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 answer over and over.
AI Optimizer is a local caching proxy for OpenAI- and Anthropic-compatible 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 run works
The proof uses a deterministic scheduled AI job that sends the same known request through AI Optimizer and expects the same known response: CACHE_TEST_OK.
Current proof setup
Deterministic scheduled requests, visible local stats, and a live browser hit counter
- the request repeats every 15 minutes
- the TTL is set to 1 hour to match the repeat pattern
- 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 instead of paying full price every time.
Proof, not promises
This page is built around a deterministic scheduled AI job, a chosen TTL, visible cache-hit stats, and a browser popup that shows the running totals locally. As of July 3, 2026, 209 of 610 requests were served locally — a 34.3% hit rate.
Visible cache-hit proof
In the current proof run, 209 of 610 requests were served locally — a 34.3% hit rate, visible as it happens instead of guessed from a bill later.
Real scheduled workflow
A deterministic proof run is scheduled every 15 minutes, so the repeat behavior is automated and inspectable.
Intentional TTL setup
The cache window is chosen to fit the workflow instead of relying on provider defaults or vague behavior.
"Don’t OpenAI and Anthropic already cache?"
Fair question. They do — 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.
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