AI API costs can spiral fast. A team processing 10 million tokens per day with Claude Opus spends over $1,000/day. These six strategies can cut that by 40-80% without sacrificing quality. Most take less than a day to implement.
40-70%
Model routing
50-90%
Prompt caching
20-50%
Output length control
The cheapest paid model right now is LFM2-8B-A1B at $0.02/M output tokens. See all pricing.
Most teams use one model for everything. Instead, route simple tasks to cheap models and only use expensive ones when you need them.
How to implement
Real-world example
A customer support bot handling 100K requests/day: 85% are simple FAQs routed to Gemini Flash ($0.10/M), 15% are complex issues routed to Claude Sonnet ($3/M). Cost: $45/day instead of $300/day using Sonnet for everything.
If you send the same system prompt or context with every request, you are paying for identical tokens repeatedly. Most providers now offer prompt caching.
How to implement
Real-world example
A coding assistant with a 3,000-token system prompt. With 10K requests/day at $3/M input: normal cost = $90/day. With caching at 90% hit rate and 90% discount: $18/day.
Output tokens cost 2-5x more than input tokens. If your model generates 500 tokens when 100 would suffice, you are overpaying by 4x on the expensive side.
How to implement
Real-world example
A classification endpoint generating 200-token explanations when you only need a category label. Setting max_tokens=20 and asking for just the label: 90% reduction in output tokens.
If your workload can tolerate a delay, batch APIs process requests at half the cost. OpenAI and Anthropic both offer batch endpoints with 50% discounts.
How to implement
Real-world example
Processing 50K product descriptions overnight using GPT-4.1 mini batch API: $0.20/M input + $0.40/M output (50% off regular pricing). Total: ~$30 instead of ~$60.
Self-hosting Llama 4 or Qwen 3 eliminates per-token costs entirely. Your only cost is GPU infrastructure, which becomes cheaper at scale.
How to implement
Real-world example
Processing 50M tokens/day with Claude Sonnet: $150/day ($4,500/month). Self-hosting Llama 4 Maverick on 2x A100: $3,000/month for unlimited tokens. Savings: $1,500/month.
Many user queries are semantically similar. Cache responses to common questions and serve them instantly without making API calls.
How to implement
Real-world example
A customer support bot where 40% of questions are variations of the same 50 topics. With semantic caching: 40% of requests served from cache at near-zero cost.
Where each strategy has the biggest impact, ranked by your current monthly spend.
| Monthly Spend | Best Strategy | Expected Savings |
|---|---|---|
| Under $100 | Switch to budget models + control output length | 30-50% |
| $100 - $1,000 | Prompt caching + model routing | 40-60% |
| $1,000 - $10,000 | Batch processing + semantic caching + routing | 50-70% |
| $10,000+ | Self-host open-source + all of the above | 60-90% |
Use our pricing tools to compare models, estimate monthly costs, and find the most cost-effective option for your workload.
The cheapest option is self-hosting open-source models like Llama 4 or Qwen 3, which eliminates per-token costs entirely. For API access, budget models like Gemini 2.0 Flash ($0.10/M tokens) and GPT-4.1 mini ($0.40/M tokens) offer strong performance at very low cost.
Prompt caching typically reduces input token costs by 75-90% for requests that share the same prefix (system prompt, few-shot examples). If your system prompt is 3,000 tokens and you make 10,000 requests/day, caching can save $50-200/day depending on the model.
Self-hosting becomes cost-effective at roughly 10-50M tokens/day, depending on the model. Below that threshold, API access is usually cheaper when you factor in GPU rental costs, engineering time, and maintenance overhead.
Model routing sends each request to the most appropriate (and cost-effective) model based on the task. Simple classification or FAQ queries go to a cheap model, while complex reasoning or creative tasks go to a more capable (expensive) model. You can implement this with a simple classifier or use platforms like OpenRouter.