--- title: "Agent Spend Optimization: cut AI agent and LLM costs · Audon" description: "Cut AI agent and LLM costs from read-only billing data: detectors, savings levers sized on your spend, fixes for your coding agent and measured savings." url: https://audon.ai/product/agent-spend-optimization last_updated: 2026-11-02 --- What your agents cost # Agent Spend Optimization. Smaller AI bills, same results. Find what you can cut from your AI bill, get the exact fix for each saving, and count it only once it’s measured. [Start auditFree](https://audon.ai/sign-up) [See a sample report](https://audon.ai/sample-report) ## What is Agent Spend Optimization? Agent Spend Optimization is Audon’s product for cutting what AI agents and coding tools cost. It reads OpenAI, Anthropic, xAI, Cursor and Devin billing with read-only keys, finds savings such as prompt caching, batch processing, model routing and unused seats, and counts a saving only after it is measured. Formerly Agent Loss Prevention. ## Six steps, from a key to a measured saving. 1. 1 Connect A read-only key per provider, encrypted. No SDK, no proxy. 2. 2 Reconcile Daily cost records; recent days stay provisional. 3. 3 Detect Versioned detectors compare each project and model with its history. 4. 4 Explain Every finding carries its evidence, baseline and confidence. 5. 5 Monitor Scheduled re-scans, with alerts by email, webhook or agent. 6. 6 Measure Live changes are measured from billing; an experiment verifies them. ## Detectors in this release. Deterministic, unit-tested and versioned. Every finding records the detector version and the snapshot it ran on, so a result can be reproduced. No detector can write a verified saving. Findings Detector registry | Detector | What it finds | Writes | Needs | | --- | --- | --- | --- | | spend_spike 1.0.0 | Days where spend on a project/workspace/model exceeds 2.5× its trailing 14-day median (minimum 7 days of history). | Anomalous spend | Cost data, 7+ days of history | | spend_concentration 1.0.0 | A single project/workspace/model accounts for at least 60% of spend in the period. | Observed spend | Cost data | | budget_burn 1.0.0 | Month-to-date spend run-rate projects above the organization’s configured monthly budget. | Observed spend | Cost data and a monthly budget | | sustained_growth 1.0.0 | Week-over-week spend growth above 20% for three consecutive complete weeks on one dimension. | Observed spend | Cost data, 4+ weeks | | outcome_data_gap 1.0.0 | Material spend with no outcome telemetry — an analysis gap, never presented as waste. | Observed spend | Cost data | ## How we size a lever. On your own cost data, with every assumption stated. All sixteen savings levers are detectors too: deterministic, versioned and reproducible. Each one reads a signal in your data, applies the provider facts it records and one explicit assumption, and writes a potential saving. Nothing more. 1. Your data, not a price list Signals come from your cost line items: token types, service tier, observed cost per token per model. 2. One stated assumption The share of eligible traffic is never claimed as known. It is an explicit, editable assumption on the lever. 3. Facts with a source Each lever records the provider facts it used, with the source and the date they were checked. 4. Sized in order, so they add up Levers on the same spend are sized in a fixed order, each on what the earlier ones left. ### Model providers10 Changes to how your code calls a provider’s API, or to a setting on the provider account. - #### Service tier Pay the fast or priority surcharge only where someone waits; run work nobody waits on at flex or batch rates. - #### Regional processing Drop the regional or US-only processing surcharge where no contract requires it. - #### Model choice Move to a newer, cheaper version of the same model, or route simpler requests to a cheaper model. - #### Tests and evals Run tests, evals and CI on a cheaper model, a smaller sample or a nightly schedule. - #### Context size Stop resending growing history and stay under long-context price thresholds. - #### Output and reasoning Cap outputs and set reasoning effort per task, so you pay for the answer, not the padding. - #### Embeddings Embed each document once (skip unchanged content) and run bulk embedding at batch rates. - #### Retries and failed runs Stop paying for runs that fail or retry without producing a result. - #### Prompt caching Serve repeated prompt prefixes from cache, and stop paying for cache writes that are never read. - #### Storage and hosted tools Delete stored