Map the routing and extraction steps behind your product's AI features to right-sized models, and run your eval suite on both stacks before you switch.
Prefer email? hello@azitalabs.com, subject “Workload evaluation: SaaS product”
Your product team ships AI features that users love. Behind each one, a simple extraction or routing step runs on a frontier model on every request. Your customers ask where their data is processed, and your roadmap absorbs model changes and rate limits on someone else's schedule. You cannot tune what you rent.
An example workflow
Its email drafting and lead scoring features call a frontier model on every request. An evaluation could run the team's own eval set on right-sized models and compare pass rates, latency and total cost for each feature.
An example workflow
Resume parsing and job matching grow with every customer it signs. An evaluation could profile the parsing step, test smaller models against the team's evals, and set the total cost of each route side by side.
An example workflow
Customer contracts require documents to stay in the country. An evaluation could run the clause extraction step where those contracts allow and compare accuracy, latency and total cost with the current provider.
Every step is judged against your product's eval suite. A step that does not pass stays where it is.
Routing and extraction steps behind each feature are matched to models sized to them, and steps that need a frontier model keep it.
Each feature's total cost compared with what you pay today, so gross margin is visible before you switch.
Run your workload on capacity from Azita’s installations. Scope the workload and data requirements.
Discuss your workload on the fleetScope the room and workload together. Azita designs, installs and commissions the deployment.
Discuss an installation in your buildingWhere the work runs, which models are used, who has access and how long anything is kept are agreed in your proposal before any work starts. The Trust page sets out custody and residency by side.
Agree a representative job and the acceptance criteria. Compare the output, turnaround, data requirements and total cost before deciding.
Available to evaluate on your own workload.
On the Azita fleet, or on an installation in your building.
We'll review what you need to run and contact you to discuss the right setup.
There is no price list. Your proposal states what is being quoted, the commitment and what is included. Human review, software licences and integration are listed wherever they apply.
Add what you pay for today. It travels with your enquiry as a note, so the comparison starts from the same work.
Prefer to read first? How pricing works
Your eval suite decides. We map each step to the smallest model that meets your pass criteria and run the suite on both stacks side by side. A step that does not pass stays where it is.
Send the workflow behind one AI feature with its eval set and last month's usage. We agree the pass criteria, run the suite on both stacks, and set out pass rates, latency and total cost for each step.
Your evals run in CI, and you cut over step by step behind your own flags. Any step that does not pass on the new stack stays with your current provider.
On the Azita fleet, or on an installation in your own building. Where your user data is processed, who has access and how long anything is kept are agreed in your proposal before any work starts.
Proposal based on your workload and deployment. It states what is being quoted, the commitment and what is included, and lists human review, software licences and integration wherever they apply. There is no price list.
Term, what is included, exit and data handling at the end are set out in your agreement before you sign.
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Engineering and product
Agents, support, extraction and product features, tested on your own evals.
The page for every kind of work in this group, and how an evaluation works.
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AI agents for clients
Routing, extraction and formatting steps on right-sized models, tested on your client evals. Compare pass rates, latency and total cost first.
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Founder watching burn
The AI features your product relies on, compared on your actual workload. See output quality, latency and total cost before you switch.
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A production workflow and its eval set. We agree the pass criteria, then compare output, latency and total cost on the same input.
Arrange a workload evaluation