BASELINE
One request at a time, FP16 weights, nothing cached. This is how most teams first ship a model, and where most of the bill comes from.
Same model.
Smaller bill.
We take a model you already serve (or pay an API for) and cut the cost per token without changing what your users get back. Scroll to watch it happen.
MODELYOUR USER
WEIGHTSFP16
€/1M TOK€3.80
P952.4s
TOK/S38
QUANTIZATION
FP16 → FP8 → INT4. Fewer bits per weight, checked against your own eval set, so any quality cost is measured.
CONTINUOUS BATCHING
Requests share the GPU instead of queueing for it. Throughput on the same card goes up by an order of magnitude.
KV CACHE
System prompts, shared context and chat history stop being recomputed on every request. Repeated work is served from cache.
SPECULATIVE DECODING
A small draft model guesses the next few tokens; the big model checks them in one pass. Same output, less waiting.
RESULT
Same model, same answers, a fraction of the cost per million tokens. The numbers here are illustrative; yours come out of the audit.
ALSO ON THE TABLE
HOW IT RUNS
- 01
Signal
30 MINUTES · FREEBring the bill or the slow job. We tell you on the call whether there is anything on the table.
- 02
Audit
1–2 WEEKS · FREEWe profile the real workload and write down what each fix is worth, and what it risks.
- 03
Fix
2–6 WEEKS · WEEKLY BENCHMARKFixes ship in order of value, each one proven against the audit baseline.
- 04
Verify
30 DAYS · THEN HANDOFFWe measure the result, fix any regression, and leave the benchmarks and runbooks in your repo.
ENGAGEMENTS · INFERENCE
INFERENCE AUDIT
1–2 WEEKS · FREEWe measure your real workload and hand back a written report: where the time and the money go, and which fixes pay off. Yours to keep, whether or not we do the fixes.
- Profiling on your workload, not a demo
- Fixes ranked by € saved
- Baseline benchmark you keep
VLLMTENSORRT-LLMSGLANGTRITON SERVER
INFERENCE OPTIMISATION
2–6 WEEKS · 15% OF FIRST-YEAR SAVINGSWe ship the fixes in order of value, then measure the saving for 30 days against the audit baseline, per token so your traffic growth never counts. You pay 15% of one year of that verified saving, once. Nothing if it does not move.
- Weekly benchmark against the baseline
- Quality gate on your own evals
- Configs and runbooks in your repo
Save €24,000/mo → fee €43,200, once. You keep €244,800 of year one.
YOUR CLUSTERYOUR REPOYOUR EVALS
RESULTS
arhiTECH
Permit drawings read by a model that never leaves the EU.
Self-hosted inference pipeline for architecture permitting. A Mistral model served on Ollama, deployed in the EU for GDPR, classifies and interprets submitted drawings and documents, with Claude one switch away per request. OCR and field extraction fill the permit forms; vector search runs over the permit and operator database.
Service Storm
A voice agent that answers before the silence gets awkward.
Optimised the AI voice-agent pipeline behind real-time customer calls for a US field-service SaaS, so the agent starts answering while the caller is still on the line, not after an awkward silence.
QUESTIONS
What if the audit finds nothing worth fixing?
Then you pay nothing. The report still says so, with the numbers behind it, and you keep the baseline benchmark.
Will quantization hurt our model's quality?
Sometimes, a little. That is why every precision change is checked against your own eval set, and why the report lists the quality cost next to the saving.
Ask your own question
MOMO DEV · AI ANSWER
Answered by AI from what is on this site. It can be wrong, so for anything binding, ask a human. Please don't enter personal data. Privacy
THREE WAYS IN · ONE TEAM
INFERENCEMake serving cheap.Lower cost per token, lower latency, same answers.
GPU PERFMake the hardware fast.Profiling, kernels, scaling. More work per GPU-hour.
PRIVATE AIMake it yours.Your own model, on your data, on your hardware.
Send us your last inference invoice. We will tell you what is on the table.
GET IN TOUCH →