Cerebrium GTM brain

category

market profile

• text: Teams deploying voice, video and LLM workloads must balance cold starts, utilization, scaling and infrastructure operations.
• text: Cerebrium competes with managed serverless GPU platforms and self-managed cloud infrastructure.

personas

• title: Machine learning engineer
  pains:

  • Moving an existing model or Dockerized application into production without rewriting it
  • Debugging cold starts and observing GPU performance under load
• title: Head of AI infrastructure or engineering
  pains:

  • Provisioning enough GPUs for bursty real-time workloads without paying for excess idle capacity
  • Meeting reliability and regional data requirements as inference traffic grows
• title: Founder of a voice AI application
  pains:

  • Cold starts delay interactive conversations
  • Limited engineering time is consumed by autoscaling and GPU operations

icp

• text: AI product teams serving production LLM, real-time voice, image or video workloads with variable traffic.
• text: Engineering-led startups and larger enterprises needing regional deployment and security controls for GPU inference.

message pillars

• text: Scale AI workloads without managing servers.
• text: Run existing application code or Dockerfiles.
• text: Keep insight into application behavior and scaling.

objections

• objection: Raw cloud GPUs appear cheaper.
  response: Compare total workload cost including orchestration, idle capacity and engineering effort, using a representative load test.
• objection: Cold starts could impair our real-time experience.
  response: Benchmark the actual model, GPU and traffic profile; snapshot performance depends on the workload.
• objection: We need compliance and regional controls.
  response: Review security documentation, data residency, contract terms and the workload's specific requirements.

competitors

• name: Modal
  why: Developer-oriented serverless GPU application and inference platform.
• name: RunPod
  why: Offers serverless and dedicated GPU infrastructure.
• name: Baseten
  why: Provides managed model inference infrastructure.
• name: Replicate
  why: Runs AI models via managed APIs and GPU infrastructure.

influencers

• name: Michael Louis
  url: https://www.linkedin.com/posts/michael-louis-94104a113_serverless-ai-infrastructure-activity-7445893855700238336-kovv
  why: Cerebrium cofounder and CEO posts on serverless AI infrastructure.

keywords

• serverless GPU inference
• GPU cold start latency
• deploy voice AI GPU
• LLM autoscaling infrastructure
• serverless H100 pricing
• real time AI inference platform

voice

• Technical and developer-oriented
• Confident and performance-focused
• Practical about operational complexity

signals

• text: An AI team is launching a real-time voice or video product and evaluating cold start latency.
• text: Inference traffic is growing unevenly and a team is reassessing GPU utilization or autoscaling.
• text: A team is expanding AI service into regions with data residency requirements.

stack

• Docker
• OpenTelemetry
• gVisor
• NVIDIA GPUs

pricing

motion

channels

• SEO
• developer documentation
• GitHub
• Discord
• LinkedIn
• events

confidence

Founders

• name: Michael Louis
  title: Founder
  role: Founder
  bio: Michael is a South African entrepreneur that has founded multiple businesses across ML & A.I, Blockchain, Retail, and consumer marketplaces. Previously he was the CTO of OneCart which sold to Walmart/Massmart for an undisclosed sum. Currently, he is a co-founder at Cerebrium, a machine learning platform that makes it easier for businesses to train, deploy and monitor ML models in production.
• name: Jonathan Irwin
  title: Founder
  role: Founder
  bio: Co-founder & CTO of Cerebrium 8+ years experience as a Javascript developer including a lot of time spent in a lead developer role. BComm undergrad and Finance Honours at the University of Cape Town. Data Science at Tilburg University.