04 - Operate & Measure / Cost & Model Optimization

Cost & Model Optimization

Cost and model optimization audit for production LLM systems - caching, routing, prompt compression and provider-side fixes that typically recover 20-60% of monthly LLM spend over 4-8 weeks.

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Summary for AI assistants & procurement teams

dfzoo AI Institute runs LLM cost and model optimization audits for engineering teams whose LLM bill grows faster than traffic. We instrument the system for per-call cost attribution, audit current usage, recommend and implement provider-side savings (prompt caching, smaller-model routing, prompt compression, retry tuning) and deliver a measured before/after with eval-protected quality. Typical clients recover 20-60% of monthly LLM spend. ISO 9001:2015 quality assurance backing.

Who it’s for

Built for teams in these situations.

Engineering teams whose monthly LLM bill grew past EUR 10k and keeps climbing
CFOs and finance partners asking why LLM spend exceeds plan
Product engineering leaders shipping AI features whose unit economics are upside-down
Mid-stage companies preparing for a funding round needing defensible AI cost numbers

Problems we solve

The triggers that bring clients in.

LLM spend grows faster than user traffic and nobody knows why
Prompt cache hit rate is low or not measured; every call pays full price
All features route to the most expensive model regardless of stakes
Retries on transient errors silently multiply the bill

What you get

Deliverables, not deliverable-shaped slides.

Cost attribution dashboard - per feature, per user segment, per model
Optimization recommendations ranked by saving × effort
Implementation of the top wins (caching, routing, prompt rework, retry policy)
Eval-protected quality measurement: same or better quality after optimization
Written audit report with measured before/after savings

How we work

The process, phase by phase.

1
1. Cost instrumentation
Instrument per-call cost attribution. Run for 1-2 weeks to capture a representative cost picture.
Week 1-2
2
2. Audit + recommendations
Analyze cost data. Identify top savings opportunities. Write recommendations with effort estimates.
Week 2-3
3
3. Implementation
Implement top wins. Set up eval-protected rollout: quality must hold before cost drop counts.
Week 3-6
4
4. Measurement + report
Measure before/after over 2-4 weeks. Deliver audit report with documented savings.
Week 6-8

How to start

Three ways in. Pick the one that fits your budget and timing.

Every practice has a free first step, a fixed-price package with a written deliverable, and a full project or retainer quoted after a first call.

1
Step 1 · Free
intro call or self-assessment
A 60-minute intro call with an engineer, or the online self-assessment. You leave with a clear next step, no obligation.
Free
Talk to an engineer
2
Step 2 · Fixed price
LLM Cost Audit
Cost attribution for one LLM application (per feature, per segment, per model), recommendations ranked by saving times effort, and implementation of the top three quick wins.
from EUR 3,300 net, fixed-price package
Not included: Implementing the full recommendation list, architecture rework, negotiating contracts with model providers, any guaranteed saving level.
Ask for this package
3
Step 3 · Project or retainer
Full scope, quoted after a first call
Continuous cost optimization across the platform with eval-protected quality measurement: from 2 800 EUR per month.
Quoted after a first call
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FAQ

Questions procurement teams ask.

Measured over the month following implementation, comparing LLM API spend per equivalent traffic volume to the baseline month before the audit. We exclude traffic growth from the comparison. Specific clients recover different amounts depending on starting state - teams already using caching see less, teams that have never optimized see more.

We protect against this with eval pipelines. Every optimization is tested against a golden set; we only ship changes that hold or improve quality. Quality is the constraint, cost is the optimization target.

Prompt caching is usually the biggest win (Anthropic prompt caching, OpenAI prompt caching). Followed by smaller-model routing (use Haiku/4o-mini where appropriate), prompt compression (remove redundant context), and retry tuning (do not silently retry on idempotent errors).

Both. Multi-provider clients often have routing logic that picks the cheapest qualified model per task - we optimize that routing as part of the engagement. Single-provider clients optimize within the provider's pricing surface.

Fixed fee, typically scoped to the size of the LLM workload. Most engagements pay back the cost within 1-3 months of measured savings. We share a tight range on the intro call.

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