1. It is slow
Show the answer as it is produced. Send less text. Use a smaller, faster model for easy tasks. Run independent calls at the same time instead of one after another.
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Fixing AI problems in a SaaS product.
Why AI features that work in a demo struggle with real customers, and the practical fix for each problem: speed, cost, wrong answers, data mix-ups and silent regressions.
Rajesh Ayyavu, TenlyrUpdated 2026-11-03Where does your AI feature hurt?Tap one
Pick the symptom. The matching problem lights up below.
The short answer
Most AI problems in a SaaS product are engineering problems, not model problems. A demo works because it is small and watched. In production you need limits on cost, timeouts and fallbacks for failures, per-customer data filtering, a fixed test set so changes do not make things worse, and logs that show what happened. Fixing these usually makes an AI feature faster, cheaper and more trustworthy without changing the model.
Mostly engineering
Not model problems.
Seven known problems
Speed, cost, accuracy, data, regressions, outages, visibility.
Known fixes
No need to wait for a better model.
Common AI problems in SaaS, and the fix for each
Each one shows up only after real customers use the feature.
2. It costs too much, or the bill jumps
Cache repeated answers. Cap input and output size. Route easy requests to cheaper models. Track cost per customer so you can see and limit heavy use.
3. It gives wrong or invented answers
Give the model the right source material to answer from. Tell it to say when it does not know. Add a human check where mistakes are costly.
4. It could show one customer another’s data
Filter by customer when you fetch the documents the model will see, not after. Test this the same way you test the rest of the product.
5. Changes make it worse without anyone noticing
Keep a fixed set of real questions with good answers and run it before and after every prompt or model change.
6. It breaks when the AI provider is slow or down
Set timeouts, retry sensibly, and decide what the product does when the model is unavailable. A clear message beats a spinner that never ends.
7. Nobody can tell what happened
Log the request, the model used, the cost and the result per customer, with personal data removed. You cannot fix what you cannot see.
Why a demo is not a product
A demo
- One person
- A few good questions
- Everyone is watching
Production
- Thousands of people
- Unexpected questions, at the same time
- With their own data, and nobody is watching
The distance between the two is mostly engineering: queues, limits, filtering, testing and monitoring.These are known problems with known fixes. They do not depend on waiting for a better model.
A practical order of work
- Add logging and per-customer cost trackingSo you can see what is happening.
- Build the fixed question setAnd measure answer quality.
- Fix the biggest cost and speed causesAdd timeouts and fallbacks.
- Before scaling upCheck customer data filtering end to end.
Tenlyr helps teams take AI features from demo to dependable. If you want a second pair of eyes first, our architecture review includes AI features, or see what we solve.
Keep reading
Why is my app slow when users increase?Six causes of slowdowns as you grow, how to find yours, and the fix for each.How to build a SaaS that scales without a rebuildFive early decisions that decide whether you rebuild later.How to find problems in your SaaS before customers doFive checks you can run this week, and what your own team cannot see.
Written by Rajesh Ayyavu, founder and software architect at Tenlyr. He reviews SaaS products and fixes backend, database and AI problems. Last updated 2026-11-03. LinkedIn
Common questions
Why is my AI feature slow?
Common causes are a very large prompt, waiting for the whole answer before showing anything, calling the model for work that does not need it, and making several model calls one after another. Showing the answer as it is produced, trimming what you send and using a smaller model for easy tasks usually helps most.
Why is my AI bill so high or unpredictable?
Cost grows with the amount of text sent and received and the model used. Without limits and per-customer tracking, a few heavy users can drive most of it. Cache repeated answers, cap input size, route easy requests to smaller models and track cost per customer.
How do I stop an AI feature giving wrong answers?
You cannot remove every error, but you can reduce them: give the model the right source material, tell it to say when it does not know, and test with a fixed set of real questions every time you change the prompt or model. For risky outputs, add a human check.
Can AI mix up one customer's data with another's?
It can if retrieval or context is not filtered per customer. Apply the customer filter when fetching the documents the model sees, not afterwards, and test it the same way you test the rest of the product.
How do I know if a prompt or model change made things worse?
Keep a set of 30 to 50 real questions with known good answers and run it before and after every change. Without it, you find regressions through customer complaints.
Is an AI feature slow, costly or wrong?
Take the free 30-question SaaS health checklist to see where the risk is, or request an Architecture Review. Rajesh replies personally within 2 business days.