From overspend to optimized in 5 steps
We benchmark your real production workloads against the full model landscape , then hand you a concrete roadmap to cut LLM costs by 40%+ without touching quality.
Map Your AI Workloads
We start by cataloguing every LLM-powered workflow in your stack , what it does, which model it uses, and what you're paying for it.
- Audit existing LLM integrations and API usage
- Classify workloads: RAG/Search, Extraction, Creative/Reasoning
- Document current model choices and monthly spend
- Identify highest-cost and highest-risk workloads first
Current AI Workloads
Internal Search & RAG
GPT-4o
$4,200/mo
High spendData Extraction Pipeline
GPT-4o
$6,100/mo
High spendContent Generation
Claude 3.5
$2,800/mo
Med spend$13,100/mo total
Define Quality Criteria
For each workload we define exactly what 'good enough' means , so benchmark scores are tied to your real business requirements, not generic leaderboard metrics.
- Set workload-specific quality dimensions
- Define minimum acceptable thresholds per metric
- Align quality bars with business impact (accuracy vs. speed vs. cost)
- Create golden test sets from your real production prompts
Quality Thresholds , Internal Search
Answer Relevance
CriticalContext Faithfulness
CriticalHallucination Rate
CriticalLatency (p95)
HighRun Side-by-Side Benchmarks
We test 5+ candidate models against your real prompts and production data , so you see exactly how each one performs on your specific tasks.
- Parallel evaluation across 5+ models
- Uses your actual production prompts, not synthetic data
- Deterministic, reproducible runs for fair comparison
- Real-time progress with per-metric breakdowns
Benchmark Running , Internal Search
Analyze Cost–Quality Tradeoffs
The benchmark results are mapped against each model's API cost , surfacing exactly where you can switch to a cheaper model without sacrificing the quality that matters.
- Cost per 1M tokens mapped to quality scores
- Automatic identification of 'dominated' choices
- Projected monthly savings per workload
- Risk-adjusted recommendations (critical vs. low-stakes tasks)
Cost vs. Quality , Internal Search
GPT-4o
$15.00/M tokens
91%
quality score
Gemini 2.0 Pro
$7.00/M tokens
89%
quality score
Llama 3.1 70B
$0.90/M tokens
83%
quality score
Gemini 2.0 Pro delivers 97.8% of GPT-4o quality at 47% of the cost.
Get Your Optimization Roadmap
You receive a concrete, prioritized roadmap: which model to deploy on which workload, what to expect in savings, and how to monitor quality after rollout.
- Prioritized list of model swaps by ROI
- Projected annual savings with confidence range
- Implementation guide per workload
- Ongoing monitoring setup to catch quality drift
Optimization Roadmap
Priority 1
Data Extraction
GPT-4o → Gemini Pro
$2,900/mo
Priority 2
Internal Search
GPT-4o → Gemini Pro
$1,800/mo
Priority 3
Content Generation
Claude 3.5 → Mistral Large
$900/mo
$68,400
Projected annual savings
Start with a free ecosystem assessment
We'll map your workloads, run the benchmarks, and deliver your optimization roadmap in 2 weeks , at no cost.