Cut your AI bill, without cutting quality.
We audit production AI systems and find the leverage to cut LLM cost and GPU spend: caching, model routing, prompt compaction, and batch inference. 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.
Every runaway AI bill has the same anatomy.
AI spend does not explode randomly. It leaks through the same handful of holes at almost every company, and the audit's job is finding which of these is yours.
The Invoice That Doubled Without a Launch
No new feature shipped, but usage grew, contexts got longer, and the bill kept climbing, exactly the pattern behind IDC's finding that large enterprises underestimate AI infrastructure costs by 30 percent through 2027. Inference is roughly 80 percent of production AI spend, it scales with success, and a bill that grows faster than the value it produces is not a scaling story, it is a leak with good PR.
The Feature That Loses Money Per Use
Somewhere in your product is a workflow whose per-request cost quietly exceeds what it earns, and nobody knows which one because the invoice arrives as a single number. Per-feature and per-customer attribution, built on our monitoring and observability layer, turns that blur into a ranked list, and the top of that list is usually the fastest saving in the entire engagement: fix it, cache it, or kill it.
Agents Multiplying Every Token
One user request to an agent fans out into planning calls, tool calls, retries, and verification, multiplying token volume several-fold over simple chat, which is why agentic features are where 2026 bills are actually exploding. Optimizing agents is its own discipline: trimming reasoning loops, caching tool results, and routing sub-tasks to small models, and it is where the largest single-workflow savings on this page tend to hide.
Paying Frontier Prices for Commodity Questions
Most production traffic does not need the biggest model, and routing the routine majority to small fast models while reserving frontier calls for the genuinely hard few percent cuts blended cost 60 to 80 percent in typical mixed workloads. When one task dominates the bill, the deeper version of the same lever is distilling it into a fine-tuned small model, which at high volume runs an order of magnitude cheaper per query than prompting a giant.
GPUs Idling at 40 Percent
For self-hosted stacks, the waste wears hardware: real production teams sustain 40 to 65 percent GPU utilization, and unoptimized deployments run worse, single-request serving, capacity sized for a peak that comes twice a month, and batch-tolerant jobs running expensively in real time despite batch inference costing 5 to 10 times less per token. Fixing this layer is serving architecture work, and it routinely lets the same GPUs carry several times the traffic before anyone is allowed to buy more.
Token Prices Fell 80 Percent. Bills Went Up Anyway.
The strangest fact in AI economics: per-token API prices collapsed through 2025 and 2026, and total spend rose regardless, because usage, context length, and agent loops all grow faster than prices fall. Waiting for cheaper models is not a cost strategy, it is how this year's bill becomes next year's bigger one at a lower unit price. The teams whose costs actually fall are the ones treating optimization as architecture, which is the entire premise of this page.
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.
Where the money leaks, which lever to pull first, and why waiting costs more.
Every vendor promises savings. Here is the actual mechanics: where AI money leaks, the order the fixes pay off, and the structural moves for when tuning is not enough.
In rank order across the audits we run: redundant computation, the same or similar requests paying full price because nothing is cached; oversized models, frontier calls answering questions a model a tenth the price handles; token bloat, prompts and retrieved context carrying thousands of tokens per call that evals prove unnecessary; synchronous processing of work nobody is waiting for, forfeiting batch pricing several times cheaper; and, on self-hosted stacks, idle GPU capacity billed around the clock. Most companies have three of the five. The audit's deliverable is which three, sized in dollars.
Effort against yield, and the order is fairly universal. Caching lands first, days of work, immediate double-digit savings on repetitive traffic. Prompt and context compaction is next, pure token arithmetic, verified against your eval suite. Model routing follows, a few weeks to tune the router to your accuracy bar, and usually the largest single line. Batching converts every latency-tolerant workload after that. Fine-tuning a small replacement model comes last, because it is the most powerful lever and the only one that takes a project rather than a sprint. We sequence exactly this way so savings from the early levers are funding the later ones by week three.
Because three multipliers outrun the discount: usage grows with adoption, context windows grow with ambition, and agentic features multiply calls per user action, so a 40 percent price cut disappears inside a 3x volume increase and the CFO sees only the product. This is the trap in "wait for cheaper models" thinking, and it is why enterprises keep underestimating AI infrastructure costs by double digits. Falling unit prices are real and welcome, and they change nothing about whether your architecture wastes tokens. Optimization compounds with the price cuts; waiting just compounds the volume.
Two crossovers mark where optimization graduates into re-architecture, and we flag both in every audit. First: when one stable, high-volume task dominates the bill, a distilled fine-tuned model beats any amount of prompt tuning, running 10 to 50 times cheaper per query past roughly 100,000 daily requests on that task. Second: when sustained volume passes roughly half a billion to a billion tokens a month, self-hosting open models starts beating API pricing by 60 to 85 percent on inference, with all the operational caveats priced in honestly. Below those lines, tune what you have. Past them, the cheap option changes shape, and pretending otherwise just delays the savings.
They do not, by default, and this is the part most optimization engagements skip. Costs regress the same way test coverage does: the next feature ships unoptimized, the next prompt bloats, the next agent loop goes uncapped, and six months later the bill has recovered its old habits. The countermeasure is the FinOps layer, per-feature budgets, cost alerts wired to the people shipping, and cost-per-task as a metric reviewed alongside latency and quality in the same cadence. Our engagements end with that governance running, because a one-time cut is a discount, and a cost discipline is a margin.
The discovery call has one input: your last invoice. Before any contract, we will name the three biggest levers in it and roughly what each is worth, and if the honest answer is that your bill is already tight, you will hear that too.
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