AI visibility report
Cerebrium ranks #7 in LLM Inference & Serverless GPU AI search.
Outside the top three on 20 of the 25 prompts buyers actually ask.
Fireworks AI is cited on 9 of those losses.
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Track Cerebrium across these prompts daily.
Start free trial#7 among 10 vendors · still absent from 97.6% of tracked prompt responses
Top-3 citations across 125 prompt × platform pairs
Peer Ranking
Key Metrics
Platform Breakdown
Gemini Search
Google AI Mode
ChatGPT
Bing Copilot
PerplexityNarrower footprint, stronger tone. Cerebrium ranks #7 on presence but #1 on sentiment. That means the brand is framed well when it appears, but still needs broader prompt-response coverage.
Where Cerebrium is losing
Prompts where competitors are visible and Cerebrium is not.
These prompt-level losses are the first prompts to track and repair.
Where Cerebrium is winning2
Which serverless GPU inference platforms have the lowest cold-start latency for a customer-facing chat app that needs sub-second first-token response times?
Avg # 1.0 · 1 platform
Which serverless inference platforms support running large multimodal models — handling both text and image inputs — on high-end GPUs at production scale?
Avg # 2.0 · 1 platform
Where Cerebrium is losing5
What LLM inference platforms offer the best SDK and API ergonomics for a Python-first engineering team shipping a conversational AI feature?
Competitors on 3 platforms
Track this promptWhich serverless GPU inference platforms have the best developer experience for iterating quickly on prompt templates and sampling parameters without redeploying?
Competitors on 3 platforms
Track this promptWhat are the fastest serverless GPU inference platforms to go from an open-source LLM to a live production API endpoint with no GPU infrastructure to manage?
Competitors on 3 platforms
Track this promptWhich LLM inference platforms integrate natively with vector database services for building retrieval-augmented generation pipelines without extra glue code?
Competitors on 2 platforms
Track this promptWhat are the best LLM serving platforms for a small ML team that needs built-in request logging and usage dashboards without wiring up a separate observability stack?
Competitors on 2 platforms
Track this prompt
Track Cerebrium daily before the next report refresh.
Track these gapsResearch dossierCapabilities, use cases, sources, reviews, pricing, and FAQ
Overview
Cerebrium is a New York-based serverless AI infrastructure platform founded in 2021 and backed by Gradient Ventures, Y Combinator, and Authentic Ventures. The platform enables engineering teams to deploy, scale, and operate multimodal AI workloads—including LLMs, voice agents, video generation, and digital avatars—without managing servers or DevOps infrastructure. Cerebrium's core technical differentiator is its proprietary container runtime with GPU and memory snapshotting, delivering cold starts of 2–4 seconds across 12+ GPU types from T4 to B200. It charges per second of actual compute usage, supports custom Dockerfiles without code rewrites, and provides native multi-region deployment, OpenTelemetry observability, and enterprise compliance certifications (SOC 2, HIPAA, GDPR, ISO 27001). Notable customers include Tavus, Deepgram, Vapi, and Resemble AI.
Cerebrium is a managed serverless GPU platform for real-time, multimodal AI applications. It allows developers to deploy any AI workload—LLMs, voice pipelines, video models, or custom containers—using a simple CLI or Dockerfile, with automatic autoscaling, per-second billing, and built-in observability across multiple cloud regions.
Key Facts
- Founded
- 2021
- HQ
- New York, USA
- Founders
- Michael Louis, Jonathan Irwin
- Employees
- 11-50
- Funding
- ~$9M
- Status
- Private
Target users
Key Capabilities10
- Serverless GPU compute with 2–4 second cold starts via memory and GPU snapshotting
- 12+ GPU types (T4, L4, A10, L40s, A100 40/80GB, H100, H200, B200) with per-second billing
- Bring-your-own-Dockerfile deployment with no SDK rewrites or decorators required
- Elastic autoscaling from zero to thousands of concurrent GPU instances
- Multi-region deployments (US, EU, Asia) with data residency and sovereignty controls
- Full observability: real-time logs, metrics, scaling events, and native OpenTelemetry integration
- SOC 2, HIPAA, GDPR, and ISO 27001 compliance with gVisor container isolation
- WebSocket, streaming, async, and REST endpoint types with concurrency and batching controls
- CI/CD pipeline integration with gradual rollouts and versioned deployments
- 99.999% uptime with multi-region failover and automatic traffic rerouting
Key Use Cases8
- Real-time voice agent infrastructure (sub-500ms end-to-end latency pipelines)
- LLM inference serving (custom and open-source models at scale)
- LLM fine-tuning on multi-GPU clusters (H100, H200)
- Generative video and digital avatar rendering
- Image generation and computer vision inference
- Large-scale batch data processing and ETL pipelines
- Multimodal AI application pipelines combining ASR, LLMs, and TTS
- Regulated-industry AI deployments requiring HIPAA/GDPR compliance
Cerebrium customer outcomes
18x faster cold starts (from several minutes to ~10 seconds)
Migrated from multi-cloud GPU setup to Cerebrium for AI tutor and avatar workloads, eliminating complex scaling logic and allowing engineers to focus on product development.
