Every time a coding assistant sends a request, it re-sends a huge pile of context: the conversation so far, old tool outputs, logs, and file dumps. The AI model charges per token of input, so all that repeated bulk costs money on every single request. Clawgate sits in the middle, between the developer’s tool and the AI provider. Before the request goes out, it rewrites the bulky parts to be shorter while keeping the meaning intact. The model receives a smaller request, so you’re billed for fewer input tokens.
Four real agent workloads, measured before and after compression. The more repetitive the material, the more comes off — which is why bulky tool output saves the most and unique code saves the least.
The same figures, in full:
| Workload | Before | After | Savings |
|---|---|---|---|
| Code search (100 results) | 17,765 | 1,408 | 92% |
| SRE incident debugging | 65,694 | 5,118 | 92% |
| GitHub issue triage | 54,174 | 14,761 | 73% |
| Codebase exploration | 78,502 | 41,254 | 47% |
Sending less only counts if the work still comes back right. On standard public benchmarks, accuracy held.
Accuracy unchanged
Accuracy improved
at 19% compression
at 32% compression
The provider counts tokens on what it receives. Because Clawgate compresses upstream of that meter, the savings are real, not cosmetic.
Repetitive JSON, logs, and duplicated context, the stuff that’s bulky but low-value, get squeezed. Unique code and the developer’s actual questions are left untouched.
The most recent messages and the critical reasoning are never altered, so the AI’s answers stay the same. Your team notices nothing except a lower bill.
The savings come out of the old, repetitive material that piles up over a long session. Everything the model needs in full is passed straight through.
If compression can’t help a request, or anything looks off, the request goes out unchanged. The worst case is a request that simply isn’t compressed, which is exactly how it would have run with the feature off. It never blocks a developer’s work.
An admin turns it on for the organization and sets how hard to compress. Developers change nothing. Because Clawgate is already in the request path, it records what each request saved and shows it back to you in dollars on the dashboard.
Measured on real agent workloads and standard public benchmarks. Recent context and model reasoning are never altered.
Full benchmarks & methodology →