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Cost Audit
We trace every LLM call, analyze usage patterns, and identify exactly where money is wasted. You get a prioritized report with concrete savings opportunities.
LangfuseLiteLLMCustom analysis
Request auditCut your AI spend, not your output.
Most teams run the most expensive model for everything. We trace where the money goes, route simple work to cheaper or open-source models, cache repeated calls, and put cost controls in the runtime, so the bill drops without the quality.
One workflow to production. One platform to build on what works.
Positioning
Most AI bills are bigger than they need to be.
The most powerful model gets used for every task, so a simple lookup costs as much as a hard analysis.
Prompts are bloated, identical queries hit the API again and again, and nobody can see what is actually being spent.
Laava makes the spend visible and brings it down, inside the platform your agents already run on.
- Start with the bottleneck before tooling
- Proof before scale or transformation
- Decisions based on operational reality
Outcomes
What the optimization gives you
Complete cost breakdown per feature, user, and model
Smart model routing, right model for each task
Prompt caching (up to 90% savings on repeated context)
Budget alerts before costs spiral
Ongoing monitoring dashboard
Concrete recommendations you can implement immediately
Three ways we take cost out
From a scoped audit to routing, caching, and controls in the runtime.
02
Model Routing
We implement intelligent routing: simple queries go to fast, cheap models (GPT-4o-mini, Haiku) or self-hosted open-source models (Llama, Mistral). Complex tasks stay on flagship models. Same quality, fraction of the cost.
LiteLLMCustom routing logic
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Continuous Monitoring
Real-time dashboards showing cost per feature, per user, per day. Budget alerts. Anomaly detection. Never be surprised by your AI bill again.
LangfuseSentryCustom dashboards
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How the optimization runs
Trace first, fix the biggest waste, then keep it low.
Step 01
1. Trace & Measure
We instrument your LLM calls with Langfuse tracing. Within days, we have complete visibility into every API call, token count, and cost.
Step 02
2. Analyze & Identify
We find the waste: oversized prompts, wrong model choices, missing caching, duplicate queries. We quantify exactly how much each issue costs.
Step 03
3. Optimize & Implement
We implement quick wins first: caching, model routing, prompt trimming. Then deeper optimizations. You see savings within weeks.
Step 04
4. Monitor & Maintain
We set up dashboards and alerts so you stay optimized. Costs stay low. New inefficiencies get caught early.
Measured on real production workloads.
Results
Three examples of AI making customer contact, document work, and knowledge processes faster, more consistent, and easier to control.
Insurance Company. Claims Processing
A mid-sized insurer was spending €8,000/month on flagship models for claims intake. We discovered 85% of queries were simple classification tasks. By routing these to GPT-4o-mini and a self-hosted Llama model, we cut costs by 70%.
- Monthly savings
- €5,600
- Cost reduction
- 70%
- Implementation time
- 2 weeks
Law Firm. Document Analysis
A growing law firm had €4,000/month in LLM costs with zero visibility. Our audit revealed duplicate queries (same documents analyzed repeatedly) and no prompt caching. After optimization, costs dropped to under €1,000/month.
- Monthly savings
- €3,000+
- Cost reduction
- 75%
- Visibility
- Full dashboard
FAQ
What teams ask about AI cost
The most practical questions that usually come up before a first application actually lands in the operation.
View all questionsHow quickly will I see results?
Most clients see measurable savings within 2 weeks. The audit itself takes 3-5 days once tracing is set up.
Will quality suffer if I use cheaper models?
No. We only route tasks that don't need the most powerful model. Complex reasoning stays on flagship models (GPT-4o, Claude Sonnet). Simple lookups go to smaller models or open-source alternatives like Llama and Mistral. Same output quality.
What if my costs are already low?
We'll tell you. The audit shows exactly where you stand. If there's no room for optimization, you'll know, and you'll have the visibility to keep it that way.
Do you work with any LLM provider?
Yes. OpenAI, Anthropic, Azure, Google, and open-source models like Llama and Mistral, we're model-agnostic. We often help clients switch expensive API calls to self-hosted alternatives.
First step
See where your AI spend is leaking.
Bring one AI workload and its bill. We show you where cost, routing, and model choice can be tightened first.
Response time
We typically respond within 24 hours