LLM API costs scale non-linearly. A prototype that costs $20/month becomes a $2,000/month problem in production - not because you're doing more, but because you never designed for cost. Token waste, wrong model selection, no caching, and context bloat compound silently until someone notices the AWS bill.
of enterprise LLM spend wasted on inefficient prompts and wrong model routing
cost reduction possible with prompt caching on repetitive system prompt patterns
cost difference between Haiku and Opus for tasks that don't need full reasoning
of teams have per-agent cost observability when they first come to us
Not every task needs your most powerful (and expensive) model. Intelligent routing sends each query to the right tier based on complexity, latency requirements, and acceptable quality thresholds.
Classification and routing decisions
Simple extraction and formatting
High-volume, low-stakes operations
Intent detection and triage
~$0.25–0.80 per 1M tokensMost standard agent tasks
Code generation and review
Document analysis and summarization
Customer-facing interactions
~$3–15 per 1M tokensComplex multi-step reasoning
Architecture and design decisions
High-stakes judgment calls
Novel problem solving
~$15–75 per 1M tokensSystem prompts and static context repeated across hundreds of requests can be cached. Claude's prompt caching feature delivers 10–90% cost reduction on repetitive pattern workloads with near-zero added latency.
Context bloat is a silent cost killer. We audit what lives in your context windows, remove noise, and implement dynamic context loading - only pulling in what the agent actually needs for each specific task.
Async batch processing delivers significant cost discounts for non-real-time workloads. We identify which agent tasks can be batched and architect the queuing infrastructure to support it.
Hard limits per agent, per team, per project. Token budgets enforce fiscal discipline and surface runaway costs before they hit your invoice. Paired with alert thresholds and auto-routing fallbacks.
You can't optimize what you can't see. We build cost observability into your agent infrastructure - per-agent, per-use-case, per-team cost tracking with dashboards that show where the money actually goes.
Tier rate limits, quota management across multiple API keys, graceful backoff, and priority queuing ensure high-value agent tasks never get throttled by low-value background jobs.
Full analysis of your current LLM spend - where it goes, what's wasted, and where the quick wins are. Baseline established before optimization begins.
Intelligent model routing layer that automatically selects the right model tier for each request based on complexity scoring and configurable policies.
Real-time cost visibility per agent, team, and use case. Budget alerts, anomaly detection, and trend analysis so cost surprises become a thing of the past.