Research & Benchmarks

Enterprise AI cost intelligence.

Data-driven research on LLM cost optimization, AI infrastructure economics, and production deployment patterns. From our work across defense, financial services, and enterprise technology.

Key Benchmarks

What we see across enterprise AI deployments.

40–65%

Typical savings from model routing, semantic caching, and prompt optimization combined. Based on our enterprise AI audits.

47%

Of production LLM queries are semantically similar, per VentureBeat analysis of 100K queries. Semantic caching catches these.

95%

Of GPT-4 quality maintained while routing 46–86% of calls to cheaper models. Per LMSYS RouteLLM research (2024).

30–80%

Of AI projects fail to reach production — 30% per Gartner (GenAI), 80% per RAND (all AI). The path to production, not the tech, is what breaks.

Published Research

Frameworks and findings.

AI FinOps

The 3 Most Common LLM Cost Traps

Three structural cost problems in enterprise LLM deployments — and the specific architectural fixes for each: tiered model routing (with RouteLLM data), semantic caching, and prompt compression.

Model Selection Caching Prompt Optimization
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AI Implementation

The 3 Gaps Framework for Stalled AI Projects

A diagnostic framework for enterprise AI pilots stuck between POC and production. Three gaps to check: Data, Eval, and Cost. Most stalled projects have at least two.

POC to Production Evaluation Diagnostics
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From Our Engagements

Real numbers from real deployments.

Engagement details are anonymized per client agreements. The metrics are real.

Cloud FinOps

$11M

Annual cloud savings

Fortune 100-scale enterprise. $40M annual cloud spend with no cost visibility. Reserved instance optimization, automated shutdown schedules, HPA rightsizing, and chargeback tagging.

28% cost reduction

GenAI Enablement

6×

Faster to production

Defense contractor in IL4/IL5 classified environments. AI use cases taking 6+ months to reach production. Enterprise AI platform on AWS Bedrock with governance frameworks and compliance controls.

5 AI use cases deployed

Developer Platform

67K

Engineering hours saved/yr

Large-scale government technology program. Legacy authentication across VPN and Zero Trust. OAuth-based CLI authentication, hardened CI/CD pipelines across 7 development teams.

100% adoption in 6 months

Want to benchmark your AI costs?

Our Cloud & AI FinOps audit examines model selection, caching architecture, prompt efficiency, and unit economics per AI feature.

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