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AI infrastructure · Tool
Cerebrium
ResearchedCerebrium is a serverless GPU infrastructure platform for real-time AI workloads, offering pay-per-second pricing with no Kubernetes required. It supports deploying LLMs, voice agents, and image/video models across global regions with sub-second cold starts and SOC 2 Type II compliance.
Official site snapshots
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In one minute
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.
Pricing5 options
Pay-per-second pricing
Pay-per-second / usage-based serverless GPU pricing
no commitment or idle costs
Hobby plan is Free + compute per month: 3 user seats, up to 3 deployed apps, 500 CPU concu
Standard plan is $100 + compute per month: unlimited seats and apps, 1000 CPU concurrency,
Platforms
serverless GPU compute
API accessNot public
FoundedFounded in Cape Town, South Africa
AvailabilityWeb / remote
LicenseProprietary
Best suited to
Source-backed fit Teams needing serverless GPU infrastructure for real-time AI workloads Deploying LLMs such as Llama, Mistral, DeepSeek, and custom models Building voice agents and multimodal AI applications Organizations requiring regional data sovereignty Engineering teams wanting usage-based GPU billing without Kubernetes expertise Used by teams at companies including Tavus, Deepgram, and ResembleAI
Decision support
6 sourced answersCommon questions and adoption checks
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.
01What does Cerebrium say it can do?
Serverless GPU infrastructure for real-time AI workloads · Deploying machine learning models with a focus on efficiency and performance · Serverless AI infrastructure platform for real-time, high-performance applications · Serverless GPU infrastructure for real-time AI applications
Serverless GPU Infrastructure for Real-Time AI | Cerebrium
02Who is Cerebrium intended for?
Used by teams at companies including Tavus, Deepgram, and ResembleAI
Cerebrium now supports teams at companies like Tavus, Deepgram, and ResembleAI.
Ledger citation[3] cerebrium.ai/about
03What use cases does Cerebrium describe?
Deploy voice agents, video models, and LLMs on serverless GPUs · Large Language Models · Image & Video · LLM inference, voice AI, image generation, video generation, embeddings, reranking, and mu
Deploy voice agents, video models, and LLMs on serverless GPUs with sub-second cold starts.
04What should teams verify before adopting Cerebrium?
Customer/developer is responsible for handling Protected Health Information (PHI) and impl
as the developer and operator of your applications on the platform, you're responsible for how you handle Protected Health Information (PHI) that gets processed by those applications.
Ledger citation[4] cerebrium.ai/blog/cerebrium-supports-hipaa-compliance
05What pricing information is available for Cerebrium?
Pay-per-second pricing · Pay-per-second / usage-based serverless GPU pricing; no commitment or idle costs · Hobby plan is Free + compute per month: 3 user seats, up to 3 deployed apps, 500 CPU concu · Standard plan is $100 + compute per month: unlimited seats and apps, 1000 CPU concurrency,
Pay-per-second pricing.
06Does Cerebrium document API access?
OpenAI-compatible API endpoints
enabling you to create a scalable OpenAI compatible endpoint using vLLM
Decision guide
AI infrastructureCapabilities and operating fit
This profile connects the jobs Cerebrium is described as handling with its delivery model, access options and the subjects used to match it to related products in this directory.
Common use cases
- Large Language Model inference and deployment
- Voice AI agents and real-time voice bots
- Image generation workloads
- Video generation workloads
- Embeddings and reranking
- Multimodal AI applications
Topics mapped
Verified capabilities
- Serverless GPU infrastructure for real-time AI workloads
- Deploying machine learning models with a focus on efficiency and performance
- Serverless AI infrastructure platform for real-time, high-performance applications
- Serverless GPU infrastructure for real-time AI applications
- Serving LLMs in production
- Serverless GPU infrastructure for real-time AI model applications
- Supports deploying LLMs across regions with data residency and fine-tuning models at scale
- GPU cold-start reduction via memory snapshots that restore CUDA workloads in seconds
Recorded integrations
Intended audiences
Access signals
- Pricing model
- Pay-per-second pricing · Pay-per-second / usage-based serverless GPU pricing; no commitment or idle costs · Hobby plan is Free + compute per month: 3 user seats, up to 3 deployed apps, 500 CPU concu · Standard plan is $100 + compute per month: unlimited seats and apps, 1000 CPU concurrency,
- API
- Not publicly listed
- Source links
- 17 recorded
Source-backed
Updated July 28, 2026Verified facts
Each fact points to a recorded source, making it easy to distinguish verified product information from claims that need checking.
