Evidence tier Source Confirmed · 4 captures on record
What the publisher says
As described on AWS Marketplace.
AI Cost Evaluation is a fixed-scope assessment for teams running production LLM workloads on Amazon Bedrock, Anthropic or OpenAI, where token spend is growing faster than the business case.
We start with token-level cost observability: unified usage, cache hit rates and dollar costs across Bedrock, Anthropic and OpenAI in a single view. You see where every dollar goes by workload, feature, user and prompt, including input vs output ratios and cache performance.
Show the rest of the publisher’s description (6 more lines)
We then work through the four highest-leverage cost levers on modern LLM stacks:
Prompt caching audit. For any workload with a stable system prompt, long reference document or repeated tool schema, we review cache checkpoint placement, minimum token thresholds and TTL choice (5-minute vs 1-hour) across Claude 3.7 / 4.5 / Opus 4.5.
Batch inference candidates. Bedrock batch inference returns results within 24 hours at a flat 50% discount vs on-demand. We identify every latency-tolerant workload, nightly summarisation, enrichment, backfills, notify-when-done user flows, that can move to batch with zero prompt or model changes.
Intelligent prompt routing. With price gaps exceeding 100x between flagship and lightweight models like Amazon Nova Micro, routing simple classification, extraction and formatting traffic to smaller models is one of the largest structural savings available. We design a router that preserves quality where it matters.
Model and provider selection. We benchmark equivalent-quality alternatives across Bedrock, Anthropic and OpenAI for your actual workload mix.
Deliverable: a ranked report of every AI cost recommendation, quantified annual saving, implementation effort and quality-risk score. Followed by a working session with your AI engineering leads to sequence the changes into sprint-ready tickets.
Highlights
Highlighted by the publisher on AWS Marketplace.
Unified token, cost and cache observability across Amazon Bedrock, Anthropic and OpenAI. See every dollar by workload, feature, user and prompt.
Prompt caching, batch inference, intelligent routing and model selection audit, the four highest-leverage AI cost levers, quantified per workload.
Ranked savings report with annual saving, effort and quality-risk score. Joint sprint planning with your AI engineers.
Agent build and provenance
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The layer-by-layer build, the evidence behind each claim, the risk basis and the cross-marketplace links are open to any account. Some rows are disclosed, some the source leaves Unknown; a free account shows you which.
Sources
Linked repositories
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