Where repetition shows up
- scripts rerunning the same task
- agents revisiting the same request patterns
- cron jobs repeating on schedule
- local testing replaying identical calls
- reusable AI workflows creating stable request structure
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Local exact-hit caching for repeat-heavy AI workflows
Your cron jobs are paying full price for the same request over and over.
AI Optimizer is a local caching proxy for repeat-heavy AI workflows. When a script, agent, or scheduled job repeats an identical request inside your chosen TTL, the response is served from your machine instead of being sent upstream again.
Change one base URL. Set a TTL that fits the workflow. Then watch the hit counter in local stats and the browser popup so you can prove the savings instead of guessing.
Repeated AI requests are everywhere
A lot of AI usage is not one-off chatting. It is repeat-heavy operational work that quietly sends the same or nearly identical requests again and again.
Why that matters
Even when a workflow feels dynamic, many requests are exact repeats at the API level. When that happens, paying full price every time is waste.
If a scheduled job runs every 15 minutes, that is 96 runs per day and 2,880 runs per month. When those requests are identical, repeat cost adds up fast.
Repeated AI work creates repeated cost.
How the proof method works
The proof lane uses a deterministic AI request that sends the same known input through AI Optimizer and expects the same known response: CACHE_TEST_OK.
Repeatable proof setup
Deterministic repeat requests, visible local stats, and a browser hit counter
- the same exact request is replayed on purpose
- the TTL is chosen to match the repeat pattern you want to test
- cache hits are visible in local stats
- the browser popup shows the same running totals for quick inspection
Adjust TTL to fit the workflow. For scheduled jobs and automations, AI Optimizer gives you a local, controllable caching layer that can be verified with repeatable inputs instead of guessed from a later bill.
What a clean proof pass looks like
The goal is not to chase a vanity number. It is to make one repeatable workflow visible enough that you can tell where the first request went and where the repeated one was served.
1. Route one real workflow through localhost
Pick one script, agent, or scheduled job you already run and point its OpenAI- or Anthropic-compatible base URL at AI Optimizer.
2. Choose a TTL that matches the repeat pattern
Set the cache window intentionally so the workflow has a fair chance to repeat inside the period you actually care about.
3. Run the same request again on purpose
The first request proves the upstream path works. The second identical request is the one that tests whether the result can be served locally instead.
4. Verify the hit in two places
The stricter target is the browser popup plus a simple curl /stats call. The currently staged support visuals are the popup and the local dashboard, which is honest today but not the same thing as the cleaner popup-plus-terminal pair.
Proof, not promises
This page is built around deterministic repeat requests, a chosen TTL, visible cache-hit stats, and a browser popup that shows the running totals locally. The point is not one magic number. The point is that repeat-heavy workflows can be tested, verified, and inspected on your machine.
The screenshots staged here today are the popup and the app dashboard. If we want the stricter matched pair later, the next capture adds a true terminal curl /stats still instead of pretending the dashboard image already fills that role.
Visible cache-hit proof
Local stats and the browser popup make cache hits visible as they happen, so you can inspect real repeat behavior instead of inferring it from billing later.
Dashboard stats you can inspect
The app dashboard keeps exact local hits, partial provider-side reuse, and reused tokens visible in one place, so the proof lane stays inspectable without leaning on stale scheduled-run screenshots. It is honest support for the current staged bundle, while a true terminal curl /stats still remains the stricter companion if we decide to capture it.
Intentional TTL setup
The cache window is chosen to fit the workflow instead of relying on provider defaults or vague behavior, which makes the proof method portable across different repeat-heavy tasks.
"Don’t providers already cache?"
Fair question. OpenAI, Anthropic, and Google Gemini all have provider-side reuse or caching behavior in some lanes, but that solves a different problem than exact repeated-request caching for repeat-heavy local workflows.
Provider-side caching
- discounts repeated input tokens, but every request still goes to the provider API
- output tokens are still billed on every call
- cache behavior and visibility stay provider-controlled
- short cache windows are not built for every repeat-heavy scheduled workflow
AI Optimizer exact-hit caching
- an identical repeat inside the chosen TTL is served locally instead of being sent upstream again
- you choose the TTL to fit the job
- hits are visible locally through stats and the browser popup
- built for scripts, cron jobs, agents, automations, and reusable prompt workflows
Provider-side caching doesn’t solve every repeat-heavy workflow. AI Optimizer is for the workflows where exact repeated local requests, chosen TTL windows, and visible proof still matter.
Built for repeat-heavy AI workflows
AI Optimizer is strongest where repeated AI work is already part of normal operations.
Scripts
Rerun the same prompts and tasks without paying full price every time.
Agents
Reduce waste from repeated agent loops, retries, and recurring reasoning patterns.
Cron jobs
Match TTL to scheduled jobs and turn repeat-heavy runs into measurable cache hits.
Automations
Cut repeat cost in recurring background workflows and operational pipelines.
Developer iteration
Support repeat-heavy testing, prompt refinement, and local AI-assisted development.
Reusable workflows
Reduce waste in standardized AI tasks like review, scaffolding, tests, and setup.
Best first workflows to test
If you want fast proof, start with a workflow that already repeats the same structure often enough to create a clean before-and-after result.
Scheduled summaries or reports
Recurring cron jobs, digests, and background summaries are strong first tests because the request shape is often stable and the repeat timing is easy to match with TTL.
Agent loops with fixed instructions
Agents that revisit the same setup, evaluation, or review path can create repeatable exact requests when the prompt structure stays controlled.
Prompt and script test harnesses
Local development runs, QA checks, and repeated prompt tests are ideal for proving cache hits quickly because you can intentionally replay the same request on demand.
What to keep stable during proof capture
Clean cache proof depends on repeatability. If too many variables change between runs, you learn less from the result.
Keep these fixed
- the exact request body
- the provider and model path
- the workflow step that sends the request
- the TTL window you are trying to validate
Avoid contaminating the proof
- do not inject timestamps or changing metadata into the request
- do not mix one clean proof pass with unrelated production traffic and call it the same thing
- do not treat a changing prompt as if it were an exact-repeat cache test
What AI Optimizer honestly will not do
AI Optimizer is not magic. It is strongest in a narrow, provable lane.
What it does not promise
- savings on requests that never repeat
- automatic wins on prompts that keep changing
- fake optimization where nothing measurable changed
- a reason to pay if your workflow is not repeat-heavy enough for caching to matter
Where it is strongest
It works best when requests are truly repeated, when the workflow runs often enough to make those repeats meaningful, and when you want visible local proof instead of hoping the economics work out.
How it works
Simple operational flow. No theory exercise required.
1. Point your workflow at AI Optimizer
Use AI Optimizer as the local endpoint for OpenAI- or Anthropic-compatible requests.
2. Run your normal workflow
Scripts, agents, cron jobs, and automations keep working as usual.
3. Get exact cache hits on repeated requests
When the same request appears again, AI Optimizer serves the cached result locally.
Try it on a workflow you already run.
Pick one script or cron job you already run. One base URL change, one TTL, one visible cache hit.
Start with the 14-day free trial. If it proves useful in one real workflow, AI Optimizer is $4.99 USD/month after trial.
OPENAI_BASE_URL=http://localhost:3000/v1