Your GenAI costs are growing.
Do you know which endpoint is driving them?
Rogue Wave makes GenAI cost per usable output measurable and optimizable. A four-week audit on real workloads, with a concrete re-architecture backlog and euro impact per patch.
API costs are growing faster than usage, product value and revenue contribution.
Many production GenAI stacks were built fast but never optimized for unit economics. The result: API costs grow faster than usage, product value and revenue contribution.
Teams often see the total bill, but not which endpoint, use case or architecture decision is causing the cost. That is exactly where optimization gets lost.
Where money disappears in production stacks
Almost every GenAI stack not explicitly built for unit economics shows the same five patterns. Each harmless on its own — together, four- to five-figure monthly bills with nothing to show for them.
Prompt Bloat
System prompts with several thousand tokens run with every request, even when large parts are redundant.
Wrong Model Choice
Top-tier models handle tasks smaller models could solve at a fraction of the cost.
No Caching
Recurring prompts, reloads and variants are recomputed every single time.
Decentralized Growth
Teams build features with different models, without central spend attribution.
Missing Transparency
Standard integrations rarely show which endpoint is actually driving the bill.
From diagnostic triage to ongoing optimization in four stages
Each stage stands on its own. The entry point is a low-risk diagnosis. You only activate the following stages once the potential holds up.
AI Spend Triage
Low-risk diagnosis: is there enough measurable potential for a full audit? Credited toward the audit.
Unit Economics Audit
Benchmarking against 4–6 alternatives. Prioritized patch backlog with euro impact, effort, risk and quality effect.
Implementation
Implementing the patches with your engineering team: routing, caching, prompt slimming, vendor mix.
Cost Monitor
Basic: tracking. Pro: alerts and drift. Managed: review call and an ongoing optimization backlog.
We discuss the investment range for each stage in the fit check. We quote binding prices once use-case volume and delivery mode are clear.
What an audit actually delivers
No strategy PDF, no dashboard without consequence. A prioritized patch list that translates directly into engineering work.
| Patch | Effort | Risk | Potential | Quality |
|---|---|---|---|---|
| Summary endpoint: Opus → gpt-4.1-mini | Low | Low | €4,800 / month | Neutral |
| System prompt: trim 4,200 → 1,600 tokens | Medium | Low | €2,100 / month | Neutral to positive |
| Semantic cache for recurring RAG queries | Medium | Medium | €7,500 / month | Neutral |
| Batch mode for offline processing | Low | Low | €1,300 / month | Neutral |
| Fallback router: top tier only on low confidence | High | Medium | €9,000 / month | Neutral to positive |
| Total potential across five patches | €24,700 / month | approx. €296K / year | ||
The example shows identified savings potential, not guaranteed realization. A patch only counts if defined quality, latency and stability thresholds are maintained.
We don't optimize token cost. We optimize cost per usable output.
A patch only counts if it improves, or at least maintains, the following five dimensions.
€ per 1,000 accepted outputs
Eval score, accuracy, completeness, hallucination risk
p50 / p95 response time
Failure rate, retry rate
Cache hit rate, routing distribution, cost drift
We're not built for everyone.
For the audit ROI to add up, volume and setup need to match. Here's an honest look at when we make sense and when we don't.
Companies with a GenAI feature in production
€10,000–30,000 / month API spend or clearly growing volume. Own GenAI features in production, not pure SaaS usage.
Production RAG, in-house copilots, AI features
From €30,000 / month API spend. Own SaaS products with AI components. Entry point: CTO, Head of AI, VP Engineering.
When an audit doesn't pay off
Pure SaaS users without their own implementation, under €10K / month AI spend, enterprises with their own AI FinOps practice.
Tools show where costs occur. We show which architecture decision causes them and which patch reduces them.
| Category | Their strength | What we do differently |
|---|---|---|
Observability tools Helicone, Langfuse, LangSmith | Token tracking, dashboards | We deliver concrete architecture patches, not just dashboards. |
AI FinOps vendors Vantage, CloudHealth | Cloud FinOps with AI as a module | LLM-native: models, routing, prompts and caching are our core craft. |
Big4 / McKinsey AI Practice Strategy consulting €200K+ | Full transformation programs | Fixed-price, technically deep, accessible to the DACH mid-market. |
Four delivery options, depending on your security level
Depending on your security requirements, we work with pseudonymized logs, in your cloud, via a telemetry proxy without payload storage, or with curated test sets. We settle on the right option in a pre-call.
Audit methodology built on our own production stack
With LLM BrandView we built a production multi-model system ourselves: OpenAI, Anthropic, Google, Serper grounding, Supabase, caching, scan architectures and automated synthesis pipelines. From that practice came a repeatable audit methodology.
Free 30-minute fit check.
No log data required. Afterwards you'll know whether an AI Spend Triage makes sense in your case, and if so, with what expected leverage.