AI Cost Optimization Services
Crescent AI attributes AI spend by model, provider, agent, and customer down to the individual request, then tunes caching and routing so the bill matches what the system actually needs to do — not a blanket switch to cheaper models.
Performance tuning, monitoring, and incident response sit with the broader AI Operations & Optimization engagement this page is scoped out of.
A Bill You Can Explain, Not Just Pay
A single monthly total from a provider invoice tells you nothing about why it moved. We engineer the attribution, caching, and routing that turn AI spend into something you can break down by model, feature, and customer, and actually act on.
What is AI cost optimization?
AI cost optimization is the practice of attributing AI spend by model, feature, and customer down to the individual request, then reducing it through caching, model routing, and usage controls without cutting the quality a system actually needs.
AI Cost Optimization is the cost-specific slice of the broader AI Operations & Optimization discipline, which also covers performance tuning, monitoring, and incident response. In practice the two overlap — caching cuts both cost and latency — but this page is scoped to the cost side specifically: what's driving spend, and what to change about it.
Recognize the symptoms
When You Need AI Cost Optimization
If two or more of these are already true, this isn't a tuning problem.
- Usage costs climb with no way to tell which model, feature, or customer is driving it
- Nobody can say whether a cost spike is retries, a backup provider kicking in, or actual growth
- The same question gets sent to the model repeatedly with no caching
- Cost is tracked only as a single monthly total on the provider invoice
What We Optimize
Five parts, engineered together, not a single switch to a cheaper model.
Cost Attribution & Tagging
Every request tagged with model, provider, agent, feature, and customer, so a cost spike can be diagnosed instead of just noticed on the invoice.
Token & Context Usage
Trimming what actually gets sent to the model, so a request doesn't carry more context than the task needs.
Semantic & Prompt Caching
Reusing responses for questions that mean the same thing, not just ones that match character for character, cutting repeat calls out of the bill entirely.
Model & Provider Routing
Cheaper, faster models for simple lookups and classification; stronger models reserved for the parts of a task that actually need them.
Retry & Failure Cost Controls
Retries built to be safe and bounded, so a flaky tool or provider outage doesn't quietly multiply the bill while it's failing.
Cost Wins We've Delivered
Concrete changes, not a general promise to make the bill smaller.
Per-Customer Cost Attribution Dashboards
A live breakdown of spend by model, feature, and customer, replacing a single monthly number nobody can explain.
Semantic Caching for Repeated Queries
A caching layer that recognizes when two different wordings ask the same question, cutting duplicate model calls out of production traffic.
Model Routing by Task Complexity
Simple classification and lookup tasks routed to smaller, cheaper models automatically, with the expensive model reserved for what actually needs it.
Retry Storm Elimination
Bounded, safe retry logic that stops a single failing dependency from silently multiplying request volume and cost.
Context Window Trimming
Prompts and retrieved context cut down to what the task requires, reducing token spend on every single request, not just the expensive ones.
What You Receive
From our experts.
Writing on production AI cost and operations.
Common questions.
Related Engineering Services
Bring us the bill nobody can explain.
Whether it's a single spike nobody's diagnosed, or a system that's just been getting more expensive every month with no clear cause, we'll walk through where the spend is actually going before we recommend anything.
No hype · No forced roadmap · Just a clear view of what's driving cost