50% lower inference costs; accuracy improved from 83% to 92%
Deployed customer-specific SLM inference at up to 150 requests/second per model with production-grade autoscaling, reducing costs while improving model accuracy.
Cold starts reduced from 30s to under 3s (warm); $5K–$10K/month in infrastructure savings
Replaced Azure Functions and reserved instances with Cerebrium for digital human avatar deployments, cutting cold-start times dramatically and eliminating idle infrastructure costs.
Runs real-time audio and video AI models at scale on Cerebrium, maintaining compute reliability through rapid viral growth and usage spikes.
Recent Trend
How AI describes Cerebrium3
\[3\] ### Most serverless GPU platforms Platforms such as Together AI, Baseten, Replicate, Fal, RunPod Serverless, and Cerebrium generally expose: * request logs * endpoint health * deployment metrics * autoscaling information...
Which serverless GPU platforms give engineering teams the most visibility into per-request latency and token throughput right out of the box?
When engineering teams evaluate serverless GPU platforms for LLM production workloads, Baseten and Cerebrium provide the deepest native visibility into per-request latency and token throughput right out of the box, closely followed by Modal . ### 1\.
Which serverless GPU platforms give engineering teams the most visibility into per-request latency and token throughput right out of the box?
...at application on serverless GPU infrastructure requires carefully navigating the multi-stage cold-start bottleneck . cerebrium.ai A true cold start for a Large Language Model involves sequential phases—infrastructure provisioning, massive containe...
Which serverless GPU inference platforms have the lowest cold-start latency for a customer-facing chat app that needs sub-second first-token response times?
Most cited sources3
Alternatives in LLM Inference & Serverless GPU6
Cerebrium positions itself as a developer-first, multimodal serverless GPU platform purpose-built for real-time AI workloads—voice agents, LLMs, video generation, and digital avatars—rather than a general-purpose GPU marketplace or a model-API aggregator.
- Its key differentiators are sub-4-second cold starts enabled by a proprietary container runtime and GPU/memory snapshotting, bring-your-own-Dockerfile deployment (no SDK rewrites), per-second billing, and a compliance stack (SOC 2, HIPAA, GDPR, ISO 27001) that supports enterprise data-residency requirements.
- Against Modal Labs and Beam, Cerebrium emphasizes multimodal/voice-video specialization and deeper compliance.
- Against Baseten and Replicate, it highlights full Dockerfile control and broader GPU diversity.
- Against RunPod and Together AI, it stresses managed orchestration and 99.999% uptime SLAs over raw GPU access or hosted-model APIs.
Reviews
Praised
- Sub-4 second cold starts via GPU snapshotting
- Bring-your-own-Dockerfile with no code rewrites
- Highly responsive engineering support via Slack
- 40% cost savings vs traditional cloud providers
- 12+ GPU types with per-second billing
- Production-grade autoscaling from zero to thousands of instances
- Developer-friendly CLI and deployment experience
- SOC 2, HIPAA, GDPR, ISO 27001 compliance out of the box
Criticized
- AWS and GCP credits cannot be applied to Cerebrium spend
- Not cost-optimal for always-on, high-utilization workloads
- No verified third-party reviews on G2 or Gartner (early-stage brand recognition)
- Capacity guarantees require minimum monthly spend commitments
No verified third-party review scores exist on G2 (profile unclaimed, 0 reviews) or Gartner Peer Insights as of May 2026. Community sentiment on Product Hunt and Hacker News is positive, with developers praising the speed and simplicity of GPU deployment. Published case studies from Tavus, Creatium, DistilLabs, and bitHuman document strong customer satisfaction around cold-start performance, developer experience, and support responsiveness.
Pricing
Per-second, usage-based billing for all compute. GPU rates range from $0.000164/s (T4) to $0.00167/s (B200), with A10 at $0.000306/s and H100 at $0.000944/s. Memory is billed at $0.00000222/GB/s; CPU at $0.00000655/vCPU/s. Storage costs $0.05/GB/month (first 100 GB free). Three plan tiers: Hobby (free base + compute, up to 3 apps, 5 concurrent GPUs), Standard ($100/month + compute, unlimited apps, 30 concurrent GPUs, custom domains), and Enterprise (custom pricing, unlimited concurrency, dedicated Slack, volume discounts, ML engineering services). Volume discounts and capacity guarantees (e.g., up to 50 H100s with $10,000/month minimum spend) are available for enterprise deployments.
Limitations
- AWS and GCP cloud credits cannot be applied to Cerebrium usage, limiting its appeal for teams with existing hyperscaler commitments.
- The platform is optimized for bursty and variable workloads; always-on, high-utilization workloads may be more cost-effective on reserved instances.
- The G2 profile is unclaimed with zero published reviews, limiting third-party social proof.
- With approximately 13 employees as of early 2026, enterprise feature requests and dedicated SLA support may be constrained relative to larger vendors.
- Guaranteed capacity commitments require a minimum monthly spend (e.g., $10,000/month for H100 guarantees).