Serverless GPU Infrastructure for Real-Time AI | Cerebrium
Serverless GPU infrastructure for real-time AI workloads · Pay-per-second pricing model · No Kubernetes required for deployment
Serverless GPU infrastructure for real-time AI workloads
Pay-per-second pricing
No Kubernetes required
Deploy voice agents, video models, and LLMs on serverless GPUs
Available in regions us-east-1, eu-west-2, eu-north-1, and ap-south-1
View 32 more verified facts
Bring your own code with no rewrites, no decorators, and no custom SDKs
Pay-per-second / usage-based serverless GPU pricing; no commitment or idle costs
Hobby plan is Free + compute per month: 3 user seats, up to 3 deployed apps, 500 CPU concurrency, 5 GPU concurrency, 7-day log retention, community support
Standard plan is $100 + compute per month: unlimited seats and apps, 1000 CPU concurrency, 30 GPU concurrency, 30-day log retention, custom domains
Enterprise plan pricing is Custom; includes volume discounts, unlimited concurrent GPUs, unlimited log retention, private Slack support, white glove onboarding, ML engineering services
Deploying machine learning models with a focus on efficiency and performance
Large Language Models
Image & Video
Llama 3.1 70B FP8
Serverless AI infrastructure platform for real-time, high-performance applications
Global deployment with region-aware infrastructure supporting data sovereignty
LLM inference, voice AI, image generation, video generation, embeddings, reranking, and multimodal applications
Large Language Models, Voice, Image & Video
Cerebrium bills GPUs in US dollars per GPU-second (e.g., NVIDIA H100 80GB listed at $0.000953/GPU-second)
Serverless GPU platform whose pricing is benchmarked against Runpod, Modal, and Baseten
251 Little Falls Drive, Wilmington, 19808, Delaware, USA
Email support at [email protected]
Large Language Models, Voice, Image & Video
serverless GPU compute
running AI models at scale
Serverless GPU infrastructure for real-time AI applications
Large Language Models
Image & Video
SOC 2 Type II Compliance
Serving LLMs in production
Serverless GPU infrastructure for real-time AI model applications
Large Language Models, Voice, and Image & Video applications
Serverless platform that abstracts cold starts, autoscaling, orchestration, observability, and regional deployment so engineers do not manage servers
Supports deploying LLMs across regions with data residency and fine-tuning models at scale
Founded in Cape Town, South Africa
New York City
Used by teams at companies including Tavus, Deepgram, and ResembleAI
Practical capabilities
8 documented areasWhat it helps with
A concise view of the jobs, capabilities and integrations described in the recorded product sources.
Large Language Model inference and deployment
Voice AI agents and real-time voice bots
Image generation workloads
Video generation workloads
Embeddings and reranking
Multimodal AI applications
Fine-tuning models at scale
Deploy voice agents, video models, and LLMs on serverless GPUs
Availability
Source checkedWhere it runs and where to get it
Documented product formats, platforms and official distribution destinations. Availability can vary by region and plan.
