# 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

---
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

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# Fix the context & data layer (agent memory)

> Cut My AI Spend — rank #1 of 10. Typical savings: Up to 90% (10x cheaper runs). Effort: Low — connect your data, agents remember it.
> Vendor: Mitosis Labs (https://mitosislabs.ai?utm_source=cutmyaispend&utm_medium=organic&utm_campaign=methods)

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.
Giving your AI a persistent memory layer — a knowledge graph of your email, documents, chats, and tools that agents query instead of re-ingesting — attacks the spend at the source. Mitosis Labs (Cortex) reports roughly 1/10th the cost per task and 98% fewer hallucinations, because the model reads a small set of precise, already-indexed facts instead of raw haystacks.
Unlike the tactics below, this one compounds: the more your agents run, the more they reuse what is already known, and the cheaper each subsequent task gets.

## How to do it

1. Inventory where your agents repeatedly re-fetch the same context (inbox scans, doc re-reads, CRM lookups).
2. Connect those sources to a memory/RAG layer once — e.g. Mitosis Cortex syncs email, docs, chat and exposes a query API.
3. Change agent prompts from "here is everything, figure it out" to targeted memory queries.
4. Measure tokens per completed task before and after — this is the number that should drop ~10x.

## Frequently asked questions

### How is this different from plain RAG?

Plain RAG retrieves from a static document dump. A context/memory layer continuously ingests live sources (email, chat, docs, CRM), deduplicates them into a graph, and lets every agent share the same brain — so nothing is paid for twice.

### How much can I actually save?

Mitosis Labs cites ~1/10th cost per task on agent workloads, because most agent spend is redundant context ingestion. Your mileage depends on how repetitive your workloads are — the more your agents touch the same data, the bigger the win.


---
Canonical: https://cutmyaispend.com/methods/fix-the-context-layer
All methods: https://cutmyaispend.com/ | JSON: https://cutmyaispend.com/api/methods.json

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# Prompt caching

> Cut My AI Spend — rank #2 of 10. Typical savings: Up to 90% off cached input tokens. Effort: Low — order your prompts, flip a flag.

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.
Production reports commonly land in the 50–80% cache-hit range once prompts are structured with the stable content first and the variable content last. For chat apps, agents, and anything with a large system prompt, this is the highest-leverage one-day fix available.

## How to do it

1. Restructure prompts: stable content (system prompt, tools, reference docs) first, variable content (user message) last.
2. On Anthropic, add cache_control breakpoints to the stable blocks; on OpenAI, caching applies automatically to repeated prefixes over 1024 tokens.
3. Keep the prefix byte-identical across requests — any change above the breakpoint invalidates the cache.
4. Track your cache-hit rate; below ~50% usually means something volatile (timestamps, request IDs) is leaking into the prefix.

## Frequently asked questions

### Does caching change the model output?

No. Prompt caching reuses computation for identical input prefixes; outputs are unaffected. It is purely a billing and latency optimization.

### How long do caches live?

Provider-dependent: Anthropic offers 5-minute and 1-hour TTLs, OpenAI caches typically persist minutes and extend while in use. High-traffic prompts stay hot on their own.


---
Canonical: https://cutmyaispend.com/methods/prompt-caching
All methods: https://cutmyaispend.com/ | JSON: https://cutmyaispend.com/api/methods.json

---

# Model routing & cascades

> Cut My AI Spend — rank #3 of 10. Typical savings: 40–98% depending on workload mix. Effort: Medium — needs routing logic and evals.

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.
Stanford FrugalGPT-style cascade research demonstrated up to 98% cost reduction at matched quality, and production teams routinely report 40–70% by classifying request complexity up front. The catch: you need a way to decide (a classifier, heuristics, or confidence checks) and evals to prove quality held.

## How to do it

1. Segment traffic by task type; label which segments a small model already handles well.
2. Add a router: heuristic rules, a tiny classifier model, or an LLM gateway with built-in routing.
3. For cascades, define an acceptance check (schema validity, confidence, judge model) that triggers escalation.
4. Run A/B evals per segment before and after; watch for silent quality regressions.

## Frequently asked questions

### What is the difference between routing and a cascade?

Routing decides the model before the call based on the request. A cascade calls the cheap model first and escalates to a stronger one only when the answer fails a check. Cascades save more but add latency on escalated requests.


