom scripts) as inference endpoints without requiring Cerebrium-specific decorators or SDK modifications. Supports ASGI/WSGI applications, async functions, and long-running processes. Automatically exposes HTTP endpoints and manages request routing, environment variables, and secrets injection.

Deploys containerized applications using custom Dockerfiles, enabling arbitrary system dependencies, compiled binaries, and non-Python runtimes (Node.js, Go, Rust). Cerebrium builds and caches layers, then runs containers with GPU access and automatic scaling. Supports private Docker registries for proprietary base images.

Launches distributed training jobs across multiple GPUs (up to 8x H100 per example) with automatic NCCL/GLOO collective communication setup, gradient synchronization, and checkpointing. Manages job lifecycle (queuing, resource allocation, preemption), logs training metrics, and supports resuming from checkpoints. Integrates with PyTorch Distributed Data Parallel (DDP) and Hugging Face Accelerate.

Exposes inference endpoints as WebSocket connections for bidirectional, low-latency communication. Supports streaming responses (token-by-token LLM output, audio chunks, video frames), async request handling, and connection pooling. Automatically manages WebSocket lifecycle (connection, message routing, disconnection) without custom networking code.

Integrates OpenTelemetry (traces, metrics, logs) natively, exporting to external observability platforms (Datadog, New Relic, Prometheus, etc.) without custom instrumentation code. Automatically captures inference latency, GPU utilization, request throughput, and error rates. Supports custom metrics via OpenTelemetry SDK.