Cost / license
Pay-per-second pricing · Pay-per-second / usage-based serverless GPU pricing; no commitment or idle costs · Hobby plan is Free + compute per month: 3 user seats, up to 3 deployed apps, 500 CPU concu · Standard plan is $100 + compute per month: unlimited seats and apps, 1000 CPU concurrency,
Platforms
Available in regions us-east-1, eu-west-2, eu-north-1, and ap-south-1Serverless GPU platform whose pricing is benchmarked against Runpod, Modal, and Basetenserverless GPU computeServerless GPU platform supporting Llama, Mistral, DeepSeek, and custom LLMsCerebrium is an underlying platform on which applications run
Adoption notes
DeploymentNo Kubernetes required · Global deployment with region-aware infrastructure supporting data sovereignty · Serverless platform that abstracts cold starts, autoscaling, orchestration, observability, · Global multi-region deployment with data sovereignty controls for residency, compliance, a
LicenseNot disclosed by source
Model supportLlama 3.1 70B FP8
Data controlSOC 2 Type II Compliance · SOC 2 Type II Compliance achieved for production AI infrastructure · SOC 2 and HIPAA compliant · Supports HIPAA compliance with a shared responsibility model for health AI applications · Encryption for data at rest and in transit
Learning curveIntermediate
Primary use casesLarge Language Model inference and deployment, Voice AI agents and real-time voice bots, Image generation workloads, Video generation workloads, Embeddings and reranking, Multimodal AI applications, Fine-tuning models at scale, Deploy voice agents, video models, and LLMs on serverless GPUs, Large Language Models, Image & Video, LLM inference, voice AI, image generation, video generation, embeddings, reranking, and mu, Large Language Models, Voice, Image & Video, Large Language Models, Voice, and Image & Video applications, Deploying and scaling large language models (LLMs) on serverless GPUs with usage-based pri, Low-latency voice agent architectures with real-time web search, Voice
What to verify before adopting
- Customer/developer is responsible for handling Protected Health Information (PHI) and impl
Evolution and major updates
2 dated updatesCerebrium timeline
A concise history of software releases and material product changes. Events appear only when a dated source supports what changed.
Latest first · exact dates
Showing the newest updates and meaningful milestones. Open an entry for its summary and source.
A Low-Latency Architecture for Voice Agents with Real-time Web Search
Open detailsTutorial outlining a low-latency voice agent architecture pairing Cerebrium with Linkup and LiveKit, using an MoE model and real-time web search.
View source [18]Reducing GPU Cold Starts with Memory Snapshots: Restoring CUDA Workloads in Seconds
Open detailsEngineering post detailing how Cerebrium uses CPU and GPU memory snapshots to restore warmed CUDA containers and cut GPU cold starts to seconds.
View source [19]
Citation ledger
19 unique pagesRecorded sources
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.
- 1cerebrium.ai 11 facts · 3 answers · Official site
- 2cerebrium.ai/blog/2026-gpu-buyers-guide 6 facts · 3 answers · Official site
- 3cerebrium.ai/about 7 facts · 1 answer · Official site
- 4cerebrium.ai/blog/cerebrium-supports-hipaa-compliance 6 facts · 1 answer · Official site
- 5cerebrium.ai/blog/deploying-deepseek-r1-a-guide-to-a-serverless-high-performaning-openai-compatible-endpoint 6 facts · 1 answer · Official site
- 6cerebrium.ai/blog/benchmarking-vllm-sglang-tensorrt-for-llama-3-1-api 4 facts · 2 answers · Documentation
- 7cerebrium.ai/blog/category/product-update 5 facts · 1 answer · Official site
- 8cerebrium.ai/blog/how-much-does-a-h200-cost-2025-guide 6 facts · Official site
- 9cerebrium.ai/use-cases/large-language-models 5 facts · 1 answer · Official site
- 10cerebrium.ai/blog 5 facts · Official site
- 11cerebrium.ai/blog/an-alternative-to-openai-realtime-api-for-voice-capabilities 5 facts · Documentation
- 12cerebrium.ai/blog/cerebrium-achieves-soc-2-type-ii-compliance-for-secure-production-ai-infrastructure 5 facts · Official site
- 13cerebrium.ai/pricing 4 facts · 1 answer · Pricing
- 14cerebrium.ai/blog/asgi-support-now-available-on-cerebrium 4 facts · Official site
- 15cerebrium.ai/blog/how-to-deploy-machine-learning-models-a-comprehensive-guide 4 facts · Official site
- 16cerebrium.ai/privacy 3 facts · 1 answer · Security
- 17cerebrium.ai/blog/why-serverless-compute-partners-are-now-more-important-than-ever 2 facts · Official site
- 18cerebrium.ai/blog/a-low-latency-architecture-for-voice-agents-with-real-time-web-search 1 milestone
- 19cerebrium.ai/blog/reducing-gpu-cold-starts-with-memory-snapshots-restoring-cuda-workloads-in-second 1 milestone
Research status86 substantive facts · 17 source pages · quality score 100/100
Connected directory
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