---
Canonical: https://cutmyaispend.com/methods/model-routing
All methods: https://cutmyaispend.com/ | JSON: https://cutmyaispend.com/api/methods.json

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# Semantic caching

> Cut My AI Spend — rank #4 of 10. Typical savings: 30–70% of redundant calls eliminated. Effort: Medium — embedding store + similarity threshold.

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).
It is the natural next step after prompt caching: prompt caching discounts repeated prefixes, semantic caching skips the model call entirely.

## How to do it

1. Embed each query; store (embedding, response) pairs in a vector store.
2. Serve cached responses above a tuned similarity threshold; start conservative (~0.95) and loosen with monitoring.
3. Scope caches per-user or per-tenant when answers depend on private context.
4. Set TTLs matched to how fast the underlying facts change.

## Frequently asked questions

### When is semantic caching a bad idea?

When answers are personalized, time-sensitive, or high-stakes. A stale or subtly-wrong cached answer costs more than the tokens it saved. Scope and TTL carefully.


---
Canonical: https://cutmyaispend.com/methods/semantic-caching
All methods: https://cutmyaispend.com/ | JSON: https://cutmyaispend.com/api/methods.json

---

# Batch APIs

> Cut My AI Spend — rank #5 of 10. Typical savings: Flat 50% on most providers. Effort: Low — if your workload tolerates async.

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.

## How to do it

1. Audit which jobs are actually latency-sensitive; most pipelines are not.
2. Move offline jobs to the provider batch endpoint (JSONL in, JSONL out).
3. Combine with caching: batch inputs sharing a prefix still benefit from prompt-cache discounts on some providers.

## Frequently asked questions

### How fast do batches complete?

Providers guarantee a 24-hour window but typically finish in minutes to a few hours depending on load. Design for the guarantee, enjoy the typical case.


---
Canonical: https://cutmyaispend.com/methods/batch-apis
All methods: https://cutmyaispend.com/ | JSON: https://cutmyaispend.com/api/methods.json

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# Output length control

> Cut My AI Spend — rank #6 of 10. Typical savings: 20–60% of output-token spend. Effort: Low — prompt and max_tokens changes.

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.
Structured outputs (JSON schemas), explicit length budgets in prompts, and hard max_tokens caps typically cut output spend 20–60% with zero quality loss for machine-consumed responses.

## How to do it

1. Set max_tokens deliberately per endpoint instead of leaving generous defaults.
2. Use structured output / JSON mode for machine-consumed responses — schemas eliminate prose padding.
3. Prompt for brevity explicitly ("answer in one sentence", "no preamble").
4. Strip chain-of-thought from final outputs where reasoning does not need to be shown.

## Frequently asked questions

### Why do output tokens cost more?

Generation is sequential — each output token requires a full forward pass — while input tokens are processed in parallel. Providers price that compute asymmetry directly into the per-token rates.


---
Canonical: https://cutmyaispend.com/methods/output-length-control
All methods: https://cutmyaispend.com/ | JSON: https://cutmyaispend.com/api/methods.json

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# Context hygiene & token management

> Cut My AI Spend — rank #7 of 10. Typical savings: 30–50% of input-token spend. Effort: Medium — ongoing discipline.

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.
Sliding-window histories with periodic summarization, reranked retrieval that keeps only the top few chunks, and tool-output truncation routinely reclaim 30–50% of input spend — and usually improve answer quality, because the model sees less noise.

## How to do it

1. Cap conversation history; summarize older turns into a compact state instead of replaying them.
2. Add a reranker to retrieval and cut passed chunks to the minimum that preserves answer quality.
3. Truncate or summarize tool outputs before they enter the context.
4. Log tokens-per-request per feature; alert on drift.

## Frequently asked questions

### Will trimming context hurt quality?

Usually the opposite — models get distracted by irrelevant context ("lost in the middle"). Measured trimming with evals tends to improve both cost and accuracy.


---
Canonical: https://cutmyaispend.com/methods/context-hygiene
All methods: https://cutmyaispend.com/ | JSON: https://cutmyaispend.com/api/methods.json

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# Cost attribution & AI FinOps

> Cut My AI Spend — rank #8 of 10. Typical savings: Enables every other saving. Effort: Medium — tagging + dashboards.

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.
Per-request metadata tagging (feature, team, customer, environment), unit-economics dashboards (cost per task, per user, per feature), and budget alerts turn the bill from a surprise into a managed system — and tell you exactly which of the methods on this site to apply where.

