Even after the 2026 price cuts, inference remains the largest line item for most AI products, and the teams with healthy margins share a toolkit: batch what can be batched, cache what repeats, and route each request to the cheapest model that clears the quality bar. None of the three is exotic; all three are usually left on the table.
Cache what repeats
Semantic caching of prompt-response pairs removes entire classes of cost: FAQs, schema explanations, and formatted conversions are answered from cache. Use embedding similarity rather than exact matching so phrasing variations still hit, and set a TTL so cached answers cannot go stale.
Route by difficulty
Send classification and extraction tasks to small cheap models and reserve the frontier model for genuinely hard reasoning. A simple router based on prompt length and task type typically moves 60 to 80 percent of traffic to the cheap tier with no visible quality change.
Batch and truncate deliberately
Batch requests where latency allows, trim over-long system prompts, and cut few-shot examples that do not change outcomes. These unglamorous edits compound: most teams find a third of their prompt tokens were never influencing the answer.
Caching: the first dollar to save
Semantic caching — serving a previously computed answer for a meaningfully similar request — eliminates repeat spend on the questions every user asks. Start exact-match on normalized prompts, then add embedding-similarity matching with a conservative threshold and short TTL so cached answers cannot outlive their accuracy. Prompt-prefix caching offered by providers compounds this: keep system prompts stable and every request gets cheaper automatically.
Routing and batching in practice
A router classifies each request — simple classification, extraction, summarization — and dispatches to the cheapest qualified model, escalating only when confidence is low. Measure quality per route with your eval set so cost savings never hide quality regressions. For asynchronous workloads like enrichment and evaluation runs, batching requests into the provider's batch endpoints routinely halves cost. Together these typically cut spend 40 to 70% before any token-level optimization.
Key takeaways
- Semantic caching with short TTLs is the fastest cost reduction available.
- Stable prompt prefixes unlock provider prefix caching automatically.
- Route per-request to the cheapest qualified model; verify with evals.
Final thoughts
Inference cost management is now a permanent engineering discipline rather than a one-time negotiation, and the winning pattern is layered: cache, then route, then batch — each with quality checks proving the savings are real.