LLM Cost Optimization for AI Agents
We analyze token usage, compress prompts, route work to appropriate models, and add caching where it makes sense for your traffic and quality bar.
LLM costs are eating your margin and scaling is unsustainable
- Monthly LLM bills keep rising without clear attribution or review
- No visibility into which agents, workflows, or prompts drive the highest spend
- Using expensive frontier models for tasks that cheaper models handle equally well
- Redundant API calls - the same or similar queries processed repeatedly without caching
- Bloated prompts carrying unnecessary context on every single LLM call
How we solve this
We perform a complete cost audit of your LLM usage, then implement prompt compression, intelligent routing, and semantic caching where they fit your stack and evaluation standards.
Token-Level Cost Audit
Complete attribution of every dollar of LLM spend to specific agents, workflows, and prompt patterns
Prompt Compression
Systematic reduction of prompt length by 25-40% while maintaining identical output quality on your eval suite
Model Routing Engine
Intelligent classification that routes simple tasks to cheaper/faster models, keeping frontier models for complex reasoning
Semantic Cache Layer
Near-duplicate query detection that serves cached responses for semantically similar inputs
Cost Dashboard & Alerts
Real-time visibility into per-agent, per-workflow spend with anomaly detection and budget enforcement
How it works
Cost Audit
We instrument your LLM calls and build a complete cost attribution map in 3-5 days
Optimization Plan
Prioritized list of optimizations ranked by savings potential and implementation effort
Implementation
We deploy prompt compression, routing, and caching - typically 7-10 days
Measurement
2-week monitoring period comparing before/after metrics to validate savings
Frequently asked questions
How do you estimate savings opportunities?
We estimate savings only after reviewing your traffic, prompts, routing rules, and evaluation standards. Some stacks have obvious waste, while others need more careful tuning with smaller gains.
Will optimization hurt response quality?
No. We validate every optimization against your existing evaluation suite (or help you build one). We only ship changes that maintain identical quality scores. In many cases, quality actually improves because we remove noise from prompts.
What is the minimum LLM spend where this makes sense?
Optimization work is easiest to justify when model usage is already material to your operating budget or when cost spikes are blocking rollout decisions.
Do we need to change our agent framework?
No. Our optimizations work at the prompt and API layer, independent of framework. LangChain, CrewAI, AutoGen, custom agents - the optimization techniques are the same.
Ready to get started?
Book a consultation with a senior AI engineer. We will use the conversation to understand your requirements and suggest next steps.
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