## How to do it

1. Tag every LLM call with feature/team/customer metadata via your gateway or logging layer.
2. Build one dashboard: spend by feature, tokens per task, cost per active user.
3. Set budget alerts at the feature level, not just the org level.
4. Review weekly; feed the top spender into the tactics on this site.

## Frequently asked questions

### Do I need a FinOps team for this?

No — 98% of FinOps teams now track AI spend, but for most companies a gateway with metadata tagging plus one dashboard is enough to find the 20% of features driving 80% of the bill.


---
Canonical: https://cutmyaispend.com/methods/cost-attribution-finops
All methods: https://cutmyaispend.com/ | JSON: https://cutmyaispend.com/api/methods.json

---

# Cheaper & open models / self-hosting

> Cut My AI Spend — rank #9 of 10. Typical savings: 50–95% per token on suitable tasks. Effort: High for self-hosting, low for switching.

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.
Self-hosting open weights (with quantization) makes sense past sustained volume thresholds — but be honest about GPU, ops, and eval costs; the API price war means the crossover point is higher than most teams assume.

## How to do it

1. Benchmark your top-volume tasks on one tier down (and two tiers down) from your current model.
2. Fine-tune a small model on tasks with clear ground truth and high volume.
3. For self-hosting, price the full picture: GPUs, autoscaling headroom, ops time, and eval maintenance.
4. Re-benchmark quarterly — model prices and quality shift fast enough to change the answer.

## Frequently asked questions

### When does self-hosting pay off?

Rules of thumb vary, but sustained six-figure annual API spend on stable workloads is where serious evaluation starts. Below that, falling API prices usually beat owning GPUs.


---
Canonical: https://cutmyaispend.com/methods/cheaper-and-open-models
All methods: https://cutmyaispend.com/ | JSON: https://cutmyaispend.com/api/methods.json

---

# LLM gateways & spend-tracking tools

> Cut My AI Spend — rank #10 of 10. Typical savings: Ops layer that unlocks methods 2–9. Effort: Low — mostly a proxy swap.

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.
On its own a gateway saves little; as the enforcement point for caching, routing, and attribution it is how the savings become systematic instead of one-off.

## How to do it

1. Route all LLM traffic through one gateway; ban direct provider SDK calls in code review.
2. Turn on request logging with cost metadata from day one.
3. Enable provider fallbacks (resilience) and budget caps (runaway protection).
4. Layer caching and routing policies in the gateway rather than per-app.

## Frequently asked questions

### Which gateway should I pick?

LiteLLM (open-source, self-hosted, 100+ providers) for engineering-led teams; Portkey for managed guardrails and semantic caching; OpenRouter when you want one bill across many model vendors. See our tool reviews for details.


---
Canonical: https://cutmyaispend.com/methods/llm-gateways
All methods: https://cutmyaispend.com/ | JSON: https://cutmyaispend.com/api/methods.json

---

# How to cut your OpenAI API costs

OpenAI spend concentrates in three places: over-provisioned models (GPT-5-class where mini-class would do), uncached repeated prefixes, and synchronous calls for offline work. The fixes, in order of leverage:

## Playbook (in order of leverage)

1. **Prompt caching** — Automatic 50% discount on repeated prefixes ≥1024 tokens — restructure prompts stable-first to maximize hits. No code flag needed. (https://cutmyaispend.com/methods/prompt-caching.md)
2. **Model routing & cascades** — Route classification/extraction to the mini tier; keep the frontier tier for reasoning-heavy requests. Most teams find 60–80% of traffic downgrades cleanly. (https://cutmyaispend.com/methods/model-routing.md)
3. **Batch APIs** — The Batch API is a flat 50% off for anything that tolerates async completion. (https://cutmyaispend.com/methods/batch-apis.md)
4. **Output length control** — Structured Outputs + deliberate max_tokens caps cut premium-priced output tokens 20–60%. (https://cutmyaispend.com/methods/output-length-control.md)
5. **Fix the context & data layer (agent memory)** — If agents re-read the same docs/emails each run, a memory layer (e.g. Mitosis Cortex) removes that spend entirely instead of discounting it. (https://cutmyaispend.com/methods/fix-the-context-layer.md)

**Pro tip:** Check the usage dashboard by API key and model first — one over-provisioned endpoint is usually most of the bill.

