AI Infra Cost Audit
Most AI platforms let token and database costs scale linearly with user growth and call it the price of growing. It isn't. We run a 2-week diagnostic on your actual inference and data pipeline, then fix the highest-leverage bottlenecks — typically a 40-60% reduction in compute spend, same accuracy, same stack.
Get a Cost Diagnostic40-60%
Compute/API spend cut
2 wks
Diagnostic turnaround
0
Accuracy traded away
Fixed
Price, not hourly billing
Your Costs Shouldn't Scale Linearly With Your Users
Ask yourself one question: do your token and database costs climb in a straight line with user growth? For most AI platforms, the honest answer is yes — and that's not a usage problem, it's an architecture problem. GPU prefill locks on large inputs, extraction workloads burning full-reasoning compute on deterministic output, redundant token spend chained across calls, and database queries that were never built for the data volume they now carry. None of that shows up until the bill does.
We run a fixed-scope, 2-week diagnostic across your inference pipeline and data layer: prefill/concurrency behavior under load, token throughput versus task complexity, model routing (are simple extractions burning frontier-model compute?), and database query/index patterns at current and projected scale. You get a prioritized map of exactly where the cost is leaking and what fixing each leak is worth — before you commit to a build.
Then, if you proceed, the 4-8 week build implements the highest-leverage fixes against your live stack. This isn't a generic FinOps dashboard or a one-time right-sizing exercise — it's inference and data architecture work, the same engineering that previously compressed a document-AI platform's API spend 40-60% in two weeks without any accuracy loss.
Where the Money Is Actually Leaking
The specific mechanisms that let cost scale linearly with growth — and what we do about each one.
Chunked Prefill
Large inputs lock the GPU during prefill, stalling every concurrent request behind them. We cap and interleave prefill so latency stays flat under load — no added hardware.
Speculative Decoding
Deterministic, low-complexity outputs don't need full reasoning compute. A lightweight draft model handles the easy tokens; 2-3x throughput on the same hardware.
Model Routing
Simple extractions and classifications get routed to lightweight models; heavy compute is reserved for the calls that actually need it.
Prompt & Context Audit
Redundant token spend hiding in chained calls and bloated context windows — restructured and trimmed without changing output quality.
Database Scaling Diagnosis
Query and index patterns that worked at launch volume but degrade non-linearly at current scale — the other half of the linear-cost problem, and the one most cost audits skip.
Prioritized Impact Map
Every finding comes with a projected cost and throughput impact, ranked — so you know which fix pays for the engagement before you approve the build.
Who Overpays for AI Infra Without Knowing It
AI Platforms Scaling Fast
Usage-based AI products where compute cost per user hasn't been re-examined since launch architecture — the bill is now a growth tax.
Document & Data Processing Platforms
Extraction, reconciliation, and OCR-adjacent pipelines where GPU cycles are spent identically on trivial and complex outputs alike.
SaaS With Growing Token + DB Spend
Products where both the LLM bill and the database bill climb with every new signup, and nobody has isolated which part is architecture versus actual growth.
Anyone Building on Hosted Model APIs
Teams on OpenAI/Anthropic/hosted-API billing who haven't audited model routing or prompt architecture since their first integration.
How the Engagement Runs
Week 0: Scoping Call
We look at your current spend, growth curve, and stack, and quote the fixed-price diagnostic — no commitment beyond the 2 weeks.
Weeks 1-2: Diagnostic
Full inference and data-pipeline audit against your real traffic patterns, not synthetic benchmarks. You get a prioritized, quantified findings map.
Weeks 3-10: Build (optional)
If you proceed, we implement the highest-leverage fixes directly against your live stack — typically 4-8 weeks depending on scope.
Ongoing: Monitor
Performance tuning as volume, tenant count, and model choices evolve — so the savings don't quietly erode back to linear.
AI Infra Cost Audit: Frequently Asked Questions
By fixing architecture, not by serving a worse model. Chunked prefill and speculative decoding change how compute is scheduled, not what the model produces. Model routing sends simple work to simple models and keeps hard work on strong models. None of it trades correctness for savings.
Are your token and DB costs scaling linearly with growth?
Tell us your current stack and growth curve. We'll scope a fixed-price, 2-week diagnostic and show you what it's worth fixing before you commit to anything.
Get a Cost Diagnostic