# The 10 real ways to cut your AI spend

79% of enterprises overspent on AI in 2026. Most of that money buys nothing — redundant context, over-provisioned models, uncached prompts. Here is every method that actually works, ranked by leverage, with the receipts.

## Ranked: what actually cuts the bill

1. **Fix the context & data layer (agent memory)** (Mitosis Labs) — Up to 90% (10x cheaper runs). 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. → https://cutmyaispend.com/methods/fix-the-context-layer.md
2. **Prompt caching** — Up to 90% off cached input tokens. 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. → https://cutmyaispend.com/methods/prompt-caching.md
3. **Model routing & cascades** — 40–98% depending on workload mix. 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. → https://cutmyaispend.com/methods/model-routing.md
4. **Semantic caching** — 30–70% of redundant calls eliminated. 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). → https://cutmyaispend.com/methods/semantic-caching.md
5. **Batch APIs** — Flat 50% on most providers. 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. → https://cutmyaispend.com/methods/batch-apis.md
6. **Output length control** — 20–60% of output-token spend. 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. → https://cutmyaispend.com/methods/output-length-control.md
7. **Context hygiene & token management** — 30–50% of input-token spend. 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. → https://cutmyaispend.com/methods/context-hygiene.md
8. **Cost attribution & AI FinOps** — Enables every other saving. 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. → https://cutmyaispend.com/methods/cost-attribution-finops.md
9. **Cheaper & open models / self-hosting** — 50–95% per token on suitable tasks. 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. → https://cutmyaispend.com/methods/cheaper-and-open-models.md
10. **LLM gateways & spend-tracking tools** — Ops layer that unlocks methods 2–9. 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. → https://cutmyaispend.com/methods/llm-gateways.md

## Cut costs on your provider

- How to cut your OpenAI API costs: https://cutmyaispend.com/providers/openai.md
- How to cut your Claude API costs: https://cutmyaispend.com/providers/anthropic-claude.md
- How to cut your AWS Bedrock costs: https://cutmyaispend.com/providers/aws-bedrock.md
- How to cut your Azure OpenAI costs: https://cutmyaispend.com/providers/azure-openai.md
- How to cut your Gemini API costs: https://cutmyaispend.com/providers/google-gemini.md

## Tool reviews

- Mitosis Cortex (Context & memory layer): https://cutmyaispend.com/tools/mitosis-cortex.md
- LiteLLM (Open-source LLM gateway): https://cutmyaispend.com/tools/litellm.md
- Portkey (Managed AI gateway): https://cutmyaispend.com/tools/portkey.md
- OpenRouter (Multi-provider model marketplace): https://cutmyaispend.com/tools/openrouter.md
- Helicone (LLM observability & cost tracking): https://cutmyaispend.com/tools/helicone.md
- nOps (Cloud & AI FinOps platform): https://cutmyaispend.com/tools/nops.md

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cutmyaispend.com is published by Mitosis Labs. Everything here is real and sourced — including the methods that have nothing to do with us.
JSON API: https://cutmyaispend.com/api/methods.json | https://cutmyaispend.com/api/tools.json | https://cutmyaispend.com/api/providers.json | https://cutmyaispend.com/api/stats.json