01
Hosted inference is convenient, until your margin becomes their margin. Token markups compound silently while you're shipping features.
their margin62¢ of every dollar
currently accepting engagements
AI infrastructure, on your terms
hosted · before
throughput438tok/s
p95 latency131ms
$ / 1M tok$8.40
self-hosted · after
throughput1,840tok/s↑ 2.0×
p95 latency38ms↓ 45%
$ / 1M tok$2.27↓ ~60%
throughput · live1,840tok/s
vLLM + speculative decoding · same hardware
latency distribution38ms p95
0mstighter peak →200ms
8× H100 · utilization82%
request pipeline · live
~60%inference cost savedmedian, hosted API → self-hosted
2.0×throughput upliftvLLM + speculative decoding, same hardware
~150 tok/soutput speed, per userLlama 3.1 8B FP8 on H100, optimised
100%eval parityagainst hosted baseline, before rollout
01
You own the data
Weights, logs, embeddings, traces. Nothing is shipped to a third-party endpoint, ever.
02
You own the infra
Your VPC. Your GPUs. Your kubernetes. We never deploy to a platform we control.
03
Predictable bills
Monthly inference cost varies ±2%, not 4×. No token markup, no surprise tier upgrades.
04
We do the housekeeping
Migration, kernels, scaling, on-call. We leave a runbook'd system, then we leave.
the stackYour infrastructure.
Your infrastructure.
Our toolchain.
we deploy on
AWS
Google Cloud
Microsoft Azure
Cloudflare
NVIDIA
Hugging Face
Replicate
DigitalOcean
Vercel
RunPod
Modal
Lambda
CoreWeave
Together AI
Fireworks AI
Anyscale
Vast.ai
Paperspace
AWS
Google Cloud
Microsoft Azure
Cloudflare
NVIDIA
Hugging Face
Replicate
DigitalOcean
Vercel
RunPod
Modal
Lambda
CoreWeave
Together AI
Fireworks AI
Anyscale
Vast.ai
Paperspace
AWS
Google Cloud
Microsoft Azure
Cloudflare
NVIDIA
Hugging Face
Replicate
DigitalOcean
Vercel
RunPod
Modal
Lambda
CoreWeave
Together AI
Fireworks AI
Anyscale
Vast.ai
Paperspace
we work with
PyTorch
NVIDIA CUDA
AMD ROCm
Llama
Mistral
Ollama
LangChain
Hugging Face Transformers
Docker
MLflow
Weights & Biases
vLLM
TensorRT-LLM
TensorRT
SGLang
Triton
FlashAttention
JAX
Qwen
LlamaIndex
Ray
PyTorch
NVIDIA CUDA
AMD ROCm
Llama
Mistral
Ollama
LangChain
Hugging Face Transformers
Docker
MLflow
Weights & Biases
vLLM
TensorRT-LLM
TensorRT
SGLang
Triton
FlashAttention
JAX
Qwen
LlamaIndex
Ray
PyTorch
NVIDIA CUDA
AMD ROCm
Llama
Mistral
Ollama
LangChain
Hugging Face Transformers
Docker
MLflow
Weights & Biases
vLLM
TensorRT-LLM
TensorRT
SGLang
Triton
FlashAttention
JAX
Qwen
LlamaIndex
Ray
how we thinkInference is a
Inference is a
codesign problem.
Architecture.
Match model size to actual task entropy. Distill where you can, route where you must, keep a hosted fallback for the long tail.
- distillation studies
- MoE / dense tradeoffs
- routing policy
- fallback contracts
what we doSeven practices,
Seven practices,
one discipline.
- ~60%median cost cut
Move from OpenAI, Anthropic, Together, Replicate to your own GPUs without breaking prod. Shadow traffic, drift checks, gradual cutover.
01 · Migration~60%median cost cut
Move from OpenAI, Anthropic, Together, Replicate to your own GPUs without breaking prod. Shadow traffic, drift checks, gradual cutover.
Hosted → self-hostedShadow trafficDrift dashboardsEval parity
full scope · deliverables →why fastpriorsMost teams don't
Most teams don't
need an API.
02
Your data becomes their training set. Your roadmap becomes contingent on someone else's quota, someone else's outage, someone else's pricing memo.
data sovereignty✓ in your VPC
03
We believe the next durable AI companies will run on infrastructure they own. Predictable bills. Auditable data paths. Latency they can fix, not file a ticket about.
billing variance±2% month-to-month
how we operate
Few engagements. Each one shipped.
01 / lead
2 / founders
end-to-end, every engagement
02 / capacity
6 / yr
engagements we'll take
03 / scope
fixed
written, signed, no creep
04 / never
- revenue share
- platform lock-in
- training on your data
Ready to own
your inference stack?
Free 30-min architecture call. No deck, no pitch, bring a P&L line item or a latency graph and we'll tell you whether we can help.