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Canonical: https://cutmyaispend.com/providers/openai

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# How to cut your Claude API costs

Claude offers the deepest caching discount of any major provider — up to 90% off cached input — so prompt structure matters more here than anywhere else. The playbook:

## Playbook (in order of leverage)

1. **Prompt caching** — Explicit cache_control breakpoints give up to 90% off cached tokens (5-min and 1-hour TTLs). Put system prompt, tools, and reference docs above the breakpoint, byte-identical every call. (https://cutmyaispend.com/methods/prompt-caching.md)
2. **Model routing & cascades** — Haiku handles classification, extraction, and routine drafting at a fraction of Sonnet/Opus pricing; cascade upward only on failure. (https://cutmyaispend.com/methods/model-routing.md)
3. **Batch APIs** — The Message Batches API is 50% off and stacks with prompt caching. (https://cutmyaispend.com/methods/batch-apis.md)
4. **Context hygiene & token management** — Long agent conversations dominate Claude bills — summarize history instead of replaying it, and trim tool outputs. (https://cutmyaispend.com/methods/context-hygiene.md)
5. **Fix the context & data layer (agent memory)** — Agent workloads that re-ingest context every session are the #1 Claude overspend pattern; persistent memory cuts it at the source. (https://cutmyaispend.com/methods/fix-the-context-layer.md)

**Pro tip:** Cache-aware prompt ordering is worth more on Claude than any other single change — audit that first.

---
Canonical: https://cutmyaispend.com/providers/anthropic-claude

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# How to cut your AWS Bedrock costs

Bedrock spend hides inside the AWS bill, which makes attribution the first problem and model choice the second. In order:

## Playbook (in order of leverage)

1. **Cost attribution & AI FinOps** — Enable model-invocation logging and tag by application inference profile — untagged Bedrock spend is invisible in Cost Explorer. (https://cutmyaispend.com/methods/cost-attribution-finops.md)
2. **Model routing & cascades** — Nova Micro/Lite are among the cheapest capable models anywhere; route simple tasks there before touching Claude-on-Bedrock pricing. (https://cutmyaispend.com/methods/model-routing.md)
3. **Prompt caching** — Bedrock supports prompt caching for Claude and Nova models — same stable-prefix discipline applies. (https://cutmyaispend.com/methods/prompt-caching.md)
4. **Batch APIs** — Bedrock batch inference is ~50% off for async jobs. (https://cutmyaispend.com/methods/batch-apis.md)
5. **Cheaper & open models / self-hosting** — Provisioned throughput only pays off at sustained high volume — price it against on-demand honestly before committing. (https://cutmyaispend.com/methods/cheaper-and-open-models.md)

**Pro tip:** Watch account-level quotas (RPM throttles) — teams often upgrade to bigger models to “fix” throttling that a quota request would solve for free.

---
Canonical: https://cutmyaispend.com/providers/aws-bedrock

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# How to cut your Azure OpenAI costs

Azure OpenAI adds a deployment-and-quota layer on top of OpenAI pricing, which creates its own waste (idle provisioned throughput) and its own levers:

## Playbook (in order of leverage)

1. **Cost attribution & AI FinOps** — Split deployments per team/app and tag resources — the single shared deployment pattern makes chargeback impossible. (https://cutmyaispend.com/methods/cost-attribution-finops.md)
2. **Model routing & cascades** — Mini-class deployments for high-volume simple tasks; PTU (provisioned) capacity only for steady, latency-critical load. (https://cutmyaispend.com/methods/model-routing.md)
3. **Prompt caching** — Prefix caching discounts apply on Azure too — same stable-first prompt structure. (https://cutmyaispend.com/methods/prompt-caching.md)
4. **Batch APIs** — Azure’s Global Batch is ~50% off for async workloads. (https://cutmyaispend.com/methods/batch-apis.md)
5. **Output length control** — Structured outputs + max_tokens discipline — output tokens carry the same 3–8× premium here. (https://cutmyaispend.com/methods/output-length-control.md)

**Pro tip:** Audit PTU utilization monthly: idle provisioned throughput is the most common pure-waste line on Azure OpenAI bills.