files, vector stores and caches nobody uses, and cap paid tool calls. - See which ones apply to you. The free audit sizes every method on your last 30 days and shows only the ones that apply, each with its fix. [Start auditFree](https://audon.ai/sign-up?intent=spend) ### Coding tools and seats, contracts and commitments6 Plans, seats and defaults an admin changes in a tool’s dashboard. Contracts and commitments are sized on the numbers you enter under Settings → Contracts. - Coding tools and seats #### Right plan per person Give each person the cheapest plan that covers how they work: remove, downgrade, upgrade or move to a seat. - Coding tools and seats #### Coding model mix Default coding agents to a cheaper model or the tool’s own model pool; keep the premium model for hard tasks. - Coding tools and seats #### Cursor Max mode Keep Max mode for large, multi-file work; normal mode costs less per token for the rest. - Coding tools and seats #### Overlapping tools People paying for two tools that do the same job: keep the one they use. - Contracts and commitments #### Cloud commitments and credits Pay for the same models through AWS, Google Cloud or Azure where a commitment or credits would otherwise go unused. - Contracts and commitments #### Reserved capacity Size provisioned throughput to what you use, or move low-use capacity back to pay-as-you-go. ## From finding to fix. Apply it in one click. Each lever comes with the exact change for your provider and model. One click hands it to your coding agent as a GitHub issue or a webhook. The saving is still measured before it counts. - The fix, on every finding Steps, code and rollback for your provider and model. - Apply fix One click sends it to your coding agent as a GitHub issue or a webhook. - Followed to live The pull request and the merge show up on the finding. Then the saving is measured. Findings Prompt caching on claude-sonnet-5-5 Synthetic example The fix · Anthropic · claude-sonnet-5-5 Cache the static prompt prefix 1. 1.Move instructions, tools and examples to the front 2. 2.Mark them as cacheable 3. 3.Ship behind a flag and watch the cached share ``` system: [{ type: 'text', text: STATIC_INSTRUCTIONS, cache_control: { type: 'ephemeral' }, }], ``` Sends a GitHub issue to northwind/support-agent 1. Issue #214 opened 2. Pull request #215 open 3. Live on merge Alerts All rules Synthetic example Rule Status 14 days Daily spend above $3,000 daily_spend > $3,000.00 Triggered 16 Oct Monthly budget 80% reached budget_burn ≥ 80% Armed Provider sync stale for 12 h sync_staleness > 12 h Armed Cost per success above $0.13 cost_per_success > $0.13 Armed ## Monitoring and governance by default. Hard limits stay with your provider. Provider billing APIs report with a delay and the base product isn’t in your request path, so it doesn’t stop spend as it happens. It catches it the day it shows up and governs what you change. **Monitoring** Daily re-scans and alerts by email or webhook. **Governance** Rollback plans, a second approver, an audit trail. **Hard caps** Your provider’s limits, set where Audon suggests. **Cost per team** Spend per team, with a monthly budget for each. **Renewals and prices** Renewal reminders and announced price changes. **New keys** New or unnamed API keys wait for review. Developers POST /v1/telemetry/events ``` { "events": [{ "run_id": "run_8f2c", "workflow_id": "support-triage", "started_at": "2026-10-21T09:14:02Z", "result_status": "succeeded", "result_verification": "system_verified", "estimated_provider_cost_minor": 42, "currency": "USD" }] } ``` ## Cost per successful task. The number billing data can’t give you. Send outcome events (run id, workflow, status, how the outcome was verified, estimated cost). Audon computes cost per verified success and finds spend on failed runs. Fields that look like prompts or personal data are rejected. [Telemetry API](https://audon.ai/docs/api) [MCP setup](https://audon.ai/docs/mcp) ## Methodology and the words we use. [All terms in the glossary](https://audon.ai/glossary) **Observed spend** Invoice-aligned, provider-reported cost for the selected UTC days. Sourced from the OpenAI organization Costs API and the Anthropic Cost Report API. Recent days are *provisional* and are re-reconciled on every sync. **Anomalous spend** Observed spend with features that indicate a possible problem (e.g. a day more than 2.5× its trailing 14-day median). It is *not* a saving: it may be legitimate demand. **Potential saving** A modelled reduction if a proposed change works, under stated assumptions. **Validated