I Cut Our AI Costs by 73% in One Week — Here's How
Our LLM bill was $12,400/month. Seven days later, it was $3,350 — without changing a single user-facing feature. Here's the exact playbook.
NR
NeuralRouting Team
April 10, 2026
Our LLM bill was $12,400 in March. By the end of the first week of April, the run rate was $3,350/month. Same features, same output quality, same users. Nobody noticed the change — except finance.
This isn''t a theoretical exercise. It''s a step-by-step account of what I did, what I found, and how the numbers moved each day.
Day 0: The audit
Before touching anything, I needed to understand where the money was going.
I pulled a week of API logs — every request, every model, every token count. The breakdown was sobering:
Total requests/week: ~84,000
Model used: GPT-4o for 100% of requests
Average input tokens/request: ~800
Average output tokens/request: ~350
Weekly cost: ~$3,100 (extrapolating to $12,400/month)
Then I categorized the requests by what they were actually doing:
Task Type
% of Requests
Example
Classification/routing
28%
Categorize support tickets into 6 types
Entity extraction
22%
Pull names, dates, amounts from text
Simple Q&A
18%
Answer FAQs from a knowledge base
Summarization
15%
Condense customer conversations
Complex generation
12%
Draft detailed responses, reports
Multi-step reasoning
5%
Analyze data, draw conclusions
50% of our requests were classification and extraction. Tasks where the output is a single label or a structured data object. Tasks that Llama 3.1 8B handles identically to GPT-4o.
Another 18% were FAQ-style Q&A — essentially lookup against known content. GPT-4o-mini handles this without breaking a sweat.
Only 17% of our traffic genuinely benefited from GPT-4o''s reasoning capabilities. We were paying premium rates on 83% of requests for no quality gain.
The routing logic started simple: I wrote a fast classifier that looked at the task type (extracted from the system prompt or API endpoint) and routed accordingly. Classification endpoints → Tier 1. Summarization → Tier 2. Generation → Tier 3.
Total setup time: about 6 hours. Most of that was mapping our internal API endpoints to task types and testing that the economy models produced acceptable output.
Day 3: First results
After 24 hours of routed traffic:
Tier
% of Traffic
Daily Cost
Economy (Llama 3.1 8B)
50%
$1.20
Mid-range (GPT-4o-mini)
33%
$14.80
Premium (GPT-4o)
17%
$67.50
Total
$83.50/day
Previous daily cost: ~$443/day. New daily cost: $83.50/day. That''s an 81% reduction on day one.
But I wasn''t celebrating yet. The numbers meant nothing if quality had degraded.
Day 3-4: Quality validation
I ran a shadow comparison. For every economy-tier response, I also sent the same prompt to GPT-4o in the background and compared outputs.
The results for classification tasks: 97.3% agreement between Llama 3.1 8B and GPT-4o. The 2.7% divergence was almost entirely on edge cases where even GPT-4o''s classification was arguable.
For extraction tasks: 99.1% agreement. Dates, names, amounts — the economy model was functionally identical.
For simple Q&A: 94.8% agreement on GPT-4o-mini. The 5.2% gap was on questions that required more nuance. I adjusted the routing to escalate ambiguous Q&A to GPT-4o, which moved about 3% of traffic from Tier 2 to Tier 3.
After the adjustment, the effective accuracy was above 98% across all tiers.
Day 5: Adding semantic caching
While reviewing the logs, I noticed heavy repetition. Our support bot was getting variations of the same 50-60 questions, worded differently each time. Each variation was a fresh API call.
I added a semantic cache layer:
Exact match cache: Hash the prompt, store the response. If the identical prompt comes in, return the cached response. Hit rate: ~12%.
Similarity cache: Embed prompts with a lightweight model, store in a vector database. If a new prompt is semantically similar (cosine similarity > 0.95) to a cached one, return the cached response. Hit rate: ~22%.
Combined cache hit rate: 34%. Meaning 34% of requests never reached an LLM at all. Cost for those requests: $0.
Day 6-7: Final numbers
After a full week of routing + caching:
Component
Impact
Model routing (3-tier cascade)
-81%
Semantic caching (exact + vector)
-34% of remaining calls
Prompt optimization (trimmed system prompts)
-15% input tokens
Combined new run rate
~$3,350/month
From $12,400 to $3,350. A 73% reduction in total LLM costs.
And the quality metrics I tracked throughout the week:
Metric
Before
After
Classification accuracy
94.2%
94.0%
Extraction accuracy
97.8%
97.6%
User satisfaction (CSAT on support)
4.3/5
4.3/5
Average response latency
1.8s
1.2s
Quality was flat. Latency actually improved because economy models on Groq respond faster than GPT-4o. Users didn''t notice anything — except the support bot was slightly snappier.
What I''d do differently
If I did this again, I''d skip the manual routing logic and use an automated router from day one. Mapping endpoints to task types worked for our codebase, but it doesn''t generalize — and it broke every time we added a new feature. A system that analyzes prompt complexity in real time, like NeuralRouting''s Model Cascading, would have saved me the 6 hours of manual mapping and handled edge cases I hadn''t anticipated.
I''d also start with shadow validation from the beginning, not day 3. The anxiety between "I flipped the switch" and "I''ve confirmed quality is fine" was unnecessary. Shadow Engine-style background validation eliminates that gap entirely.
Your turn
The specific numbers will differ for your workload. Maybe your split is 60% simple / 20% moderate / 20% complex instead of our 50/33/17. Maybe you''re spending $3K/month, not $12K. The principle is the same: if you''re not routing by complexity, you''re overpaying.
Start by measuring your Model Tax — the gap between what you''re spending and what you''d spend with intelligent routing.