First LLM Integration or a Full Agent Fleet — Your AI Spend Needs a Control Plane from Day One.
LLM token costs are the fastest-growing blind spot for SMBs — and most teams have zero visibility into them. KubeNiche implements token economics frameworks and chargeback models that give engineering, product, and finance a shared source of truth on AI spend: by team, by product, by agent, by model. Whether you're calling your first OpenAI API or running a multi-agent production system, we wire cost governance in before the bills surprise you.
Get a No-Obligation Token Cost AssessmentAI Spend Is Uncontrolled at Every Stage — From First API Call to Production Agent Fleet
A single runaway agentic loop can consume thousands of dollars in tokens overnight. A team experimenting with GPT-4o, Claude Sonnet, and Gemini Pro simultaneously — with no routing strategy — pays frontier model prices for every query, including the simple ones. Without token-level cost attribution, you can't hold teams accountable, can't forecast AI budgets, can't optimise model selection for cost vs. quality, and can't demonstrate ROI to leadership. SMBs starting their AI journey face this problem from their first API key. Teams already running LLM workloads face it at scale. Token economics is the FinOps discipline your AI strategy is missing — and it's far cheaper to implement before the spend accumulates than after.
What's Included
Core Capabilities
Token Cost Attribution — Per Team, Product, Agent & Model
Instrument your LLM calls — via LiteLLM Proxy or direct provider APIs (OpenAI, Anthropic, Azure OpenAI, Bedrock, Vertex AI) — to attribute token consumption to teams, products, environments, and individual agent workflows. Virtual key management for per-consumer spend isolation. Real-time spend dashboards in Grafana or your BI tool of choice. For SMBs making their first LLM API calls, we wire attribution in from the start so you never have an unattributed AI bill.
Model Routing & Cost Optimisation — Tiered LLM Strategy
Not every query needs GPT-4o or Claude Opus. We analyse your token usage patterns and implement intelligent model routing strategies — routing simple classification and summarisation tasks to cost-efficient models (GPT-4o-mini, Claude Haiku, Gemini Flash), and reserving frontier models for complex reasoning and high-value inference. Semantic caching for repeated prompts. Typical AI cost reduction: 30–60% without degrading answer quality.
Chargeback & Showback Framework Design
Design and implement AI chargeback models that finance teams can act on — monthly AI spend reports by cost centre, with variance analysis, budget vs. actual tracking, and per-model cost breakdown. For SMBs starting their AI journey, we establish the chargeback framework before the first production workload runs. For teams with existing AI spend, we retrofit attribution and produce the first chargeback report within 30 days.
Token Budget Guardrails & Real-Time Alerting
Implement hard and soft token budget limits at the gateway layer (LiteLLM Proxy) — with real-time alerts to Slack or PagerDuty before spend exceeds thresholds. Hard limits that stop runaway agent loops before they drain your monthly budget. Per-team, per-environment, and per-agent budget enforcement. For SMBs deploying their first Agentic AI workload, this is the safety net that prevents the first production incident from being a $10,000 surprise.
AI Unit Economics — Cost Per Inference, Per Feature, Per User
Move beyond raw token counts to business-meaningful AI unit economics — cost per API call, cost per document processed, cost per active user, cost per agent task completed. These metrics give product and engineering leadership the signal they need to make model selection, feature prioritisation, and pricing decisions. For SMBs building AI-powered products, unit economics are the foundation of a sustainable AI cost model.
Context Window & Prompt Efficiency Optimisation
Context window management is one of the highest-leverage cost levers in LLM workloads. We audit your prompt templates, RAG retrieval chunk sizes, conversation history management, and system prompt lengths — and implement optimisations that reduce token consumption without degrading output quality. For teams already running production LLM workloads, prompt efficiency audits typically surface 20–40% token reduction opportunities.
First LLM integration or a production agent fleet — AI cost governance starts now
Book a no-obligation token cost assessment. Whether you're making your first OpenAI API call or running a multi-agent system with uncontrolled spend, we'll map your current AI cost landscape and show you exactly where the waste is — or how to prevent it from ever appearing.
Get a No-Obligation Token Cost Assessment