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. Tikal deploys and manages 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.
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.
Continuously lower AI spend through engineering optimization, without slowing product delivery.
Understand spend, token usage, prompt cache efficiency, and latency across every model, team, application, and agent.
Compress context and reduce unnecessary output tokens through the gateway, without requiring application rewrites.
Reserve premium models for complex reasoning and route routine workloads to faster, lower cost alternatives.
Control model access with scoped keys, budgets, alerts, and kill switches for every team and agent.
Move high volume, repetitive, or privacy sensitive workloads to self hosted SLMs when it makes economic sense.
Continuously tune routing, context compression, prompt cache efficiency, guardrails, and model selection as usage and provider economics change.
The AI Cost Management engagement begins with a focused 30 day deployment designed to establish visibility, control, and active cost reduction, followed by ongoing optimization.
(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.
(Week 2)
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 3-4)
We reduce token consumption through context compression, prompt cache optimization, and output control, while routing routine workloads to lower cost models and reserving frontier models for deep reasoning.
We continuously tune routing, compression, guardrails, and model selection as workloads and provider economics change, keeping AI costs under control without slowing product delivery.
Following deployment, engagements continue through a standard managed retainer. Where relevant, Tikal can also take full accountability for the AI invoice or work under a shared success model tied to verified savings.
Ready to optimize?
Let’s reduce AI spend without slowing your engineering teams.
