Where the Money Actually Goes
AI API spend is one of the fastest-growing, least-governed lines in an engineering budget. The good news: most overspend comes from a handful of fixable habits, not from the sticker price. Here are seven strategies, roughly in order of ROI, with the real levers each one pulls.
1. Route Simple Tasks to Cheaper Models
The single biggest lever. Model tiers span a 5–25x cost range, and most workloads send everything to the flagship out of habit. Send classification, extraction, routing, and summarization to a budget model — DeepSeek V4 Flash ($0.14/$0.28), Claude Haiku 4.5 ($1/$5), or Gemini Flash — and reserve premium models like Claude Opus 4.8 or GPT-5.x for genuinely hard prompts. A cheap-default-with-premium-escalation router is the highest-ROI change most teams can make.
2. Turn On Prompt Caching
Chatbots and agents resend the same system prompt and context on every call. Prompt caching stores it and serves repeats at up to ~90% off input. On Claude, a cached Opus read is ~$0.50/MTok instead of $5.00; DeepSeek's automatic prefix caching drops repeated input ~98%. Structure prompts so the stable part (system instructions, documents) comes first and hits cache. This is often a same-day 30–60% cut on input costs for conversational apps.
3. Batch Everything Non-Urgent
Every major provider offers a Batch API at ~50% off both input and output, with results within 24 hours. Overnight content generation, bulk classification, nightly image/video refreshes, embeddings backfills — anything a human isn't waiting on — should run through Batch. It stacks with caching, and on image models like GPT Image 2 or Nano Banana Pro it halves per-image cost outright.
4. Right-Size Resolution and Quality
For generation models, resolution is a cost multiplier that scales worse than linearly. A 4K image can cost 5–10x a 1024px one; native-4K video costs far more than 1080p. A 16:9 web hero rarely needs 4K. Match output to the destination, not the maximum — draft in low/fast tiers, render finals in high tiers only for assets that ship. On video, disabling native audio when you don't need it can cut 30–50% on some models.
5. Trim Context and Output
You pay for every token in and out. Prune retrieved context to what's relevant instead of stuffing the window; cap max_tokens so a verbose model doesn't run long; and prefer structured output over prose when you'll parse it anyway. On output-heavy agent loops, output tokens — not input — are usually the bill driver, so watch reasoning/"thinking" modes, which can consume 3–5x more tokens at the same rate.
6. Use a Discounted Gateway
Buying models through an aggregator that pre-negotiates volume pricing passes a structural discount to you with no code change beyond a base URL. LinkModel, for example, prices generation models up to 30% below official rates with fixed per-request pricing (no GPU-second variance), one key across providers, and the price shown before each call. On a $10k/month generation bill, a 25–30% markdown is $2,500–$3,000/month — before any of the levers above. Fixed pricing also removes the forecasting risk of per-GPU-second platforms; see LinkModel vs fal.ai.
7. Measure Per-Feature, Not Just Per-Month
You can't cut what you can't see. "$14,000 in API usage" hides the staging experiment that finished last Tuesday but never got turned off. Track spend by model, feature, and environment, set budgets and alerts, and review the top cost drivers weekly. Most gateways and providers expose per-request cost and usage dashboards — use them.
Stacking It Up
These compound. Caching (up to 90% on input) plus Batch (50%) can drop effective spend 95%+ on the right workload; add model routing and a gateway discount on top and the flagship-everything baseline you started with looks unrecognizable. Start with routing and caching — the two biggest, fastest wins — then layer the rest.
For the numbers behind the model choices, see the AI API pricing comparison and the cheapest AI API guide.
Start on LinkModel with a $1 credit and compare your real per-call cost against your current provider — no card, no commitment.