Your AI bill is 3–5× higher than it needs to be.
We audit every LLM endpoint, deploy a smart routing layer, implement prompt caching, and install hard budget caps — cutting your API spend 40–80% within 30 days. No code changes required.
The Problem
Why AI bills spiral — and why most teams don't catch it.
Half of companies with AI-core products don't track LLM costs per feature. By the time someone notices the bill, the waste has been running for months.
Flagship models doing commodity work
GPT-4o and Claude Opus cost 15–60× more per token than smaller models. Most companies route every request through their best model by default — including simple classification, FAQ responses, and summarization tasks where a $0.00015/1k token model is equally accurate.
Zero prompt caching
If your system prompt is 2,000 tokens and you make 500,000 calls per month, you're paying to process that same prompt 500,000 times. Prompt caching alone — supported by OpenAI, Anthropic, and Google — typically cuts input costs 80–90% on repetitive workloads.
No cost attribution
Most engineering teams can tell you the total LLM bill but not which feature, team, or workflow is driving it. Without per-endpoint attribution, you're flying blind — you can't cut what you can't see.
No budget caps
A misbehaving agent loop, a retry storm, or a sudden traffic spike can burn through thousands of dollars before anyone gets a Slack notification. Hard budget caps with automatic cutoffs are a one-day implementation that most teams skip until it's too late.
The Audit
Ten things we look at in every AI cost review.
Most audits stop at "which model are you using." We go deeper — into prompt structure, call patterns, retry logic, and cost attribution gaps that your provider dashboard will never show you.
Book a free audit →What We Build
Five levers. Every one compounds on the last.
We implement them in order of impact. Most clients hit 50% savings from routing alone before we touch anything else.
Intelligent model routing
A drop-in proxy layer classifies each incoming request and routes it to the cheapest model that can handle it accurately. Complex reasoning goes to flagship models. Classification, summarization, and FAQ go to mini models. Typical result: 50–70% cost reduction before any other change.
Prompt caching
Static system prompts, few-shot examples, and repeated context blocks are cached at the API level. Every provider supports it. Most teams haven't turned it on. A single afternoon of implementation typically saves 40–60% on input token costs for high-volume features.
Token optimization
We audit your prompt structure for redundancy, reformat outputs to use structured JSON instead of verbose prose, and eliminate token-expensive patterns like chain-of-thought where it isn't needed. Average reduction: 20–35% on top of routing and caching savings.
Hard budget caps
Per-feature, per-team, and per-model hard limits with Slack and email alerts at 80% threshold and automatic cutoffs at 100%. Prevents a single runaway agent or traffic spike from destroying your monthly budget while your on-call engineer is asleep.
Real-time cost dashboard
A CFO-readable view of your AI spend by feature, model, team, and day. Built on top of your existing logging infrastructure — no new data pipelines required. Delivered as a live Grafana, Retool, or Notion dashboard depending on what your team already uses.
The Process
Audit to savings in 10 business days.
Everything is done for you. You approve the routing rules before anything goes live.
AI Spend Audit
We review your API logs, endpoint structure, and current model selection. You walk away with a full cost attribution map and a ranked list of every optimization opportunity sorted by dollar savings.
Cost attribution reportOptimization Design
We design the routing rules, caching strategy, and token reduction changes specific to your architecture. No generic templates — the routing logic is built around your actual query patterns.
Custom optimization specDeploy & Verify
The routing layer goes in as a drop-in proxy. Caching changes are applied. Budget caps are configured. We verify cost reductions on live traffic before handing anything over.
Live system with verified savingsMonitor & Tune
Weekly cost review for the first 60 days. We tune routing accuracy as your query patterns evolve, catch model version upgrades that change cost profiles, and flag any new spend spikes.
Ongoing cost governanceProvider Support
We optimize across every major provider.
Whether you're running OpenAI, Anthropic, Google, or AWS Bedrock — the same routing and caching architecture applies.
GPT-4o, GPT-4o-mini, o1, o3-mini, embedding models
Claude Opus 4, Sonnet 4, Haiku 4 — prompt caching natively supported
Gemini 2.5 Pro, Flash, Vertex AI — context caching available
AWS BedrockAny Bedrock-hosted model including cross-region inference
Enterprise OpenAI deployments with PTU and serverless
Llama, Mistral, and self-hosted models on your infrastructure
FAQ
Questions before you book.
Everything engineering teams ask before the first call.
No. We work from API logs and usage dashboards. We need read access to your LLM provider usage reports (OpenAI, Anthropic, Google) and a 30-minute call with your engineering lead. No code access required for the audit phase.
OpenAI (including Azure OpenAI), Anthropic, Google (Vertex and AI Studio), AWS Bedrock, and Groq. If you're running open-source models on your own infrastructure, we can audit those too — the optimization principles are the same.
Enterprise contracts typically come with volume commitments, not discounts on wasted usage. Reducing your token consumption by 60% on an enterprise plan directly reduces your next renewal benchmark — and makes your actual usage cost materially less. We optimize usage; you renegotiate contract rates on top.
The audit takes 2–3 business days. Routing and caching deployment takes 3–5 days. Most clients see verified savings within 10 business days of the first call. Budget caps and dashboards are typically live within the first week.
Only if the routing rules are wrong. We validate every routing decision against your actual outputs before going live. The rule is simple: a query gets routed to a cheaper model only if that model has been benchmarked to match flagship quality on that specific task type. If it doesn't match, it stays on the flagship model.
Both options are available. The audit and initial optimization is a one-time fixed fee. Ongoing monitoring — weekly cost reviews, routing tuning as your product evolves, and catching model version changes — is a monthly retainer. Most teams start with the one-time engagement and move to retainer after seeing the first results.
Find out what your AI stack is actually costing you.
In a free 30-minute call we'll review your current usage patterns, estimate your optimization potential, and show you exactly which levers have the fastest payback. No commitment, no sales pitch.
The audit is a free 30-minute call with our team — not a sales pitch. We review your current setup, map exactly where you're losing leads or time, and hand you a dollar figure on each gap. You walk away with a clear plan whether you hire us or not.
What the free audit covers:
- A breakdown of where your LLM spend is going by endpoint and model
- Your estimated savings from routing, caching, and token optimization
- The fastest payback change for your specific architecture
- A fixed-fee quote to implement everything — no hourly rates, no surprises