AI app for it and development · no coding needed
Observability retention optimizer
Control observability cost without discarding required evidence.
Made for: SaaS infrastructure teams
What it does for you
The problem
Log retention costs grow without evidence of diagnostic value.
What it gives you
Engineer-approved retention experiment
What you give it
Sanitized telemetry metadataapproved retention constraints
How it works, step by step
- Map diagnostic use cases
- Simulate retention tiers
- Compare retrieval coverage and cost
- Compare the reviewed result with the recorded baseline and value assumptions
- Capture corrections and named-owner approval before consequential use
- Export a versioned engineer-approved retention experiment with source references and unresolved questions
Build it yourself with your AI system
Build this app yourself, no coding needed
Start with a quick version you can try in a few minutes. Like it? Then build the full app by copying and pasting our step-by-step instructions: everything is prepared for you.
Sign in to see how to build it yourself
Build a quick version to try, or get the full app pack for Observability retention optimizer with the step-by-step building instructions. You don't need any technical skills: you copy, paste and answer a few questions. Both are included in the membership.
4 Have it built for you days to a few weeks
Rather not do it yourself, or want it fully tailored to your data, your way of working and your brand? Nexibeo builds Observability retention optimizer with you.
What's in the app pack
Included in the Complete AI Training membership.
- The building instructions your AI follows, step by step
- The questions your AI will ask you about your business before it starts
- A clickable demo you can open in your browser, to see how it should work
- A detailed blueprint of the screens, the information it keeps and the checks it runs
Become a member to get the app packAlready a member? Sign in
The files, for the technically curious
- START-HERE.mdHow to build it with your own AI (read first)3 KB
- README.mdOverview and links1 KB
- questions.mdQuestions to answer before you build3 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare26 KB
- prompt-vps.mdThe same build on your own server (Docker)26 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria14 KB
- demo/index.htmlThe working demo on sample data197 KB
Questions
Do I need to know how to code?
No. You copy and paste the prompts on this page into ChatGPT or Claude, and the AI does the building. When it asks you something, you answer in your own words.
What does it cost?
The quick version, the app pack and the step-by-step instructions are for members: you pay the membership price, not a price per app (see the plans). Building the full app uses your own ChatGPT or Claude subscription. Putting it online is often cheap or no cost at the start, and your AI tells you before anything costs money.
How long does it take?
The quick version: about two minutes. The real app: an afternoon for a first version you can use, longer if you want every feature.
Can I change it to fit my business?
Yes. Tell your AI what to change in plain words, like “add a column for the price” or “use our logo and colours”. Or have Nexibeo build and customise it for you.
More detailsHow the AI works, safeguards and what to build first
Control observability cost without discarding required evidence
Confirm the buyer's problem and scope, collect sanitized telemetry metadata and approved retention constraints, then follow this sequence: 1. Map diagnostic use cases. 2. Simulate retention tiers. 3. Compare retrieval coverage and cost. Resolve uncertain cases with qualified reviewers, approve engineer-approved retention experiment, and measure verified storage savings minus lost diagnostic value and migration cost against a documented baseline.
How the AI works
Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. Planning only; legal and operational owners approve deletion policies. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Protect secrets, customer data and source code. Use controlled environments, technical review and a recoverable deployment process. Planning only; legal and operational owners approve deletion policies. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.
What to build first
Pilot scope: Planning only; legal and operational owners approve deletion policies. Implement one approved input format, a bounded representative case set and the first two task modules: map diagnostic use cases; simulate retention tiers. Support the third module with operator review: compare retrieval coverage and cost. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.
What it can connect to
Authorized repositories, technical documentation, application APIs and logs. Read-only operational exports, calendars and finance or inventory records as relevant. Start with plan exports and retain human approval for execution. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.
The screens in detail
Primary screens: Constraint and input setup, Scenario comparison, Decision and pilot tracker. Place editable drivers and constraints beside a clearly labeled scenario output. Include a baseline view, comparison chart or schedule, and an assumptions history. Let users trace a proposed quantity or date back to its inputs. Keep forecasts distinct from actual results. Make the task-specific outcome engineer-approved retention experiment visible beside its evidence, review state and value baseline.
