TokenAtlasIllustrative example · Seed / Series A SaaS scenario
How a SaaS startup reduced AI costs by 38% in 14 days using TokenAtlas
This is an example scenario, not a customer story. It walks through how a small SaaS engineering team could use AI cost intelligence to model their workloads, find where spend concentrates, and compare cheaper model strategies before committing to them.
“NovaStack” is a composite team profile. Every figure below is a modelled estimate produced with TokenAtlas calculations — no provider accounts, invoices, or customer data were involved.
Illustrative example. This scenario is a composite built from modelled TokenAtlas calculations, not a named or verified customer. The company, figures, and quotes are illustrative, do not represent verified customer results, and should not be read as a performance guarantee.
The challenge
Rapid AI development, uncontrolled spend
In this scenario, the team has shipped summarization, semantic search, and an in-app copilot. Each feature works. Together, they make the monthly API invoice difficult to explain — spend grows every sprint, but nobody can attribute it to a feature.
Illustrative Example Quote“We were shipping AI features, but had no idea what they were costing us.”
- AI API costs grow unpredictably, with no clear link to product usage or revenue.
- Without feature-level cost modelling, engineering cannot tell which capabilities drive spend.
- Finance sees monthly billing surprises, which makes forecasting and board reporting difficult.
- Engineers lack the data to weigh prompts, models, or retry behaviour before costs rise.
Before TokenAtlas
Cost tracking is fragmented and reactive
No cost attribution per feature
A provider dashboard shows a single aggregate cost line. The team cannot map spend back to specific features or product areas.
Limited endpoint visibility
Cost per API route is unknown. A misconfigured background job or retry loop can inflate the bill for days before anyone notices.
Manual tracking
A spreadsheet maintained by one engineer is the only source of truth. It is incomplete, error-prone, and always several days out of date.
Difficult optimization decisions
Model and architecture choices are made on intuition, because nobody can compare the cost of the alternatives side by side.
The solution
Model the workloads before the invoice arrives
In this scenario the team spends an afternoon describing its AI workloads in TokenAtlas — request volumes, prompt and completion sizes, and the models behind each feature. TokenAtlas prices those workloads against its maintained model catalog, so cost becomes a number the team can reason about upfront.
Feature-level cost modelling
Each workload is described separately, so modelled cost can be attributed to a feature instead of hiding inside one aggregate figure.
Cost visibility across scenarios
Modelled cost and token volume per feature can be reviewed at any time, rather than waiting for the billing cycle to reveal them.
Model comparison
Side-by-side model economics show where smaller, cheaper models could replace GPT-class calls, so the team can test the trade-off before changing code.
Cost strategy comparison
Alternative prompt sizes, retry policies, and routing strategies are compared as scenarios, making the expensive patterns obvious early.
TokenAtlas models costs from the workload inputs you provide. It does not connect to provider accounts, ingest live usage, or collect API keys.
Results
38% lower modelled AI spend over a 14-day process
Illustrative Outcome
Illustrative outcome across the compared workload scenarios.
Every modelled workload attributable to a feature rather than one aggregate line.
High-cost workflows identified during modelling instead of at month-end.
Model and architecture choices compared on modelled cost, not assumptions.
- Two GPT-class workloads were modelled against smaller models, showing a large cost gap worth testing for quality.
- A high-volume retry pattern was identified as the single largest modelled cost driver.
- Finance gained a modelled 30-day forecast to plan against, instead of reacting to invoices.
- New AI features could be costed against expected volume before being built.
Illustrative outcome. These figures come from modelled scenarios in this example, not from measured customer results. Your own savings depend on your workloads, models, and usage patterns.
Start tracking AI costs in under 10 minutes.
Model your own workloads and see where your AI spend concentrates — before your next invoice arrives.