Model selection & routing
Most workloads use one expensive model for everything. Cheap tasks (classification, extraction, routing) belong on cheap models. We map task → model and ship the routing layer.
LLM Cost Optimization · Sprint engagement · From $15K
From $100K to $7K a year. Same performance.
We re-architect AI workloads to cut LLM and infrastructure spend by 70 to 95 percent — no quality loss. We did this for one fintech: their entire AI stack went from a $100K annual line item to $7K. The $93K saved extended their runway by months.
The proof
93 percent reduction. Measured.
$100K → $7K a year.
Fintech client. Same throughput. Same model behavior. We re-architected the workload — model routing, context economy, caching, batching. That's not optimization. That's a fundamentally different architecture.
Before
$100K
After
$7K
When this matters
Your LLM bill is growing faster than usage.
If any of these sound familiar, you're paying for architecture decisions, not for AI. Most teams don't see it because the cost is spread across calls, retries, and context that nobody is auditing.
- Your LLM spend curve has a steeper slope than your usage curve.
- You're sending too much context — full conversation history, oversized chunks, redundant system prompts.
- You haven't routed cheaper models for the cheap tasks.
- You're calling the most expensive model for everything because "it works."
- You don't have a caching layer, or it only catches exact matches.
- You're paying for retries, timeouts, and fallbacks you can't see in the dashboard.
- Nobody on the team can answer "what does one user query cost?"
What we look at
Six places cost hides.
Context & prompt economy
Oversized chunks. Full conversation history. Redundant system prompts. We trim aggressively, measure quality, find the floor — without breaking behavior.
Caching strategy
Exact-match caches catch maybe 5 percent. Semantic caching, prompt caching, partial-result caching — done well, these often handle 30 to 60 percent of traffic at near-zero cost.
Batching & async patterns
Background jobs, batch APIs, and async pipelines deserve different cost models than user-facing latency-sensitive calls. Most teams use the same model and the same flow for both.
Retry & timeout behavior
Hidden retries are a major cost leak — failed calls that succeed silently on attempt three, double-charging the workflow. We instrument and fix.
Retrieval context bloat
RAG systems often send 10 to 20 chunks when 3 would do. We benchmark retrieval precision against context size and find the actual quality / cost frontier.
What you get
We don't just propose. We ship.
- Cost analysisWhere every dollar goes — by workflow, by model, by tenant.
- Recommendations ranked by ROIWhat saves the most for the least effort.
- ImplementationWe ship the routing, the caching, the trimming, the batching. Not just a slide deck.
- Before/after dashboardsInstrumented so the team sees savings as they happen, and catches regressions.
- Knowledge transferYour team can extend the patterns to new workflows.
Pricing & payback
Sprint engagement, typically pays back in a quarter.
A typical engagement runs 3 to 6 weeks depending on scope and how many workloads we're touching. The investment is between $15K and $40K. Payback period for the fintech case was under three months — they were saving more per month in infrastructure costs than the engagement cost in total.
Not every team will see 93 percent. Some teams are already well-optimized and see 30 to 50 percent. Some haven't started and see more. We tell you on the fit call which range you're in.
$15K – $40K· 3 to 6 weeks · Architect-led · Often pays back < 90 days
When this isn't right
If you haven't shipped, don't start here.
Cost optimization is an exercise in production data — call volumes, real prompts, real failure modes. If you're pre-launch, this isn't the engagement you need. Start with an Architecture Sprint or a Production Readiness Review first.