Cut your AI bill, without cutting quality.
We audit production AI systems and find the leverage: caching, model routing, prompt compaction, batch inference, and GPU utilization. Most engagements take 30/60% off the inference bill in the first quarter.
Where the savings
actually come from.
Cost optimization is rarely one big lever. It's a stack of small wins, each measured against a quality eval, that compound into a transformed unit economic.
Smart Caching
Semantic cache, prefix cache, and KV cache reuse, eliminating redundant token spend without users noticing.
Model Routing
Cheap models for easy queries, frontier models only when needed, with a router that's tuned to your accuracy bar.
Prompt Optimization
Trimming token bloat from prompts, system messages, and retrieved context, measured against eval, not vibes.
Batched & Async Inference
Convert latency tolerant requests to batched inference, 5/10x cheaper per token on most providers.
GPU Utilization
Spotting underused GPUs, right sizing, and consolidating workloads, turning idle hardware into real throughput.
FinOps for AI
Cost dashboards by feature, customer, and team, with budgets and alerts so the next bill never surprises the CFO.
From audit to recurring savings.
Two week audit, ranked recommendations, then we implement the top wins side by side with your team.
Cost Audit
Two week deep dive into where your AI spend actually goes, by feature, customer, request type, and provider.
Ranked Recommendations
Concrete savings opportunities ranked by ROI, risk, and implementation effort, no generic playbook.
Implement & Measure
We pair program the top wins with your team, every change A/B tested against quality and cost together.
Ongoing FinOps
Cost dashboards, budgets, and review cadence so savings compound, and new features ship within budget by default.
Questions about
AI Cost Optimization
Every change is A/B tested against your eval suite. We won't ship a saving that costs quality, and we report the trade offs explicitly, not in fine print.
30/60% in the first quarter is typical for systems that haven't been optimized before. After that, savings depend on how aggressive you've already been, we'll tell you honestly during the audit.
Both. For API stacks we tune prompts, caching, batching, and routing. For on prem we optimize utilization, batching, and quantization on your hardware.
That's the most common starting point. The audit's first deliverable is a full cost attribution by feature, customer, and request type, usually surfacing surprises within the first week.
Fixed fee for the audit and implementation phase. For large engagements we sometimes structure a portion of fees against measured savings.
Stop experimenting.
Start deploying AI that works.
Book a free discovery call. Send us your last invoice and we'll tell you the three biggest levers, before you sign anything.
info@croncore.com