INFERENCE OPTIMIZATION
Send less context. Measure the difference.
Reduce redundant history and structural overhead before requests reach the provider, with deterministic transformations and visibility into token savings.
LESS REDUNDANCY IN THE REQUEST
Keep useful context.
Reduce the overhead.
Deterministic compression transforms provider-bound requests without asking another AI model to summarize them.
Long conversations and agent workflows repeatedly send history, formatting, and structural content back to a model. That repetition consumes input tokens and context-window capacity.
Milgram applies deterministic transformations to reduce redundant conversation history, structural overhead, unnecessary formatting, and other token-heavy content. The goal is to preserve the information the task needs while reducing what the provider must process.
Fewer input tokens can lower inference cost and context pressure. Response-time benefits depend on the provider and workload. Changes to task quality must be measured rather than assumed.
ARCHITECTURAL TRADEOFFS
Compression without a summarization model.
| Consideration | Milgram’s deterministic approach | Model-based summarization |
|---|---|---|
| Additional inference | No summarization-model call. | Typically requires an additional model call or an application-managed summary step. |
| Transformation | Rule-driven reductions and structural transformations. | A model generates a new representation of the source content. |
| Information risk | Transformations can still remove or alter task-relevant information; validate outcomes. | A summary can omit information or introduce unsupported content; validate outcomes. |
| Data exposure | Compression itself does not send the request to another model. | Review the summarization model’s processing and retention terms. |
| Economics | Measure input-token savings alongside task success and provider billing behavior. | Account for the extra inference cost as well as any savings on later requests. |
EVALUATE THE ACTUAL WORKLOAD
Measure savings
and successful work.
Record a baseline
Choose representative conversations, model versions, task checks, and provider cost assumptions. Include short requests as well as long agent sessions.
Compare transformed requests
Enable the intended compression mechanisms and inspect the changed context. Verify structured tool arguments, code, and identifiers that need exact preservation.
Review both sides of the result
Compare input-token usage, task correctness, latency, and failures. Provider caching, retries, and output tokens can affect the final bill.
Monitor over time
Track savings across requests, sessions, models, and compression mechanisms. Recheck quality when workflows or models change.
Optimization questions.
What percentage will we save?
There is no single savings rate that applies to every workload. Repetition, prompt structure, conversation length, model pricing, and caching all matter. Use your own traffic to measure the reduction and the resulting bill.
Is the compression lossless?
Milgram does not make a blanket losslessness or unchanged-output guarantee. Its deterministic approach cannot hallucinate a newly written summary, but the effect of its transformations still needs task-level validation.
Do we need to change model providers?
Compression is applied in the proxy for supported integrations. You retain the provider choice; validate the exact API path, client behavior, and enabled transformations during onboarding.
INVITE-ONLY BETA
Evaluate the economics of your context.
Discuss a representative workload and the task-quality checks that will make the result meaningful.