LLM Inference Cost Optimization Assessment

Teams running LLM inference in production on Bedrock, SageMaker, or self-hosted GPU instances typically choose a model and instance size early on and rarely revisit that decision as usage grows. Over time, this leads to significant overspend -- because batching requests, caching repeated calls, trimming context, right-sizing models, and routing simpler requests to cheaper models routinely cut inference costs well below what most teams assume is the floor.

The problem is that few teams have the bandwidth to audit their own setup. That's where this assessment comes in.

Software Sushi conducts a thorough audit of your current inference architecture, request patterns, and spend, then delivers a prioritized report of specific savings levers with estimated impact and implementation effort for each.

Scope and Deliverables:

1. Audit of current inference architecture: model choice(s), hosting approach, request patterns, and current spend
2. Identification of specific savings levers: model right-sizing, request batching and caching, prompt and context trimming, routing between model tiers, and reserved vs. on-demand capacity
3. A prioritized recommendation report with estimated savings per change and implementation effort
4. Optional follow-on: implementation of the top recommendations as a separate fixed-price engagement

Why This Assessment:

• Delivered by an AWS Certified AI Practitioner partner with a track record that includes a 62% cloud cost reduction for a prior client
• Fixed-price assessment -- not a percentage-of-savings model or ongoing retainer -- making it a low-risk entry point
• Assessment-only engagement, so it's an easy first step for teams that aren't ready to commit to a full re-architecture

This is a low-commitment way to get a concrete, actionable plan to cut inference spend without hurting latency or quality -- and to start a relationship with Software Sushi before any larger engagement.