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I looked at our monthly AI bill and felt sick. We spent thousands of dollars every month on a product that did not need to cost that much. I was the one who approved the design.
I spent six months rebuilding our LLM layer. I dropped costs by 95%. I also improved quality in several areas.
If you run AI features at scale, these steps will save you money.
- Measure your spending
You cannot optimize what you do not measure. I tagged every LLM call with three things: โข Model used โข Input token count โข Output token count โข Task label (classification, chat, etc.)
We found that 70% of our requests were simple tasks. We were using GPT-4o for work a cheap model could do easily.
- Use the right model for the task
The default model is often the wrong choice. Routing requests to the correct model saves 90% on those workloads.
โข Simple chat: Use DeepSeek V4 Flash instead of GPT-4o. โข Classification: Use Qwen3-8B instead of GPT-4o-mini. โข Code generation: Use DeepSeek Coder instead of GPT-4o. โข Summarization: Use Qwen3-32B instead of GPT-4o.
- Use tiered routing
Stop guessing which model to use. Let your system decide. Use a "try cheap, escalate on failure" pattern.
โข 80% of traffic should use a $0.01/M model. โข 15% should escalate to a $0.25/M model. โข 5% should go to a premium reasoning model.
This approach reduced our support chatbot costs from $420 to $28 per month.
- Implement caching
Many users ask the same questions. Caching identical prompts costs zero dollars. A simple TTL cache can cut a huge slice off your bill. Put caching in front of your routing logic.
- Compress your prompts
A long system prompt costs money every single time you call the API. If you compress a 2,000-token prompt to 400 tokens, you save massive amounts of money over time. Use a cheap model to summarize your context before sending it to an expensive model.
- Batch your requests
If you process lists of items, do not make 500 separate API calls. Put all items into one prompt. This reduces network trips and saves 10-20% on costs.
- Avoid vendor lock-in
Do not rely on a single provider. Build a unified API layer. This allows you to switch providers in an afternoon if prices rise or outages happen. Avoid features that only one provider offers.
By combining these steps, we reduced our total LLM spend by 95%. This gave us 20x more room to grow our product without increasing our budget.
Source: https://dev.to/gentleforge/how-i-cut-our-ai-api-bill-by-95-a-practical-guide-for-2026-2djb