AI Performance Optimization
If your AI feature is slow, expensive, or both, that's an engineering problem - not a model problem.
Most LLM cost and latency issues come from the same handful of mistakes: sending too much context, using the most expensive model for every task, re-generating outputs that haven't changed, and blocking on completions that could run in parallel. These are all fixable without touching what the model produces.
Prompt and Context Optimization
Every token in your prompt costs money and adds latency. Most production prompts carry more context than the model actually needs.
We audit prompts for redundant instructions, stale context that isn't referenced in outputs, verbose few-shot examples that could be shorter with the same effect, and system prompts that have grown organically and never been cleaned up.
A well-trimmed prompt typically runs 30-50% faster at 30-50% lower cost with no measurable change in output quality. This is usually the first thing we look at because the payoff is immediate and requires no architectural changes.
Response Caching
The cheapest LLM call is the one you don't make. If your application generates similar outputs repeatedly, caching is almost always the right answer.
Exact caching - For deterministic prompts where the same input always produces the same output, we cache responses by prompt hash. Works well for document classification, structured extraction, and template filling.
Semantic caching - For near-duplicate inputs, we use embedding similarity to match incoming requests against cached results. A question phrased six different ways returns the same cached answer.
Anthropic prompt caching - For applications with large static system prompts or shared context blocks, we configure Anthropic's built-in prompt caching. Repeated context after the first call costs a fraction of the original price.
Model Routing
Not every task needs your most capable model. Routing to the right tier is one of the fastest ways to reduce cost without touching output quality.
A typical routing setup:
- Haiku - Classification, routing decisions, simple extraction, anything where speed matters more than nuance
- Sonnet - The default for most production tasks: complex reasoning, code generation, document analysis, multi-step workflows
- Opus - Reserved for genuinely high-stakes tasks where the cost premium is justified by quality requirements
We build routing layers that classify each incoming request and dispatch to the appropriate model. Most applications can route a significant portion of requests to the cheaper tier without users noticing any difference.
Async and Batch Patterns
Calling an LLM synchronously in a user-facing request path is often the wrong architecture. We restructure workflows to:
- Move non-time-sensitive LLM work off the critical path and process it in the background
- Use the Anthropic Batch API for high-volume async workloads at 50% cost reduction
- Parallelize independent LLM calls instead of running them in sequence
- Stream responses where users need to see output progressively rather than waiting for completion
Cost Monitoring
You can't optimize what you don't measure. We instrument production applications with per-feature token tracking, cost attribution by user or workflow type, alerting on unexpected cost spikes, and dashboards showing cost per output unit over time.
This visibility makes clear where optimization effort will have the most impact - and makes it obvious when a model update or prompt change caused costs to drift.
What an Engagement Looks Like
Audit - We review your current prompt design, model usage, caching strategy (or lack of one), and request patterns. We identify where the waste is.
Prioritize - Not every optimization is worth the engineering time. We scope work by expected impact vs implementation cost and recommend what to tackle first.
Implement - We build and test the changes, measuring before and after on real traffic.
Instrument - We set up cost monitoring so you can see the impact and catch regressions going forward.
We don't quote percentage improvements before we've audited your application - the range is too wide. What we can tell you is that applications that have grown organically almost always have prompt bloat, that most applications haven't implemented caching, and that most use a single model tier for everything. If any of those are true for you, there's room to improve.