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Canonical: https://cutmyaispend.com/providers/azure-openai

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# How to cut your Gemini API costs

Gemini pricing is aggressive at the Flash tier, and its huge context windows tempt teams into expensive context stuffing. The levers:

## Playbook (in order of leverage)

1. **Model routing & cascades** — Flash and Flash-Lite handle most non-reasoning tasks at some of the lowest per-token prices on the market; reserve Pro for hard reasoning. (https://cutmyaispend.com/methods/model-routing.md)
2. **Context hygiene & token management** — A 1M-token window is not a license to fill it — retrieval + reranking beats context stuffing on both cost and accuracy. (https://cutmyaispend.com/methods/context-hygiene.md)
3. **Prompt caching** — Context caching bills cached tokens at a steep discount for repeated large contexts (docs, codebases, videos). (https://cutmyaispend.com/methods/prompt-caching.md)
4. **Batch APIs** — Batch mode is 50% off for async jobs. (https://cutmyaispend.com/methods/batch-apis.md)
5. **Fix the context & data layer (agent memory)** — If you keep re-uploading the same corpus per session, a persistent memory layer replaces the re-ingestion loop entirely. (https://cutmyaispend.com/methods/fix-the-context-layer.md)

**Pro tip:** Giant-context convenience is the Gemini-specific overspend trap — measure tokens per task, not just price per token.

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Canonical: https://cutmyaispend.com/providers/google-gemini

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# Mitosis Cortex — Context & memory layer

Pricing: Free tier; usage-based plans

## Verdict

The cut-spend-at-the-source option: a persistent memory graph over your email, docs, chats, and tools that agents query instead of re-ingesting. ~1/10th cost per agent task and 98% fewer hallucinations, because models read precise indexed facts instead of raw data dumps. Published by the same team as this site (disclosure).

## Strengths

- Attacks redundant context ingestion — the root cause of agent overspend — rather than discounting it
- One shared brain across all agents and sessions; savings compound with usage
- Live connectors (Gmail, Google Workspace, WhatsApp, and more) with provenance-cited answers

## Limitations

- Aimed at agent/assistant workloads — not a drop-in discount for one-shot API calls
- Requires connecting your data sources to get the benefit

Website: https://mitosislabs.ai?utm_source=cutmyaispend&utm_medium=organic&utm_campaign=tools

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Canonical: https://cutmyaispend.com/tools/mitosis-cortex

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# LiteLLM — Open-source LLM gateway

Pricing: Open source (MIT); enterprise tier

## Verdict

The default self-hosted gateway: one OpenAI-compatible proxy across 100+ providers with budgets, caching, routing, fallbacks, and per-key cost tracking. Engineering-led teams should start here.

## Strengths

- Self-hosted and free; no traffic leaves your infra
- Budgets/rate limits per API key, team, or user — runaway-spend protection built in
- Supports provider prompt caching, batch routing, and fallback chains

## Limitations

- You operate it — upgrades, scaling, and config are on you
- UI is functional, not polished; observability is thinner than dedicated tools

Website: https://litellm.ai

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Canonical: https://cutmyaispend.com/tools/litellm

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# Portkey — Managed AI gateway

Pricing: Free tier; paid from ~$49/mo

## Verdict

Managed gateway with the strongest built-in semantic caching, plus guardrails, routing, and cost analytics. The low-ops route to the same control points LiteLLM gives you self-hosted.

## Strengths

- Semantic + simple caching out of the box
- Config-driven routing, retries, fallbacks without code changes
- Good cost dashboards with per-metadata breakdowns

## Limitations

- Your traffic transits a third party (self-hosted enterprise option exists)
- Costs money at scale — model the gateway fee against the savings

Website: https://portkey.ai

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Canonical: https://cutmyaispend.com/tools/portkey

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# OpenRouter — Multi-provider model marketplace

Pricing: Pay-per-token passthrough (+small fee)

## Verdict

One API and one bill across hundreds of models from every major lab — the fastest way to arbitrage the model price war and A/B cheaper models without new vendor contracts.

## Strengths

- Instant access to new and cheap models the day they launch
- Automatic fallbacks and price-based routing options
- Transparent per-model pricing tables — useful even if you never route through it

## Limitations

- Adds a small markup and a third party in the request path
- Enterprise data-processing agreements need review for sensitive workloads

Website: https://openrouter.ai

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Canonical: https://cutmyaispend.com/tools/openrouter

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# Helicone — LLM observability & cost tracking

Pricing: Free tier; usage-based

## Verdict

Long the default open-source LLM cost dashboard. Acquired by Mintlify in March 2026 and now maintenance-only — existing deployments keep working (security fixes and new-model pricing continue), but no new features. Fine to keep; think twice before adopting fresh.

