OptimaLLM

Install once, then forget it exists

Same coding assistant. Two thirds fewer tokens.

OptimaLLM runs quietly on your machine and rewrites every request your coding assistant sends before it leaves. Repeated context is collapsed, image payloads are dropped, repeat commands are served from cache, and each request is routed to the cheapest model that can actually handle it. On one measured session of 635 requests, token use fell by 67 percent.

Get in touch See how it works

Two minute install. Nothing about your workflow changes. Everything stays on your machine.

67%fewer tokens on a measured session

2 minto install, including auto-start

0changes to how you work

100%local, nothing sent anywhere new

The problem

You are paying to send the same thing over and over.

A long coding session repeats itself constantly, and every repetition is billed at full price.

Context is resent every turn

The same file, the same error, the same explanation travels upstream on every single turn of a conversation. You pay for it each time as though it were new.

Small tasks use big models

Renaming a variable and designing an architecture go to the same model at the same price. Most requests never needed the expensive tier at all.

Screenshots are enormous

A single pasted image can cost more than an entire conversation, and almost none of that payload is what the model actually needed to answer.

How it works

It sits in the middle and you never notice.

One install, one restart, and every request from then on is optimized on the way out.

Install it

One command builds the proxy and registers it to start automatically when you log in. It listens only on your own machine.

Keep working

Your assistant behaves exactly as before. Responses still stream in token by token, and nothing about your prompts, commands, or habits has to change.

Watch the savings

A stats command shows exactly where the reduction came from, broken down by optimization, so the number is auditable rather than a claim.

What changes for you

What actually changes on your machine.

Nothing you do differently. Quite a lot that stops costing what it did.

Every time you	How it works today	With OptimaLLM
Continue a long conversation	The entire history is resent and rebilled on every single turn.	Repeated blocks are replaced with a short reference to the turn that already carried them.
Rerun the same command	The command runs again and the full output is sent again at full price.	Deterministic output is served from a local cache. Anything that genuinely changes still runs fresh.
Paste a screenshot	Tens of thousands of tokens of image payload go upstream.	The payload is stripped before sending, which is where image-heavy workflows find 70 to 90 percent.
Ask something trivial	It goes to the same expensive model as everything else.	It is classified and routed to the cheapest model that can handle it, with the output length capped to suit.
Ask about your own codebase	You paste in the relevant files and pay for them every time.	Your project is indexed locally, so the context comes from your machine instead of your budget.

Where the savings come from

One session, broken down.

These figures come from the built-in reporting on a real 635-request session, not from a projection.

Baseline

295,107 tokens would have been sent without the proxy in place. That is the number every reduction below is measured against.

Actual

98,369 tokens were sent. The difference is roughly 67 percent, and the report attributes it optimization by optimization.

Biggest single win

Removing repeated text blocks from earlier turns, which is worth 55 to 80 percent on its own in conversation-heavy work.

Cost of the pipeline

Cold intent classification adds 100 to 200 ms, and a warm cache adds effectively nothing. Streaming is preserved, so responses still arrive token by token.

What you get

Thirteen optimizations, applied automatically.

Each one is small. Applied to every request of every session, they compound.

Stop resending the same text

Repeated blocks are replaced with a short reference to the turn that already carried them. The model keeps the meaning and you stop paying for the duplicate. Worth 55 to 80 percent on discussion-heavy sessions.

Repeat commands cost nothing

Deterministic commands are served from a local cache instead of being run and billed again. Commands whose output genuinely changes are excluded, so results stay correct.

Images stop being expensive

Image payloads are stripped before sending, which removes tens of thousands of tokens per image. Image-heavy workflows see 70 to 90 percent reductions.

Cheap work goes to cheap models

Each request is classified and routed to the least expensive model that can handle it, with output length capped to suit the task. Planning still gets the deep model and the thinking budget it needs.

It learns your codebase

Your project is indexed locally in the background, so relevant context can be supplied from your own machine instead of being pasted in and paid for repeatedly.

It notices when it got it wrong

If a cheaper model produces a weak answer, the proxy detects it and moves the next turn up a tier automatically, without interrupting the response in progress.

Who it is for

Worth most where usage is highest.

The heavier your day, the more of it OptimaLLM removes from the bill.

Individual developers

Long agentic sessions on the same codebase are where savings compound the hardest, because the cache and the index are both warm.

Full-day coding sessions
Repeat work on one project
Frequent screenshot pasting

Engineering teams

Reduce spend across a team without changing anyone's tooling, workflow, or habits. Nothing to roll out beyond the install.

No workflow migration
Per-session usage reporting
Daily budget warnings

Privacy-sensitive work

Indexing, caching, classification, and memory all happen on your machine. The only thing leaving is the request that was already leaving.

Nothing sent to third parties
Local index and cache
Auditable request logs

Under the hood

What it actually does to a request.

All of it local, all of it inspectable, all of it optional to keep.

InstallOne command, about two minutes, registers auto-start on login

FootprintA single local process, no account and no external service

LatencyUnder 200 milliseconds of processing overhead per request

StreamingPreserved end to end, responses arrive token by token as normal

Savings range15 to 90 percent depending on workload, 67 percent measured on one 635-request session

ReportingPer-optimization breakdown showing where your own savings came from

BudgetingDaily spend warnings at 80 and 90 percent, with a hard stop at 100

RemovalUnset one variable and everything goes back to normal

Questions

The things worth checking first.

Will my assistant behave differently?

That is the goal, and it is also the honest caveat. Streaming, tool use, and normal editing work as before, and cheaper routing is chosen only where the request does not need more. If you disagree with a routing decision, the daily stats show you exactly what happened.

Is any of my code sent somewhere new?

No. Indexing, caching, classification, and memory all run on your machine. The only outbound traffic is the request that was already going to your model provider, in a smaller form.

How do I know the savings are real?

A stats command reports actual token counts against the unoptimized baseline, broken down per optimization. The headline number quoted here came from that report on a real 635-request session, not from a projection.

What if I want to turn it off?

Unset one environment variable and your assistant talks to the provider directly again, exactly as it did before. Nothing about your project is modified.

Does caching risk stale results?

Only commands whose output is deterministic are cached, on a short expiry. Anything time-based, network-based, or test-related is explicitly excluded and always runs fresh.

What stage is the project at?

In daily use with the full pipeline working and regression tests passing. Known issues are documented openly, including memory growth over very long uptimes and control endpoints that are unauthenticated, so it is best run on your own machine rather than exposed on a shared network.