Reduce Your LLM Costs by 50-70%
Most teams overspend on AI inference by 3-5x. We optimize your model selection, prompts, and caching to cut costs dramatically — without sacrificing output quality.
How We Cut Costs
Three Levers That Save 50-70%
Dynamic Model Routing
Route queries to the right model based on complexity. Simple queries hit fast, cheap models. Complex ones get routed to powerful models. Save 40-60% on inference costs without quality loss.
Prompt & Token Optimization
Reduce token consumption through prompt engineering, structured outputs, context window management, and response compression. Cut input tokens by 30-50% while maintaining output quality.
Caching & Batching
Implement semantic caching, prompt caching, and intelligent batching to eliminate redundant LLM calls. Achieve 90%+ cache hit rates on common query patterns.
Results
Proven Savings
50-70%
Cost reduction achieved
90%+
Cache hit rates
40%
Savings from model routing alone
0%
Quality degradation
Our Process
How We Optimize Your Costs
Audit your current LLM spend
We analyze your API usage, token patterns, model selection, and identify the biggest cost drivers. Most teams are overspending by 3-5x on inference.
Implement dynamic model routing
Route 60-80% of queries to smaller, faster models. Reserve expensive models for tasks that genuinely need them. This alone typically saves 40%.
Optimize prompts and caching
Reduce token consumption through prompt engineering and semantic caching. Eliminate redundant calls with intelligent result caching.
The result
50-70% reduction in LLM costs, often within the first month of deployment. The savings typically pay for the engagement within 2-3 months.
Technologies
Providers We Optimize
Case Studies
Real-World Savings
Dynamic Model Switching with LangGraph
Implemented intelligent model routing that reduced inference costs by 40% while maintaining output quality across multiple LLM providers.
LangChain Platform Migration
Migrated and optimized a legacy LLM platform, achieving 50-70% cost reduction through caching, batching, and prompt optimization.
FAQ
Common Questions
How much can I actually save on LLM costs?
Most teams we work with achieve 50-70% cost reduction. The savings come from three main areas: dynamic model routing (40% savings by using cheaper models for simple queries), prompt optimization (20-30% savings from reducing token consumption), and caching (eliminating 60-90% of redundant API calls). The exact savings depend on your usage patterns, but we have not yet worked with a team where we could not find significant savings.
Will cost optimization reduce the quality of AI outputs?
No. Our approach is quality-preserving by design. Dynamic model routing sends complex queries to the most capable models — it only uses cheaper models for queries where they perform equally well. We set up evaluation frameworks to measure quality before and after optimization, so you can verify there is no degradation.
How long does an LLM cost optimization engagement take?
A typical engagement takes 4-6 weeks. Week 1 is the audit (analyzing your current usage and identifying opportunities). Weeks 2-4 are implementation (model routing, caching, prompt optimization). Weeks 5-6 are monitoring and fine-tuning. You will see cost savings starting from week 2-3.
Do you work with all LLM providers?
Yes. We optimize across OpenAI (GPT-4, GPT-4o, GPT-4o-mini), Anthropic (Claude), Google (Gemini), and open-source models. Our model routing approach is provider-agnostic — we help you use the right model for each task regardless of provider.
What if we are already using the cheapest models?
Even teams using cheap models overspend significantly. The biggest savings usually come from caching (eliminating redundant calls), prompt optimization (reducing input tokens by 30-50%), and batching (reducing per-request overhead). Model choice is just one lever — and often not the biggest one.
Ready to cut your AI costs?
Most teams are overspending by 3-5x on LLM inference. Let's audit your usage and show you exactly where the savings are.