The 10 real ways to cut your AI spend

Ranked: what actually cuts the bill

Ordered by leverage: #1 shrinks what you need to spend; the rest discount what you already spend.

1. 1

   Fix the context & data layer (agent memory) Mitosis Labs

   The single biggest driver of AI overspend is not model pricing — it is agents and copilots re-reading, re-fetching, and re-deriving the same context on every single run. Every "what does this company do", every re-crawled doc, every re-summarized thread is paid for again and again in tokens.

   Up to 90% (10x cheaper runs) Low — connect your data, agents remember it
2. 2

   Prompt caching

   Prompt caching reuses the computed state behind a repeated prompt prefix (system prompt, tool definitions, long documents), so the static part of every request bills at a steep discount — up to 90% off on Anthropic with explicit cache breakpoints, and ~50% automatically on OpenAI.

   Up to 90% off cached input tokens Low — order your prompts, flip a flag
3. 3

   Model routing & cascades

   Most requests do not need your most expensive model. Routing sends simple queries to cheap models (Haiku, GPT-mini class, Nova) and reserves frontier models for the requests that actually need them; cascade designs try cheap first and escalate only on failure.

   40–98% depending on workload mix Medium — needs routing logic and evals
4. 4

   Semantic caching

   Exact-match caches miss paraphrases. Semantic caching embeds incoming queries and serves a stored answer when a new query is similar enough to a previous one — eliminating 30–70% of redundant API calls in workloads where users ask the same things in different words (support, search, FAQ-style traffic).

   30–70% of redundant calls eliminated Medium — embedding store + similarity threshold
5. 5

   Batch APIs

   OpenAI, Anthropic, and Google all offer batch endpoints at roughly 50% off in exchange for asynchronous processing (typically completed well within 24 hours, often much faster). Any workload that is not user-facing-realtime — enrichment, classification, embeddings backfills, evals, report generation — is leaving money on the table if it runs through the synchronous API.

   Flat 50% on most providers Low — if your workload tolerates async
6. 6

   Output length control

   Output tokens cost 3–8× more than input tokens (median ratio ~4:1). Verbose answers, unrequested explanations, and repeated boilerplate are billed at the premium rate. Tightening what the model is allowed to say is one of the cheapest wins available.

   20–60% of output-token spend Low — prompt and max_tokens changes
7. 7

   Context hygiene & token management

   Chat histories grow without bound, RAG pipelines stuff 20 chunks where 3 would do, and agents drag full tool outputs through every subsequent turn. Input-side bloat is the quiet half of most AI bills.

   30–50% of input-token spend Medium — ongoing discipline
8. 8

   Cost attribution & AI FinOps

   73–79% of enterprises blew their AI budgets in 2026, and the most common root cause is that spend shows up as one opaque line item (OpenAI, Anthropic, Bedrock) with no mapping to features, teams, or customers. You cannot cut what you cannot see.

   Enables every other saving Medium — tagging + dashboards
9. 9

   Cheaper & open models / self-hosting

   Frontier-model prices keep falling, and small models (Haiku-class, GPT-mini-class, Nova, open Llama/Qwen/Mistral weights) now handle classification, extraction, and routine drafting at a tiny fraction of frontier price. For high-volume, well-scoped tasks, a fine-tuned small model regularly beats a prompted frontier model on cost and matches it on quality.

   50–95% per token on suitable tasks High for self-hosting, low for switching
10. 10

    LLM gateways & spend-tracking tools

    A gateway (LiteLLM, Portkey, OpenRouter) gives you one API across providers plus the control points every other method needs: caching, routing, fallbacks, budgets, rate limits, and per-request cost logging. Spend-tracking layers (Helicone — now maintenance-only after its Mintlify acquisition — nOps, native provider dashboards) add the visibility.

    Ops layer that unlocks methods 2–9 Low — mostly a proxy swap

Cut costs on your provider

Provider-specific playbooks with the levers that matter most on each platform.

Tool reviews

The gateways, caches, and FinOps layers that make the savings systematic — honestly reviewed.