As AI usage spreads across applications, agents, teams, and providers, costs can grow faster than engineering leaders can control them. Monthly invoices show what was spent, but not how to reduce it. We deploy and manage an engineering optimization layer that actively reduces AI costs without requiring application rewrites or slowing product delivery.
We route AI traffic through a centralized, model agnostic gateway inside your environment. This creates one controlled path for measurement, context compression, prompt cache optimization, model routing, budgets, and guardrails. From there, we reduce unnecessary token usage and route routine workloads to lower cost models, while reserving frontier models for deep reasoning. AI Cost Optimization is also the cost control layer of our Agentic SDLC Factory Platform, which brings it together with AI Transformation, Shared Brain and the Agentic Factory Floor.
Our modular open source stack uses LiteLLM for centralized model routing, Headroom for context compression, and Grafana for spend, token usage, prompt cache efficiency, and latency monitoring. Where relevant, we add automated guardrails, agent steering, and local inference through Ollama or vLLM.
Cut cloud AI spend by 30% to 60% through engineering optimization, without slowing product delivery.
Understand spend, token usage, prompt cache efficiency, and latency across every model, team, application, and agent.
Reduce codebase exploration tokens by up to 70x with Graphify, alongside context and output optimization, without application rewrites.
Route up to 80% of simple requests to lower cost models, reserving premium models for complex reasoning.
Set agent budgets, alerts, and kill switches to freeze runaway retry loops ("Loopmaxxing") in real time before they burn through your monthly budget.
Move high-volume, repetitive, or privacy-sensitive workloads, such as PII scrubbing, secret redaction, and log analysis, to self-hosted SLMs inside your VPC at $0 cloud API cost.
Continuously tune routing, context compression, prompt cache efficiency, guardrails, and model selection as usage and provider economics change.
The AI Cost Optimization engagement begins with a focused 6 week deployment designed to establish visibility, control, and active cost reduction, followed by ongoing optimization. The process is scoped to a group of up to 50 developers, and then moves on to the next group.
(Week 1)
We deploy the centralized gateway and route AI traffic through it. Together, we verify application integrity and establish a baseline for usage, cost, latency, and prompt cache efficiency.
(Weeks 2-3)
We launch real time dashboards, scope model access, set budgets, and configure alerts and kill switches to bring AI usage and spend under control across teams and agents.
(Weeks 4-6)
We reduce token consumption across transport, codebase exploration, and output, then route up to 80% of simple requests to lower cost models while maintaining quality.
(After Week 6)
We continuously tune routing, compression, guardrails, and model selection as workloads and provider economics change, so your savings last.
Following deployment, engagements continue through a standard managed retainer. Where relevant, We can also take full accountability for the AI invoice or work under a shared success model tied to verified savings. AI Cost Optimization is one of four solutions in the Agentic SDLC Factory Platform. See the full platform.
Ready to optimize?
Let’s reduce AI spend without slowing your engineering teams.
