voice agents · seed
−81%inference spend
From $148K/mo to $28K/mo without a perf regression
Llama 3.1 70B → 8B distilled + speculative draft. Migrated off Together onto 4× H100 in their own AWS account.
est. 2024 · self-sovereign AI
AI infrastructure, on your terms.
Self-sovereign inference for AI-native startups. You own the data, the GPUs, and the keys. We do the housekeeping — migration, kernels, scaling, on-call.
hosted · before
throughput438tok/s
p95 latency131ms
$ / 1M tok$8.40
self-hosted · after
throughput438tok/s↑ 4.2×
p95 latency131ms↓ 71%
$ / 1M tok$8.40↓ 73%
throughput · live438tok/s
vLLM + speculative decoding · same hardware
latency distribution131ms p95
0mstighter peak →200ms
8× H100 · utilization34%
request pipeline · live
73%median cost reductionacross 14 production migrations
4.2×throughput upliftvLLM + speculative decoding
38msmedian p95 inferenceLlama 3.1 8B · INT8
11dtime to first cutoverfrom kickoff to live traffic
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.
how we think
Self-sovereign inference is a codesign problem.
You can't pick a runtime without knowing the hardware. You can't pick the hardware without knowing the model. You can't pick the model without knowing the workload. We work all four layers as one decision — that's why the numbers land.
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
we will not optimize one layer in isolation —that's how surprises end up in the invoice.
what we doSeven practices,
Seven practices,
one discipline.
Self-hosted inference at production quality. We work on a small number of well-defined problems and we keep the surface area honest.
01 · Migration73%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 →how we workSix to fourteen
Six to fourteen
weeks. One cutover.
Every engagement starts with a written audit. You decide whether the math works before we touch production. Then we execute against that plan — no scope creep, no surprises in the invoice.
Audit.
We read your traffic, models, latency budget, and current spend. You get a written cost model that says, in numbers, whether self-hosting actually saves you money.
deliverableCost model · go/no-go
fixed-feeyou decide before we touch prod
kickoff~ week 12
selected work
Receipts.
Anonymized but real. Each engagement here came with a fixed-fee audit first; we only proceed when the math works for the client.
code assistant · series A
5.1×throughput
5× more concurrent users on the same hardware
Switched from naive HF Transformers to TRT-LLM + custom KV layout. Same 8 GPUs, 5.1× output tokens/sec.
rag platform · series B
31msp95 latency
Sub-50ms RAG at the 95th percentile
Bi-encoder + cross-encoder rerank, INT8 quantized, batched. Replaced a 3-vendor stack with one VPC.
why fastpriorsMost teams don't
Most teams don't
need an API.
They need their own inference stack — and someone who's done the migration enough times to make it boring. That's the entire job.
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
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
We're a small firm by design. Four engineers, six engagements a year, every scope written down before we start.
01 / team
4
engineers, no PMs, no sales
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.