Cerebrium

Cerebrium is a serverless GPU infrastructure platform purpose-built for deploying real-time AI workloads at scale.

Reviewed by 7wData

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Publisher review

Cerebrium is a serverless GPU infrastructure platform purpose-built for deploying real-time AI workloads at scale. Founded in 2021 by Michael Louis and Jono Irwin, the platform enables developers to build, deploy, and run large language models, voice agents, video models, and other AI applications without managing underlying infrastructure. The company operates on a remote-first, globally distributed basis with headquarters in New York City and a technical origin in Cape Town.

Cerebrium addresses a specific pain point: developers spend weeks building AI features only to get stuck on infrastructure complexity. The platform eliminates that bottleneck through serverless architecture that auto-scales from zero to thousands of concurrent requests. Cold starts land between 2–4 seconds for standard workloads, with advanced memory and GPU snapshotting pushing some scenarios into sub-second territory.

Billing is per-second with zero charges for idle time, and the platform supports 12+ GPU types from T4 through H200 across multiple cloud providers and regions (US, EU, Asia). Notable customers include Deepgram, Vapi, Tavus, and ResembleAI. Cerebrium has raised $9M in funding from Y Combinator, Gradient Ventures (Google's AI fund), and Authentic Ventures. In December 2025, the company partnered with Multiverse Computing to deliver quantum-inspired AI compression integrated with serverless scaling, achieving 12x faster inference with 90% smaller models in some use cases.

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How it works

  1. Sub-4 second cold starts with GPU snapshotting

    Deploy containerized AI workloads with 2–4 second initialization via memory and GPU snapshot technology; cold start time is not billed.

  2. Per-second billing with no idle charges

    Pay only for actual GPU compute time at transparent rates (e.g., H100 80GB at $2.21/hr, T4 16GB at $0.59/hr) with no reservations or minimum commitments.

  3. 12+ GPU types and multi-region failover

    Choose from NVIDIA H100/H200, L40, A100, T4, and AMD MI300X across us-east-1, eu-west-2, eu-north-1, and ap-south-1 with automatic regional fallback.

  4. Serverless auto-scaling from 0 to 10K+ requests per second

    Automatic concurrency management and load distribution across 2,500+ GPU capacity without manual configuration or capacity planning.

  5. First-class support for vLLM, TensorRT, and custom Dockerfiles

    Deploy with popular inference frameworks (vLLM, SGLang, TensorRT-LLM, Triton) or bring arbitrary Docker containers without SDK lock-in.

  6. REST, WebSocket, and streaming endpoints

    Multiple API patterns for synchronous inference, real-time chat streams, and asynchronous job processing without application changes.

  7. SOC 2, HIPAA, GDPR, and ISO 27001 compliance

    Enterprise security certifications and data residency options for regulated industries (healthcare, finance).

Strengths and trade-offs

Strengths

  • Sub-4 second cold starts eliminate the infrastructure penalty that typically delays real-time AI features; cold-start time is not billed, reducing developer anxiety around latency.
  • Per-second billing with zero idle charges sharply undercuts reserved or hourly GPU pricing; a typical H100 workload that sits idle 70% of the time costs 70% less than Modal or RunPod's per-hour models.
  • Wide GPU selection and multi-region deployment suit latency-sensitive voice agents and video models better than competitors locked to single GPU types or geographies.

Trade-offs

  • Limited public visibility and ecosystem traction compared to Modal, RunPod, and Replicate; fewer public case studies, community tutorials, and third-party integrations make it harder to pre-evaluate for novel use cases.
  • No free tier or trial credits reported; $49/month minimum (on older pricing references) or per-second meter with unknown minimum spend may deter hobbyists and early-stage founder experiments.
  • Narrow market positioning around voice/video/LLM inference means general ML teams or batch workloads may find stronger ecosystems (SageMaker, Databricks) or lower costs (Crusoe, CoreWeave) elsewhere.

Pricing context

Cerebrium operates on a transparent per-second billing model with no minimum spend and no upfront commitments. Rates vary by GPU: T4 (16GB) at $0.59/hour, L4 (24GB) at $0.80/hour, A100 (40GB) at $1.45/hour, and H100 (80GB) at $2.21/hour. Cold-start time is not billed.

The first 100GB of persistent block storage is free; additional storage costs $5/month. Older pricing references mention a $49/month subscription tier, but current positioning emphasizes pay-per-second metering. No annual discount or reserved-capacity pricing is publicly listed, making month-to-month variable-cost budgeting the standard commercial model.

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Sources

Reporting on this tool draws on these publicly available sources.

  1. cerebrium.ai — Core product description, cold-start times, multi-region deployment, GPU types, compliance certifications, and serverless auto-scaling architecture.
  2. tracxn.com — Company founding date (2021), founders (Michael Louis, Jono Irwin), current employee count (13), total funding ($9M), and investors (Y Combinator, Gradient Ventures, Authentic Ventures).
  3. getdeploying.com — Detailed pricing per GPU type (H100 $2.21/hr, A100 $1.45/hr, L4 $0.80/hr, T4 $0.59/hr), per-second billing model, storage pricing, and customer examples (Deepgram, Vapi, Tavus).
  4. shyft.ai — Feature summary (auto-scaling, multi-cloud deployment, real-time monitoring), strengths (eliminates infrastructure management, reduces deployment timelines), and weaknesses (no free tier, limited market visibility).
  5. www.hpcwire.com — December 2025 partnership between Multiverse Computing and Cerebrium enabling 12x faster inference and 90% model size reduction via quantum-inspired AI compression.
  6. docs.cerebrium.ai — Cold-start optimization techniques including memory and GPU snapshotting, container runtime improvements, and Tensorizer model loading for large parameter models.