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Typical 30–70% savings
Most audits find a substantial share of spend that can be removed with routing, caching and batching alone — before any infrastructure change.
AI features that looked cheap in a prototype often become one of the largest lines on the cloud bill in production. LLM cost optimization finds where that money goes and removes the waste — usually without users noticing any change in quality.
We audit your OpenAI, Anthropic Claude and Google Gemini usage call by call, then apply the fixes that pay back fastest: routing simple requests to smaller models, prompt and response caching, prompt compression, batch APIs for non-urgent jobs, and moving high-volume tasks to self-hosted open models. Every change is measured against a quality benchmark so savings never come at the cost of output. The audit starts as a free working mockup on your own logs; implementation is quoted at a fixed price.
The problem
Teams default to the most capable model for everything, resend the same long system prompts on every call, and run overnight jobs at real-time prices. None of it is visible until the invoice arrives.
Why it pays
Audit your OpenAI, Claude and Gemini spend and cut it with routing, caching, batching and open-source offload — quality held constant.
Most audits find a substantial share of spend that can be removed with routing, caching and batching alone — before any infrastructure change.
We build an evaluation set from your real traffic and prove each optimisation scores at least as well as the original setup.
Dashboards show exactly which features, endpoints and customers drive spend, so pricing and roadmap decisions use real numbers.
A lightweight router sends each request to the cheapest model that can handle it, escalating to frontier models only when needed.
Sustained, repetitive workloads can move to self-hosted Llama, Mistral or Qwen at a flat infrastructure cost.
How we deliver
Every engagement starts with a free working mockup, then a fixed-price build — no hourly drift, no surprises.
We analyse your usage logs and invoices to break down cost by model, feature, prompt and customer.
A free working prototype of the top optimisations on your own traffic, with measured savings and quality scores.
We roll out routing, caching, batching and any open-source offload behind feature flags with safe rollback.
Dashboards and alerts keep cost and quality visible as your product and model prices change.
Spec sheet
Fixed scope, agreed up front. You own everything we deliver — code, models, assets and documentation.
Use cases
Patterns we build again and again — adapted to your data, your tools and your customers.
Route routine in-app requests to small models and reserve frontier models for complex queries, so heavy users stay profitable.
Move bulk description and tagging work to batch APIs or self-hosted models at a fraction of real-time cost.
Classify and route tickets with a small model; only escalate drafting of complex replies to a frontier model.
Tag every request by client and project so AI costs can be billed back accurately.
Models & tools
Model-agnostic by design: we pick per task and switch when something better ships.
Packages
Fixed price, quoted after a short scoping call — and only after you've seen the free mockup.
A complete breakdown of your AI spend with a ranked, estimated savings plan.
Savings plan in days
We implement the savings plan end to end and hand over monitoring.
Measured savings in production
FAQ
Still curious? Ask AURA, or email us and a human replies within one business day.
We measure where your AI spend goes, then apply techniques such as routing easy requests to smaller models, caching repeated context, compressing prompts, using batch APIs and moving high-volume tasks to self-hosted models. Each change is tested against a quality benchmark built from your own traffic.
Results vary with your usage pattern, but savings of 30–70% are common once routing, caching and batching are applied. Teams with large repetitive workloads can save more by offloading to open-source models.
Not if it's done properly. We only ship an optimisation if it matches or beats the original on an evaluation set drawn from real requests, and complex queries still escalate to frontier models.
Yes. We're model-agnostic and regularly optimise mixed setups across OpenAI, Anthropic Claude, Google Gemini and open models like Llama, Mistral and Qwen.
Changes are usually contained in a thin routing and caching layer between your app and the model providers, so application code changes are minimal.
We run the top optimisations against a sample of your real requests and show measured cost and quality side by side. Implementation is then quoted at a fixed price.
Keep exploring
Run Llama, Mistral, DeepSeek or Qwen on your own infrastructure behind an OpenAI-compatible API — and stop paying per token.
Production-grade integration of OpenAI, Claude and Gemini — agents, tool use, evals, guardrails and multi-provider failover.
LLMs, search and assistants that run inside your own network — secure RAG on private data, with full audit trails.
AI readiness audit, ROI-ranked use-case roadmap, governance and a pilot-to-production plan for the enterprise.
Tell us the outcome you need. We build a working mockup of the render, workflow or agent — at zero cost — so you judge results, not pitch decks.