AI COST & EFFICIENCY AUDIT
An AI cost & efficiency audit for software houses, SaaS companies and teams running LLM APIs in production. In 10–15 days we show you where costs are growing, which processes are unprofitable, and which optimisations to ship first.
MARKET STATE · 2025
Hard data from Gartner, Menlo Ventures, MIT NANDA and Benchmarkit. The Polish market is no different from the global one — except very few people are measuring it yet.
DIAGNOSIS
↳ Tick the ones that sound familiar · 0/7
AUDIT SCOPE
Every audit combines a financial, technical and business lens — because the CFO and the CTO look at the same costs from two different angles.
Monthly AI spend, cost per provider, model, project, team, user, request, process. Subscription waste (inactive seats, duplicated tools) and operational waste (retries, agent loops, errors).
Model mix, input/output tokens, prompt length trends, context bloat, retry & error rates, latency, cache & routing potential, batch potential, RAG quality, observability.
Is AI really saving time? Cost of human work vs cost of AI. Output quality. Human review rate. Cost per useful output. AI ROI. Impact on revenue and margin.
AI cost per active user, per paying customer, per pricing plan, per power user. Whether current pricing covers variable cost. Whether the AI feature should be in-plan, paid add-on, usage-limited or consumption-based.
AI usage policy, approved-tools list, Shadow AI map, sensitive data in prompts, approval flow, budget limits, cost monitoring, AI cost ownership.
PACKAGES
Each package is a defined scope, defined timeline and defined deliverables. No „get in touch for a quote”.
For teams who don't yet know whether the problem is big enough for a full audit.
For companies spending meaningfully on AI, with AI in product, who need to quantify margin impact.
For teams post-audit who want to implement the recommendations — observability, routing, caching, policy.
Monthly cost control and optimisation. Three intensity levels.
BLOG
Step by step: how to split the AI bill into cost per model, per project and per user — and which three numbers reveal the real problem in 30 minutes.
40–85% inference cost reduction without quality loss. Concrete use-case → model pairs we ship first in every implementation.
Why 4% of users generate 38% of an AI feature's cost — and exactly what to change in pricing so it covers variable cost.
What actually gets cached, how to measure cache hit rate, and why „context bloat” costs companies tens of thousands monthly.
How to inventory AI subscriptions, set up an approval flow and appoint an AI cost owner — without becoming a control-freak gatekeeper.
CALCULATOR
Type in your monthly costs and revenue. We'll show how much of your margin AI is eating and how much could come back after the audit.
FAQ
Short answers to the questions CTOs, founders and CFOs ask before the first call.
A Mini Audit takes 3–5 working days; a Full Audit 10–15 days — from onboarding to report and findings presentation. Implementing the recommendations is a separate project, usually 3–8 weeks.
No. The audit works from invoices, billing data, API usage logs and request metadata. Code access can help the technical audit, but it is not a requirement — we also work purely from cost and observability data.
Concrete deliverables: an executive and technical report, an AI cost map, a unit-economics sheet, a prioritised list of 5–10 quick wins, and a 30/60/90-day roadmap. Everything is walked through in a wrap-up session.
Pricing is public: Mini Audit from PLN 4,900 net, Full Audit PLN 14,900–29,900 net. An audit typically pays back within 1–3 months of shipping the first quick wins — model routing alone can cut inference cost by 40–85%.
For software houses, SaaS companies and product teams running LLM APIs in production or with an AI feature in product. If monthly AI spend is more than a few thousand zloty, an audit usually makes sense.
The idea is the same — visibility, allocation and optimisation of variable cost — but AI has its own mechanics: cost per token, prompt caching, model routing, retry waste and AI-feature unit economics. Cloud FinOps covers none of them.
NEXT STEP
30 minutes. No strings. You leave with a clear answer: whether the audit makes sense and what savings potential is visible at first glance.