LOSSLESS AI Cost Optimization & Token Compression
OptScale AI compresses everything your models read, aligns prompt caches so provider discounts actually hit, trims system-prompt overhead, and routes each request to the best-value model — one gateway, no code changes.
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Why The Bill Runs Away
On the workloads enterprises actually run — RAG and agents — input tokens outnumber output by 5–30×. Cheaper models help the output side, but most of the spend is on everything you send: retrieved chunks, tool outputs, logs, and re-sent history.
How OptScale Cuts It
Each request passes through four cost controls before it reaches a provider. They attack different parts of the bill, which is why they compound.
LEVER 01
Strips fixed instruction and tool-schema overhead re-sent on every single call.
Input tokens ↓
LEVER 02
OptScale's compression engine shrinks retrieved context, tool outputs, logs and history inline — reversibly, before they reach a provider.
Input tokens ↓↓↓
LEVER 03
Stabilizes prompt prefixes so Anthropic / OpenAI KV caches actually hit — repeated context billed at ~10%.
Repeat tokens ↓
LEVER 04
Routes what remains to the best-value model that still meets quality — cost per token, not token count.
Cost / token ↓
≈97%
The lossless compression engine alone strips 60–95% of context tokens on compressible payloads. Stacked with cache alignment, system-prompt trimming and smart routing, total tokens billed at full rate can drop by up to 97% on long-context, high-repetition workloads — which is what pulls blended cost down by up to 60%.*
Read the docs →Under The Hood
Content is detected, routed to the right compressor, and cached locally so nothing is lost. The model can pull an original back on demand.
01
Detects whether a payload is JSON, code, or prose and picks the right compressor.
02
Separate JSON, AST-aware code, and text compressors — each shrinks its own content type.
03
Stabilizes prefixes so provider KV caches keep hitting after compression.
04
Originals stay cached locally; the model calls one back only if it needs it.
Proof, Not Promises
How much you save depends on the payload. Here's what typically compresses on real agent workloads — with answers held flat.
Where It Fits
Compression lives on the gateway, so every team and agent inherits it without touching application code.
Point your apps and agents at the OptScale gateway. Compression, caching and routing apply to every request automatically.
Same platform, your terms. On-prem keeps every prompt and every token inside your network — a real edge in regulated industries.
Savings are modeled on top of provider prompt caching, not double-counted against it — so the number you see is the number you get.
Run a real workload through the gateway and watch tokens — and cost — drop in the dashboard.
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