Concisly / do more, pay less

For Agents

Every agent call, automatically leaner.

Drop one line in front of your model calls. Concisly trims every prompt before it ships — same output, fewer tokens.

from concisly import wrap client = wrap(anthropic.Anthropic()) # every prompt, leaner with Concisly
58
input tokens saved on a typical bloated system prompt
~50%
leaner prompt — same intent, same output3
1
line of code to route every agent through it
Works with
Anthropic
OpenAI
Gemini
The old economy

You optimized the API call.

Latency, retries, rate limits. The call was the unit of cost.

→
The agentic economy

Now you pay by the token.

Agents loop. Every loop re-sends the same bloated context. The token is the unit of cost now — and most of it is waste.

The problem

$8.4B spent on model calls in 2025. Nearly half, gone to fluff.

Independent audits put 40–60% of every token budget down to waste — bloated context the model never needed. At scale, you're paying for it on every call.

$8.4B
enterprise LLM spend in 2025, up from $3.5B in 2024²
40–60%
of production token budgets are pure waste¹
$60/mo
from one 1,200→400-token system prompt at 5k calls/day¹

¹ Field audits of production LLM applications, 2025.   ² Enterprise LLM spend, 2025.   ³ ~50–66% fewer tokens at under 5% accuracy loss — Microsoft Research (LLMLingua), 2023.

Measured waste
~50%
avg. system prompt3

That's fluff your agents re-send and re-pay for on every call in the loop.

fluff · trimmed
signal · kept
≈50%≈50%

How it works

A gateway for your prompts, not just your calls.

Route your model traffic through Concisly like any gateway. It reads the prompt, trims it, and forwards the lean version.

01 · Wrap

Point at Concisly

Swap your base URL or wrap your client.

02 · Trim

Optimized once, cached

Each prompt is trimmed once and cached by hash.

03 · Prove

Every call is metered

Tokens in, tokens out, dollars saved — logged per call.

Your prompts stay yours

A gateway sees your traffic. Here's what we don't do with it.

Encrypted end to endTLS in transit, encrypted at rest — on every hop between you and your model.
Trimmed in memoryForwarded lean instantly. Only the optimized prompt is cached — never a full log.
Never sold or trained onYour prompts are never sold, and never used as training data.
Metadata-only loggingWe record token counts to meter savings — never your prompt text.

Cost at scale

One leaner prompt, fired a million times.

The savings are tiny per call and enormous per fleet.

Without Concisly
108 tokens / call
108
With Concisly
50 tokens / call
50
$6,264
saved / year at 100k calls/day on Claude Sonnet 5
~$0
runtime cost — the trim is cached, not recomputed
$10,440
saved / year at the same volume on Opus 4.8

Wrap your first agent in a minute.

Honest math: figures assume ~58 input tokens saved on a typical bloated system prompt at current list prices; savings are input-token only. A bigger prompt saves more, a lean one saves less. Dollar figures scale linearly with call volume — $6,264/yr = 58 tokens × 100,000 calls/day × 30 days × $3/1M.