How CodePilot AI Cut API Costs by 87% Using llmair

When we launched CodePilot in January, we were burning$7,200/month on OpenAI API calls. Our SaaS tool helps small teams automate boilerplate code — mostly simple, high-volume requests. We were using GPT-4o for everything, and the economics didn't work.

At 500K requests per month, even with OpenAI's volume discounts, we were looking at a$14.40/K minimum cost just for input tokens. Add output and we're at $30-50K/month for a product priced at $49/seat.

The Problem: Wrong Model for the Task

Here's what we realized after three months: 80% of our requests were simple transformations — renaming variables, adding comments, converting TypeScript to Python, summarizing code. These tasks don't need GPT-4o's capability. They need something fast and cheap.

We tried prompting GPT-4o Mini to be "more concise" but the quality suffered. We needed a model that could handle straightforward tasks at 1/10th the cost.

The Solution: llmair + DeepSeek

We switched to llmair in April. The migration tookone afternoon — we just changed the base URL from OpenAI's endpoint to https://api.llmair.ai/v1 and kept the OpenAI SDK. No code refactoring.

We now route based on request complexity:

• DeepSeek V3 — for code transformations, summarization, comment generation. $0.50/1M input, $2.00/1M output. Handles 90% of our volume.
• Qwen3 32B — for anything requiring reasoning. $0.15/1M input. Still 95% cheaper than GPT-4o.
• llmair Failover — when a provider is slow, it automatically routes to the next best model. Zero downtime since we switched.

The Numbers

After one month on llmair, here's the comparison:

The Unexpected Win: Latency

We expected cost savings — we didn't expectP95 latency to actually improve. DeepSeek V3 responses are faster for our use case because the models are optimized for throughput. Our customers noticed before we did.

Customer satisfaction scores went from 7.2 to 8.6. Three customers mentioned "faster responses" in their NPS comments. One cancelled subscription was re-activated after we announced the speed improvement.

The Architecture

Here's the routing logic we built in about 20 lines of Python:

def route_request(prompt: str, complexity: str):
    if complexity == "low":
        return "deepseek-ai/DeepSeek-V3"
    elif complexity == "medium":
        return "Qwen/Qwen3-32B"
    else:
        return "Pro/deepseek-ai/DeepSeek-R1"

We use prompt classification to decide which model handles each request. Simple renames → DeepSeek. Conditional logic → Qwen3. Multi-file refactors → DeepSeek R1 Pro.

What We'd Tell Other Teams

The biggest lesson: model routing isn't premature optimization. It's the difference between a profitable AI product and one that burns money on every request.