Wafer GTM brain

Wafer provides optimized open-model inference for teams building AI applications and infrastructure.

category

AI inference infrastructure

market profile

  • text: Production AI applications need fast, reliable model serving as request volume and inference costs grow.
  • text: Wafer applies autonomous optimization across serving engines, kernels and hardware to open-weight models.

personas

  • title: AI infrastructure or ML platform lead
    pains:
    • Inference latency rises as concurrent requests increase.
    • Maintaining kernels and serving configurations across GPU hardware absorbs specialized engineering time.
  • title: AI product CTO
    pains:
    • Slow responses hurt interactive product experience.
    • Increasing token volumes compress margins and force tradeoffs between quality, speed and cost.
  • title: Cloud inference product leader
    pains:
    • Needs competitive performance on available hardware without sacrificing model accuracy.
    • Must deliver stable performance to multiple customers with different traffic patterns.

icp

  • text: AI-native software companies serving open models in latency-sensitive production applications, including voice and interactive assistants.
  • text: Cloud and infrastructure providers seeking more throughput from GPU capacity; DigitalOcean is a public example.

message pillars

  • text: Optimize for the customer's actual traffic over time.
  • text: Improve the operating economics and responsiveness of model serving.
  • text: Benchmark with real production-scale demand.

objections

  • objection: Can speed gains hold under our production traffic?
    response: Benchmark latency and throughput at representative concurrency and load before migrating a workload.
  • objection: Will optimization change model quality?
    response: Test output quality and task accuracy on the customer's own evaluation set alongside cost and latency.
  • objection: We already operate our own inference stack.
    response: Compare full cost, engineering maintenance and performance against a dedicated endpoint using the same workload.

competitors

  • name: Together AI
    why: Offers hosted open-model inference and dedicated infrastructure.
  • name: Cerebras
    why: Competes on high-speed model inference.
  • name: Modal
    why: GPU cloud used to deploy and run inference workloads.

influencers

  • name: Emilio Andere
    url: https://www.youtube.com/watch?v=7JoqmM5EPXo
    why: Co-founder discusses inference infrastructure and company strategy.
  • name: Steven Arellano
    url: https://www.youtube.com/watch?v=7JoqmM5EPXo
    why: Co-founder discusses GPU optimization and the product.

keywords

  • fast LLM inference
  • open source model serving
  • GPU kernel optimization
  • low latency inference API
  • dedicated LLM inference
  • inference cost per token
  • LLM throughput optimization

voice

  • Technical and performance oriented
  • Benchmark driven
  • Direct

signals

  • text: An AI product sees increasing token usage or tail latency as concurrency grows.
  • text: A team is migrating a latency-sensitive workflow to open-weight models or evaluating dedicated GPU endpoints.

stack

  • AMD GPUs

pricing

model: unknown

motion

hybrid

channels

  • developer content
  • benchmarks
  • LinkedIn
  • case studies
  • YC community

confidence

high

Founders

  • name: Emilio Andere
    title: Founder/CEO
    role: CEO
    bio: prev @ argonne, uchicago sand lab, elicit. math at uchicago.
  • name: Steven Arellano
    title: Cofounder
    role: Founder
    bio: prev @ two sigma, google, sei labs, and axlab. cs + econ at uchicago.

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