## Strengths

- Dead-simple one-line proxy integration for cost logging
- Solid per-request cost dashboards and caching stats

## Limitations

- Maintenance-only since the Mintlify acquisition — no new feature work
- New adopters should compare gateway-native analytics (LiteLLM, Portkey) first

Website: https://helicone.ai

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Canonical: https://cutmyaispend.com/tools/helicone

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# nOps — Cloud & AI FinOps platform

Pricing: Percentage-of-savings / platform fee

## Verdict

Cloud-FinOps platform that extended into AI spend (Bedrock, Azure OpenAI, GPU fleets). The fit when your AI bill is one slice of a larger AWS/Azure bill and finance wants one pane of glass.

## Strengths

- Unifies AI spend with the rest of cloud cost management
- Strong on committed-use planning and GPU utilization

## Limitations

- Heavier than what an API-only startup needs — gateway analytics may be enough
- Token-level, per-feature attribution still needs tagging discipline on your side

Website: https://www.nops.io

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Canonical: https://cutmyaispend.com/tools/nops

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# AI overspend statistics 2026

Last updated 2026-08-06. Every number sourced — cite with a link back to https://cutmyaispend.com/stats.

- **79%** of enterprises overspent on AI in 2026. Sapio Research survey (Feb 2026, commissioned by DoiT) of 500 finance leaders at 1,000+ employee organizations across the US and UK. (Source: DoiT / Sapio Research — https://www.doit.com/blog/ai-spending-survey)
- **73%** of enterprise agentic-AI implementations went over budget. Review of 127 enterprise agentic AI implementations; some exceeded original estimates by more than 2.4×, burning ~$2.3M in unanticipated costs. (Source: BERI AI FinOps analysis, 2026 — https://www.beri.net/article/ai-finops-2026-73-percent-blow-budget-cfo-fix)
- **89%** of organizations that call their FinOps "very mature" still had AI cost overruns. Mean overspend in this segment reached 30.9% — the highest of any group studied. Mature orgs run bigger AI programs and actually detect their overruns. (Source: DoiT / Sapio Research, 2026 — https://www.doit.com/blog/ai-spending-survey)
- **98%** of FinOps teams now manage AI spend — up from 31% two years ago. AI workloads have grown to ~18% of cloud budgets at AI-forward companies, up from 4% in 2023. (Source: FinOps X 2026 — https://www.usage.ai/blogs/finops/ai-ml-cost/finops-x-2026-takeaways)
- **Up to 90%** off cached input tokens with provider prompt caching. Anthropic prompt caching discounts cached tokens up to 90%; OpenAI applies ~50% automatically. Production cache-hit rates cluster at 50–80%. (Source: Provider pricing docs; production reports — https://www.digitalapplied.com/blog/prompt-caching-2026-cut-llm-costs-engineering-guide)
- **Up to 98%** cost reduction demonstrated by LLM cascade routing at matched quality. FrugalGPT-style cascades (Stanford) send queries to cheap models first and escalate only when needed. (Source: Stanford FrugalGPT research — https://neuraltrust.ai/blog/llm-cost-reduction-guide)
- **3–8×** output tokens cost more than input tokens (median ~4:1). Long responses are billed at the premium rate — output-length control is one of the cheapest savings available. (Source: Cross-provider pricing analysis, 2026 — https://neuraltrust.ai/blog/llm-cost-reduction-guide)
- **30–70%** of redundant API calls eliminated by semantic caching. Matching paraphrased queries against previously answered ones skips the model call entirely in FAQ-heavy workloads. (Source: NeuralTrust LLM cost reduction guide, 2026 — https://neuraltrust.ai/blog/llm-cost-reduction-guide)
- **50%** flat discount from batch APIs at every major provider. OpenAI, Anthropic, and Google all price async batch endpoints at ~half the synchronous rate. (Source: Provider pricing docs, 2026 — https://www.getmaxim.ai/articles/reduce-llm-cost-and-latency-a-comprehensive-guide-for-2026/)
- **~10×** cheaper per agent task with a persistent context/memory layer. Mitosis Labs reports ~1/10th cost and 98% fewer hallucinations when agents query an indexed memory graph instead of re-ingesting raw data each run. (Source: Mitosis Labs — https://mitosislabs.ai)

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