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Stop Overpaying for
AI

Most companies use the most expensive model for everything. We route simple tasks to cheaper models—or open-source alternatives—implement caching, and cut your LLM bill by 50-80%.

4Weeks to live
ProductionGrade, not demos
EUHosted · Utrecht, NL
Operations

Positioning

The Hidden Cost Problem

Developers default to the most powerful (and expensive) model for every task. A simple FAQ lookup costs the same as a complex analysis.

Prompts are bloated. Identical queries hit the API repeatedly.

There's no visibility into what's actually being spent. We fix all of that.

Start with the bottleneck before tooling
Proof before scale or transformation
Decisions based on operational reality

Outcomes

What You Get

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

Our Services

From quick audit to full optimization

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

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

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

Approach

Our Approach

Fast, practical, measurable results

Step 1

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 2

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 3

3. Optimize & Implement

We implement quick wins first: caching, model routing, prompt trimming. Then deeper optimizations. You see savings within weeks.

Step 4

4. Monitor & Maintain

We set up dashboards and alerts so you stay optimized. Costs stay low. New inefficiencies get caught early.

What we've achieved for clients

Results

Not lab demos or isolated pilots, but working applications inside live processes that materially improve speed, quality, and operational handovers.

Insurance Company — Claims Processing

Problem

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%.

€5,600Monthly savings
70%Cost reduction
2 weeksImplementation time
View case

Law Firm — Document Analysis

Problem

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.

€3,000+Monthly savings
75%Cost reduction
Full dashboardVisibility
View case

FAQ

Frequently Asked Questions

The most practical questions that usually come up before a first application actually lands in the operation.

First serious step

Ready to Cut Your AI Costs?

Plan an AI Opportunity Scan. We show you where cost, routing, and model choice can be tightened first.

Included in the first conversation

First assessmentCost visibilityClear next step
Start with one process. Leave with a sharper first route.
First step

Ready to Cut Your AI Costs?

Plan an AI Opportunity Scan. We show you where cost, routing, and model choice can be tightened first.

Response time

We typically respond within 24 hours

AI Cost Optimization - Cut Your LLM Bill by 50-80% | Laava