LLM inference-side compute avoidance

Cut repeated LLM inference waste before rebuilding your stack

ReuseAI is a proxy-first reuse layer for repeat-heavy LLM traffic. It helps teams test whether repeated requests can be reused to reduce latency, improve throughput, and avoid unnecessary inference cost without changing models, retraining, or rewriting the whole application.

Apply for a Pilot See Benchmarks
Proxy-first No model changes No retraining Self-hosted Ollama / vLLM / OpenAI-compatible APIs
Customer support +660.95%
TPS uplift
Customer support -86.91%
TTFT improvement
Compute avoided 66.67%
repeat-heavy support templates

If 50% of your requests repeat, you are wasting 50% of your GPU cost.

Problem

Many AI systems still pay full price for repeated work.

Support tools, API calls, and workflow systems often send the same requests again and again. In many real deployments, repeated or template-like traffic still pays full inference cost every time.

  • Support and ops tools repeat templates and ticket macros.
  • Private API services see retries and parameterized request bursts.
  • Workflow and agent systems repeat subprompts and scaffolded calls.

ReuseAI helps you test whether repeated traffic can be reused before committing to larger infrastructure changes.

Solution

A lightweight layer in front of your existing LLM backend.

1. Start with one path

Put ReuseAI in front of a single API route, service, or workflow.

2. Run real traffic

Measure whether your workload has enough repetition to benefit.

3. Compare impact

Look at hit ratio, TTFT, throughput, and whether the rollout shape is good enough.

4. Expand only if it works

Move toward broader deployment only after you see measurable reuse in production-like traffic.

ReuseAI is built as a proxy-first cache / replay layer in front of existing backends such as Ollama, vLLM, or OpenAI-compatible APIs.

Quickstart

5 minutes to try it.

docker run -p 8080:8080 reuseai-response-cache-proxy

Then change your API endpoint to http://localhost:8080.

Set your backend URL, point one service at the proxy, and see whether repeat-heavy traffic is worth reusing before you touch the rest of the stack.

Set your backend URL in config or .env before routing traffic through the proxy.

Results

Results on repeat-heavy workloads.

Customer support template
Hit ratio 66.67%
TPS uplift +660.95%
TTFT improvement -86.91%

Best fit for support ops and ticket triage.

API parameterized request
Hit ratio 50.00%
TPS uplift +98.11%
TTFT improvement -49.56%

Best fit for private API services and retry-heavy flows.

Structured prompt + variables
Hit ratio 25.00%
TPS uplift +35.36%
TTFT improvement -26.27%

Best fit for workflow/orchestration systems with stable prompts.

The same pattern holds under long-context serving: more repeated structure means more cache value.

Best-fit users

Best fit for repeat-heavy AI systems.

Support / ops

Recurring ticket macros and triage flows with high prompt repetition.

Private API teams

Parameter-heavy endpoints, retries, and workflow calls that repeat across bursts.

Workflow / orchestration

Repeated subprompts, retries, and scaffolded agent workflows.

Good fit

  • Support / ops tools
  • Private API inference services
  • Workflow and agent systems with repeated prompts
  • Template-heavy and retry-heavy request patterns

Not a fit

  • Fully random chat
  • Highly variable prompts
  • Workloads with little or no repetition
Deployment

Built for low-friction evaluation.

  • Standalone proxy in front of Ollama-compatible inference services.
  • Minimal config integration: backend URL, model, TTL.
  • Exact-match cache, replay, TTL, invalidation, and metrics included.

Client → response cache proxy → Ollama / vLLM / PyTorch-backed API

This is not a model optimization or GPU acceleration project. It is an inference-side deduplication layer.

Pilot

Start with a 2-week pilot.

You do not need a full migration to see whether ReuseAI is useful. Start with one service or workflow, connect ReuseAI in front of it, run real or replayed traffic, and measure reuse potential and performance impact.

  • Fit assessment
  • Hit ratio summary
  • Latency / throughput comparison
  • Rollout recommendation
Contact

Find out whether your LLM traffic is worth reusing.

Apply for a 2-week pilot or reach out directly at honjun@tju.edu.cn.

When reaching out, include:

  • Your use case
  • Your current backend
  • Your expected traffic pattern
  • One workflow to test first
  • How you heard about ReuseAI
Apply for a 2-week pilot

Pilot path

Start with one service or workflow. Route real or replayed traffic, measure hit ratio / TTFT / throughput, and decide whether broader rollout is worth it.

The pilot form is the fastest way to send the right intake details without back-and-forth.