opportunity** A scoped, feasible change with supporting evidence and a computable expected benefit, confirmed by a person. **Verified saving** A measured reduction in cost per verified successful task in the latest recorded experiment on production traffic, approved and verified by people other than those who proposed it and recorded the results, with no success-rate drop greater than 2 percentage points, no p95 latency increase greater than 20%, and at least 30 verified successes per window: ``` measured saving = (cost per success before − cost per success after) × successful tasks after ``` **Cost per verified successful task** Cost per verified successful task = Σ attributable run costs ÷ count of verified successful runs. When the denominator is zero, or cost is missing, the value is *unavailable* — never 0. **Cash impact** Lower usage reduces future usage-based invoices. Under prepaid commitments it frees capacity rather than reducing near-term cash. **Monitoring vs blocking** Alerts evaluate complete UTC days of provider billing data, which can lag by hours. They are monitoring, not real-time spend blocking. **Money** Amounts are stored as exact integer micros (10⁻⁶ of the billed currency). Currencies are never summed together. ## Integrations. Each with what we read and what we never read. - [OpenAI Costs and usage by project, model and line item, read with an organization Admin key. What we read](https://audon.ai/integrations/openai) - [Anthropic (Claude API) Cost and usage reports by workspace and model, plus Claude Code per person, with one Admin key. What we read](https://audon.ai/integrations/anthropic) - [Claude EnterpriseNew Per-person activity, idle seats, and daily cost and tokens by product and model. What we read](https://audon.ai/integrations/claude-enterprise) - [Claude CodeNew Tokens and cost per person and model, from the Admin report or Claude Code’s own telemetry. What we read](https://audon.ai/integrations/claude-code) - [CodexNew Tokens per day and model from Codex’s own OpenTelemetry export, with no key stored. What we read](https://audon.ai/integrations/codex) - [xAINew Daily team spend by model and endpoint, read with a team-scoped management key. What we read](https://audon.ai/integrations/xai) - [CursorNew Members, usage per person and model and on-demand spend, or the usage export on individual plans. What we read](https://audon.ai/integrations/cursor) - [DevinNew Daily ACUs by product and per person, priced with your own price per ACU. What we read](https://audon.ai/integrations/devin) ## Questions teams ask before connecting. **What is Agent Spend Optimization?** Agent Spend Optimization is Audon’s product for cutting what AI agents and coding tools cost. It reads OpenAI, Anthropic, xAI, Cursor and Devin billing with read-only keys, finds savings such as prompt caching, batch processing, model routing and unused seats, and counts a saving only after it is measured. **Is this the same as Agent Loss Prevention?** Yes. Agent Loss Prevention was renamed Agent Spend Optimization in November 2026. The product, your data, your plan and the API are the same, and old links lead here. **Which providers and tools does it read?** OpenAI, Anthropic (Claude Platform), Claude Enterprise, xAI, Cursor, Devin (Enterprise), Claude Code (personal plans), Codex (ChatGPT plans). Plans with no billing API, such as Claude Pro or Max, ChatGPT, GitHub Copilot or Gemini, are added under Settings → Subscriptions. See [integrations](https://audon.ai/integrations). **Does it change our code, keys or AI traffic?** No. Keys are used read-only to read cost and usage reports, encrypted with AES-256-GCM and never shown again. Audon is not in your request path. Every finding comes with the exact fix; with the Auto-apply add-on, Audon hands it to your own coding agent and your team reviews and merges the change. **When does a potential saving count as saved?** When your team marks a change as done, Audon measures it from billing, as cost per unit of work before and after, so volume changes don’t count. It becomes a verified saving only after an approved experiment shows lower cost per successful task with at least 30 successes in each window. **What does it cost?** The 30-day audit is free, with no card. Starter is USD 109 a month (USD 89 billed yearly) and adds daily monitoring and verified savings. See [pricing](https://audon.ai/pricing). Last updated 2 November 2026 ## Smaller AI bills. Same results, proven. [Start auditFree](https://audon.ai/sign-up) [See a sample report](https://audon.ai/sample-report)