Measure
Find the real economic opportunity.
Report prompt-length and savings distributions using the same tokenizer, model configuration, and cache assumptions that matter in production.
Founding-user pilot · Production AI teams
Prove prompt savings on your workload before changing production traffic.
Bring a representative set of long prompts and the downstream behavior that matters. We will measure token economics, define what must survive, inspect risky changes, and reach a bounded compress, narrow, or stop decision.
Representative prompt set
Explicit Quality Checks
Original-input fallback
Cost and task-quality decision
The immediate problem
A smaller prompt is not useful if the task gets worse.
Compression ratios are easy to market and difficult to trust. Provider caching, compression cost, latency, critical facts, schemas, instructions, grounding, and downstream behavior all determine whether a saved token is a real production win.
Protect
Make important information explicit.
Define Critical, Required, and Preference checks for facts, values, structure, instructions, citations, and task behavior.
Decide
Choose a guarded next step.
Leave with a workload-specific decision: compress, limit compression to certain paths, continue testing, or keep the original input.
The pilot
Run a prompt-cost proof sprint on one workload.
The pilot is evidence work, not a promise of a universal savings percentage. A useful result may identify a narrow safe path or conclude that compression is not worthwhile.
- 01
Select representative long prompts and freeze the downstream model configuration.
- 02
Define must-keep content, output validity, grounding, and task-success checks.
- 03
Compare original and compressed paths, including latency, fallback, and net savings.
- 04
Review failures and agree on a proceed, narrow, investigate, or stop recommendation.
Strong fit
This is worth a conversation if…
- ✓A production RAG, document, agent, transcript, or long-history workflow has material input cost.
- ✓You can provide representative prompts or reproducible test cases.
- ✓The downstream task has observable success, grounding, or output-validity criteria.
- ✓An engineering owner can review protected information and failure cases.
Probably too early or the wrong tool
Wait if…
- ×Prompts are usually short enough that compression overhead will dominate.
- ×Only output-token cost matters for the workload.
- ×There is no representative sample or way to judge downstream behavior.
- ×Any change to exact wording is unacceptable and no deterministic transform is suitable.
Clear product boundary
Prompt compression is lossy. UsageTap makes the trade-off explicit and reversible.
The implemented product supports Runtime Compression and reusable Compression Jobs, deterministic processing, editable Quality Checks, verification workflows, experiments, and fallback. None of that makes every prompt safe to compress.
- We do not promise universal savings, losslessness, or quality improvement.
- Checks and representative evaluations stay separate from a single similarity score.
- When configured validation fails, the original input remains the safe fallback.
Find out where prompt compression earns the right to run.
Bring one workload, its real prompts, and its definition of success. The result should be a decision your engineering team can defend.