We helped a growing AI product cut unnecessary model spend and respond faster. Each task now goes to the model that suits it, repeated work is reduced, and cost is tracked by feature and workflow.
Industry: SaaS & TechnologyService: AI & AutomationUpdated
Overview
The challenge
As more people used the product, AI costs became harder to predict. Expensive models were handling tasks that didn't need them, and some requests took longer than users expected.
The company didn't want to cut back what the AI could do. It wanted each task to use the right level of model, and it wanted to know exactly where cost and response time were coming from.
High-capability models were used even for simple requests.
Long chains of processing steps kept users waiting.
The team could see the total bill, but not which features were behind it.
The solution
We tracked model usage, token use, processing time, retries and AI activity for each feature.
Lighter models now handle simple tasks. Stronger models are used for complex tasks, and only when they are needed.
Caching, better prompts, fewer unnecessary calls and trimmed context reduced avoidable model usage.
Before and after
Before
One model for everything
Most tasks went to the same expensive model.
After
Routing by task
Each workflow uses the level of model it actually needs.
Before
A monthly total only
The team had no breakdown of cost by feature.
After
Cost by workflow
Spend is broken down by feature and workflow, so it is easier to understand and control.
Before
Long chains
Some requests went through processing steps they didn't need.
After
Leaner workflows
AI calls and context are cut back wherever possible.
Key features
The system picks a model based on how hard the task is, how risky it is and how fast the answer needs to be.
Business impact
Less wasted spend
The team can see which workflows and features use the most AI resources.
Business impact
Tighter budget control
Fewer calls, better prompts and caching all help responses come back sooner.
Business impact
A better experience for users
Results
Better Cost Control
The team can see which workflows drive model spend, rather than just the monthly total.
Before: Monthly BillAfter: Feature Detail
Faster AI Responses
Processing steps that weren't needed have been removed, so users wait less.
Before: SlowerAfter: Optimised
More Efficient Model Usage
High-cost models are kept for the tasks that genuinely need them.
Before: OverusedAfter: Task Based
Faster delivery
Starting from these tested components saved setup time, so the work went into routing and cost tracking for this product and shipped sooner.
Our access controls keep the AI to the information and actions approved for each user and workflow.
The workflow layer gave us one place to add model routing, caching and leaner prompts across the product's AI features.
Tested monitoring tracks tokens, retries and response times for each feature, which gives the team its cost-by-feature view.
A ready-made review step sends important or unusual cases to a person before any sensitive action is taken.
Trust and control
The AI can only reach the information and actions approved for each user and workflow.
ACCESS CONTROLLEDImportant or unusual cases can go to a person before any sensitive action is taken.
HUMAN IN CONTROLKey AI actions and their outcomes can be recorded, so the team can see what happened.
TRACEABLERules, prompts, workflows and controls can be adjusted as usage patterns change.
ADAPTABLECosts rose because expensive, high-capability models were handling almost every task, including simple requests that didn't need them. Long processing chains also slowed some responses. The team could see the monthly bill but not which features drove it, so there was no clear way to control spend as usage grew.
We routed each task to a suitable model, so lighter models handle simple work and stronger models are used only for complex tasks. We also added caching, improved prompts, removed unnecessary calls and trimmed the context sent to each model. The aim was to keep the product's AI capability while cutting avoidable usage.
We track model usage, token use, processing time, retries and AI activity for each feature. The team now sees cost broken down by workflow and feature instead of one monthly total, so they can spot which parts of the product use the most AI resources and tune those first.
If your model bill keeps rising and you can't tell which features cause it, we can measure usage, route tasks to the right models and cut repeat calls without reducing what your product can do.