Guide · Cost Optimization

How to Reduce LLM API Costs: 10 Tactics Ranked by Impact

Most teams overpay on AI APIs by 40–80%. These ten tactics — ordered roughly by effort-to-savings ratio — fix that without hurting answer quality.

1 · Right-size the model per task

The single biggest lever. Route classification, routing, extraction and short-form replies to mini/flash models ($0.15–$1/M) and reserve flagships ($2.50–$15/M input) for genuinely hard reasoning. A two-tier setup routinely cuts spend 5–20× on the cheap tier's traffic.

2 · Turn on prompt caching

Repeated system prompts and documents are pure waste at full price. Caching drops repeated input by 50–90% — see our prompt caching guide for provider specifics.

3 · Use batch mode for async work

Backfills, embeddings refreshes, evals and nightly digests don't need real-time. Batch endpoints process within 24 hours at roughly half price on OpenAI-compatible APIs.

4 · Cap output tokens

Output is the expensive side of every call. Set max_output_tokens to a realistic ceiling and ask for concise formats ("reply in under 100 words") — savings of 20–50% on chatty workloads are common.

5 · Trim conversation history

Multi-turn chats re-send the whole transcript every turn — cost grows quadratically. Summarize old turns, keep only the last N messages verbatim, or offload history to retrieval. At 20+ turns this alone can save 60%+.

6 · Compress what you send

Strip boilerplate, deduplicate pasted logs, convert tables to compact formats, and retrieve only relevant chunks instead of stuffing entire documents. Every token you don't send is a 100% discount.

7 · Cache application-side

Identical questions deserve identical answers. Hash the normalized prompt and serve repeats from your own cache (Redis + TTL). Hit rates of 10–30% are typical in support and search scenarios — that's direct API savings.

8 · Consider open-weight models

Llama, DeepSeek, Mistral and Kimi models served hosted often undercut frontier APIs by 2–10× for mid-difficulty tasks. Benchmark quality on your own evals before switching anything user-facing.

9 · Set budgets and alerts

Provider dashboards let you set monthly hard limits and alert thresholds. One runaway retry loop can cost more than a month of optimization — guardrails first.

10 · Measure per-feature unit economics

Attribute token spend to features, not accounts. Knowing that "summarize thread" costs $0.004/call while "generate report" costs $0.09 tells you exactly where optimization effort pays. Our free token calculator gives you the per-call number in seconds, and the model pricing table keeps rates honest.

Start with the big three Model right-sizing + caching + output caps typically deliver 70% of total savings in an afternoon of work. The rest is iteration.

Cost reduction FAQ

What is the fastest way to lower LLM costs?

Route simple requests to a mini/flash-class model. Flagship models cost 10–60× more than small ones (GPT-4o: $2.50/M input vs GPT-4o mini: $0.15/M), and most production traffic — classification, extraction, short replies — does not need a flagship.

How much can prompt caching save?

Cached input tokens cost 50–90% less than base input depending on provider. Apps with long stable system prompts typically see 30–60% total input savings.

Does batch mode really halve costs?

Yes — OpenAI and several providers offer ~50% off for asynchronous batch jobs processed within 24 hours. Perfect for backfills, evaluations and nightly pipelines; not for interactive features.

Should I cap max output tokens?

Always. Output tokens cost 3–5× more than input, and uncapped generations drift into verbosity. Setting a realistic cap (e.g. 300 tokens for summaries) prevents runaway spend with zero quality loss.