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Start here for the decision-making essentials: what Cerebrium does, who it is for, how it is accessed, and the first-party sources behind this profile.
Pay-per-second GPU compute with no idle charges or reservations (e.g., H100 $0.000944/s, L
Cerebrium is a serverless AI infrastructure platform for real-time, high-performance applications. It provides global, region-aware GPU deployment, sub-second cold starts, instant autoscaling, and hardened gVisor workload isolation, with support for voice agents, video models, LLMs, multimodal pipelines, and large-scale batch jobs.
Read-only public captures of Cerebrium’s homepage and verified pricing page. Screenshots are dated, never live embeds, and open full-screen.
Short answers to the questions buyers and builders commonly ask about Cerebrium. Each answer cites the shared ledger below, where every source is listed once.
Serverless GPU infrastructure for real-time AI workloads with sub-second cold starts, inst · 99.999% uptime with multi-region failover that routes traffic to the next best region/clou · Serverless GPU infrastructure for real-time AI model applications with sub-second cold sta · Serverless GPU infrastructure for real-time AI workloads with sub-second cold starts and i
Serverless GPU Infrastructure for Real-Time AI Deploy voice agents, video models, and LLMs on serverless GPUs with sub-second cold starts.
Deploying voice agents, video models, LLMs, embeddings, reranking, and other real-time AI · Real-time voice bots, multimodal inference pipelines, large-scale batch jobs, LLM serving, · Deploying voice agents, video models, LLMs, and any AI workload · Deploying voice agents, video models, LLMs, and other AI workloads
Deploy voice agents, video models, and LLMs on serverless GPUs with sub-second cold starts.
Pay-per-second GPU compute with no idle charges or reservations (e.g., H100 $0.000944/s, L · Pay-for-what-you-use model charging per core, per memory, and per GPU · Competitive On Demand Pricing
Pay for what you use Cerebrium charges based on actual compute time, measured in seconds.
OpenAI-compatible REST endpoint accessible at https://api.cerebrium.ai/v4/
base_url=os.getenv("LLM_BASE_URL"), # <https://api.cerebrium.ai/v4/><project>/sglang-llm/v1
Supports inference engines including vLLM, SGLang, and TensorRT-LLM with static, dynamic, · Integrates with LiveKit, Linkup, Deepgram, Cartesia, SGLang inference framework, and Conco
Deploy Llama, Mistral, DeepSeek, and custom LLMs on serverless GPUs with vLLM, SGLang, and TensorRT-LLM.
Bring-your-own-code deployment without Kubernetes, SDKs, or decorators — point to your ent · Multi-region deployments with region-aware compute placement for data residency, complianc · Global region-aware infrastructure with data sovereignty, deployed via Docker and .toml co · Multi-region global deployment enabling data sovereignty
Point us to your entry point or Dockerfile and we'll run your application exactly as is - versioned, reproducible, and ready to scale.
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HTTP 200 verified twice
Serverless GPU Infrastructure for Real-Time AI
Serverless GPU infrastructure for real-time AI workloads · Sub-second cold starts and instant autoscaling · Global, region-aware deployment with data sovereignty
Serverless GPU infrastructure for real-time AI workloads with sub-second cold starts, instant autoscaling, and memory/GPU snapshotting
Deploying voice agents, video models, LLMs, embeddings, reranking, and other real-time AI inference workloads
Bring-your-own-code deployment without Kubernetes, SDKs, or decorators — point to your entry point or Dockerfile
SOC 2, HIPAA, GDPR, and ISO compliance for regulated and sensitive workloads
Each workload runs on gVisor in a hardened, isolated environment with SSL-encrypted VPN access and encryption at rest
99.999% uptime with multi-region failover that routes traffic to the next best region/cloud within constraints
Pay-per-second GPU compute with no idle charges or reservations (e.g., H100 $0.000944/s, L4 $0.000222/s, T4 $0.000164/s, CPU $0.00000655/vCPU/s, Memory $0.00000222/GB/s, Storage $0.05/GB/mo with first 100GB free)
Serverless GPU infrastructure for real-time AI model applications with sub-second cold starts, instant autoscaling, orchestration, observability, and regional deployment
A concise view of the jobs, capabilities and integrations described in the recorded product sources.
Documented product formats, platforms and official distribution destinations. Availability can vary by region and plan.
A concise history of software releases and material product changes. Events appear only when a dated source supports what changed.
Showing the newest updates and meaningful milestones. Open an entry for its summary and source.
Tutorial describing a Cerebrium, Linkup, and LiveKit architecture for production voice agents that pairs a fast MoE model with real-time web search to stay within a sub-second latency budget.
View source [3]Engineering blog post outlining Cerebrium's use of CPU and GPU memory snapshots to restore warmed containers in seconds and address production GPU cold start times.
View source [15]Facts, answers, structured details, milestones and primary resource links cite this shared ledger. Each external page appears once; release tags from the same GitHub project are grouped under one release history.
These tools share a workflow, capability or audience with Cerebrium. They may complement it rather than replace it, and are not presented as integrations or endorsements.
Real-time voice bots, multimodal inference pipelines, large-scale batch jobs, LLM serving, and model fine-tuning
Serverless GPU platform that spins up containers globally in 1–2 seconds with on-demand CPU and GPU autoscaling and no pre-warming or reserved capacity
Multi-region deployments with region-aware compute placement for data residency, compliance, and latency requirements
Supports inference engines including vLLM, SGLang, and TensorRT-LLM with static, dynamic, and continuous batching
Supports Llama, Mistral, DeepSeek, and custom LLMs
Achieved SOC 2 Type II compliance covering infrastructure security, encryption at rest and in transit, MFA, least-privilege access, secure software development, production change management, incident response, continuous monitoring, vendor
SOC 2 Type II report available to prospective customers under mutual NDA via a trust center
Cerebrium was founded in 2021 in Cape Town, South Africa and is now headquartered in New York City; serves teams at Tavus, Deepgram, and ResembleAI
2021
New York City, USA (originally founded in Cape Town, South Africa)
Build global serverless GPU infrastructure for real-time AI model applications
LinkedIn (cerebrium), Twitter (cerebriumai), GitHub (CerebriumAI), YouTube (@cerebrium), Crunchbase, Product Hunt, and G2
Sales contact via https://cerebrium.ai/book-demo and customer support via https://cerebrium.ai/contact, available in English
Serverless GPU infrastructure for real-time AI workloads with sub-second cold starts and instant autoscaling
Deploying voice agents, video models, LLMs, and any AI workload
Serverless AI infrastructure platform for real-time, high-performance applications
Cerebrium (alternate name: Cerebrium AI)
2021
Global region-aware infrastructure with data sovereignty, deployed via Docker and .toml configuration
Supports high-performance NVIDIA GPUs including L40s and H100s (and B200 / BLACKWELL_B200) and CPU runtimes on ARM and x86
Pay-for-what-you-use model charging per core, per memory, and per GPU
OpenAI-compatible REST endpoint accessible at https://api.cerebrium.ai/v4/
Integrates with LiveKit, Linkup, Deepgram, Cartesia, SGLang inference framework, and Concourse CI/CD; reference examples at github.com/CerebriumAI/examples
Four-person team
Slack-based customer support with rapid response (questions typically answered within 30 minutes)
Serverless GPU infrastructure for real-time AI workloads with sub-second cold starts and instant autoscaling
Cerebrium (alternate name: Cerebrium AI)
2021
Serverless GPU infrastructure