Frequently asked questions
Topic coverageCoverage by buyer topic
Topic Coverage
Prompt-Level Results
| Prompt | ChatGPT | Google AI Mode | Gemini Search | Bing Copilot | Perplexity |
|---|---|---|---|---|---|
Capability1/5 cited (20%) | |||||
What LLM inference platforms handle streaming token responses well and support long context windows for document-processing use cases? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Which LLM inference platforms let enterprise teams bring their own fine-tuned model weights and enforce strict data isolation with private deployments? | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Which serverless inference platforms support running large multimodal models — handling both text and image inputs — on high-end GPUs at production scale? | A competitor was cited | Your brand was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
Which serverless GPU platforms support batch inference jobs for offline processing pipelines in addition to real-time API endpoints? | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited |
I'm looking for an inference platform that supports custom CUDA kernels and speculative decoding — what are my options for a performance-critical chatbot? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Developer Experience1/5 cited (20%) | |||||
Which serverless GPU platforms give engineering teams the most visibility into per-request latency and token throughput right out of the box? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Your brand and a competitor were cited | Neither your brand nor a competitor was cited | A competitor was cited |
What are the best LLM serving platforms for a small ML team that needs built-in request logging and usage dashboards without wiring up a separate observability stack? | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
What LLM inference platforms offer the best SDK and API ergonomics for a Python-first engineering team shipping a conversational AI feature? | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
Which serverless inference platforms make it easiest to manage multiple open-source model versions in parallel across staging and production environments? | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Which serverless GPU inference platforms have the best developer experience for iterating quickly on prompt templates and sampling parameters without redeploying? | A competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
Integrations & Ecosystem0/5 cited (0%) | |||||
Which LLM inference platforms integrate natively with vector database services for building retrieval-augmented generation pipelines without extra glue code? | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
What serverless GPU inference providers work best alongside AI orchestration frameworks so teams can chain model calls and tool use cleanly? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
Which inference platforms expose an API compatible with the standard chat completions format so switching providers requires minimal code changes? | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
What LLM inference platforms integrate with cloud object storage for loading large model weights at deploy time without manual upload steps? | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Which serverless GPU platforms support webhook callbacks or event-driven triggers for async inference jobs in a data pipeline built on a workflow orchestrator? | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
Performance & Reliability1/5 cited (20%) | |||||
Which serverless inference providers deliver the highest tokens-per-second throughput for a high-volume API serving thousands of concurrent users? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Which serverless GPU inference platforms have the lowest cold-start latency for a customer-facing chat app that needs sub-second first-token response times? | A competitor was cited | Neither your brand nor a competitor was cited | Your brand was cited | Neither your brand nor a competitor was cited | A competitor was cited |
What are the most reliable LLM serving platforms for an enterprise use case that requires 99.9% uptime SLAs and geo-redundant deployments? | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
What LLM inference platforms can handle sudden traffic spikes — say 10x burst load — without throttling for a mid-sized SaaS product? | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Which serverless GPU platforms have the best cost-per-token at scale for a startup burning significant GPU budget on a document summarization product? | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
Setup & First Run0/5 cited (0%) | |||||
Which LLM inference platforms have the easiest onboarding for a solo developer deploying a fine-tuned open-source model for the first time? | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Which LLM inference platforms support deploying quantized open-source models with minimal setup for a backend engineer with no MLOps background? | A competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
What's the quickest serverless GPU platform to get an image-generation model behind a REST API with autoscaling out of the box? | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
I'm evaluating serverless inference platforms for a small startup — which ones let you deploy a custom open-source LLM without writing any infrastructure config? | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
What are the fastest serverless GPU inference platforms to go from an open-source LLM to a live production API endpoint with no GPU infrastructure to manage? | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
Turn this matrix into daily prompt monitoring.
Track prompt changesVertical Ranking
| # | Brand | PresencePres. | Share of VoiceSoV | DocsDocs | BlogBlog | MentionsMent. | Avg PosPos | Sentiment |
|---|---|---|---|---|---|---|---|---|
| 1 | RunPod | 15.2% | 29.5% | 1.6% | 0.0% | 40.0% | #4.2 | +0.32 |
| 2 | Fireworks AI | 12.0% | 17.0% | 1.6% | 6.4% | 36.8% | #3.4 | +0.46 |
| 3 | Beam | 11.2% | 17.9% | 0.0% | 0.0% | 12.0% | #4.8 | +0.29 |
| 4 | Baseten | 8.8% | 16.1% | 6.4% | 3.2% | 42.4% | #2.6 | +0.50 |
| 5 | Modal | 8.0% | 9.8% | 0.0% | 2.4% | 0.0% | #3.2 | +0.55 |
| 6 | Together AI | 5.6% | 7.1% | 2.4% | 0.8% | 44.8% | #2.1 | +0.30 |
| 7 | Cerebrium | 2.4% | 2.7% | 0.8% | 0.0% | 8.0% | #2.0 | +0.60 |
| 8 | Lepton AI | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
| 9 | Replicate | 0.0% | 0.0% | 0.0% | 0.0% | 32.8% | — | — |
| 10 | Sference | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
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