Cut Your AI Costs Without Cutting Quality
For teams with AI already in production. We find what each request costs, remove the spend that does not change the answer, and move you to newer models behind an evaluation gate, so the bill goes down and your users do not notice anything except, sometimes, faster replies.
AWS Partner · OpenAI Partner Network member
Technology Partners
Recognized by Clutch
Your Model Bill Grows Faster Than Your Usage
The first version of an AI feature is built to work, and nobody tunes what it costs. Agents make it worse: one user question can become a dozen model calls, each one carrying the full conversation and every tool definition. The monthly total is easy to see. What one answer costs, and which feature is spending the money, usually is not.
Meanwhile the models keep changing. Providers retire versions on a schedule and release cheaper ones that are often better at the same task. Moving without a way to measure quality is a gamble, so most teams stay put and keep paying for last year's model.
Where the Savings Come From
Each lever, with when it applies and when it does not.
Prompt Caching
Long system prompts, tool definitions and retrieved documents that repeat across requests can be cached, and the providers bill cached input at a fraction of the normal price. It pays off when the same prefix is sent many times. It does little for short, unique prompts.
Context Trimming
Agents and RAG systems tend to send more than the model needs: full chat histories, every retrieved chunk, tool schemas for tools the step never calls. We measure what each request carries and cut what does not change the answer.
Model Routing
Classification, extraction and short replies rarely need the largest model. A router sends simple steps to a small, cheap model and keeps the expensive one for the steps where it makes a measurable difference.
Model Upgrades
Newer models are often cheaper for the same quality, and older ones get retired on a schedule. We move you behind an evaluation gate, so a migration ships only when it scores as well as the model it replaces on your own requests.
Batch Processing
Work that does not need an answer in seconds, such as nightly enrichment, document backfills or evaluation runs, can go through the providers' batch APIs at a large discount. It is usually the cheapest change to make and the most often skipped.
Self Hosting
At high, steady volume, an open-weight model on your own GPUs can cost less than paying per token, and it keeps data inside your network. Below that volume it usually costs more once you count the engineering. We do the break-even for your numbers.
How We Measure It
Cost per task, at a quality score. Price per million tokens is the wrong unit. We measure what one support answer, one processed document or one agent run costs, and we only count a saving if the quality score on your evaluation set holds. Our post on what an LLM request actually costs in production shows the method across providers.
Tracing first. Every model call gets traced with its token counts, cost and latency, attributed to the feature and the customer that caused it. Most savings are obvious once that view exists. The multi-agent system we built for Typeform on AWS Bedrock AgentCore ships with OpenTelemetry tracing and an evaluation suite in CI, the same setup a safe model upgrade needs.
An evaluation gate on every change. Coded checks plus an LLM judge, run on your own requests, before and after each change. How we run evaluations at scale is in Agent Evaluation at Scale.
Cost per unit is what we report. On an e-learning content pipeline we cut the cost of producing each course module by 70% across more than 200 modules. On a voice assistant for Medicare patients, targeted latency work cut the gap between turns in its word games by about 58%. See the case studies.
Providers and Tools
We work with whatever you run today, and recommend whichever model wins on your evaluation.
How the Engagement Runs
Measure first, then change the things that pay for themselves.
Free First Look
30 minutes with Carlos, who founded Vindler. Bring your monthly bill and a rough idea of what each feature does. You leave knowing whether a review is worth it at your volume.
AI Cost and Model Review, One Week, $4,500
Cost per task, a ranked list of savings with the quality risk of each, an evaluation set built from your traffic, and the model retirement dates that apply to you. The fee is credited to the work.
The Changes, in Milestones
We make the changes worth making, biggest saving first, each one shipped behind the evaluation gate and measured on live traffic. Or your team makes them from the report.
Frequently Asked Questions
Start With Your Bill
Book a free 30 minute call. Bring last month's invoice from your model provider, and you leave knowing where to look first.
Tell Us What You Run
Which models and providers, roughly what you spend each month, and what changed recently. We reply within 24 hours.