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Audon

How much could your AI bill shrink?

Answer four questions for a rough monthly estimate. No sign-up, nothing to connect.

  • Based on published provider pricing
  • Only savings that keep quality count
  • Exact number from a free audit
Which providers do you use?
What do your agents mostly do?

Pick all that apply

Already doing any of these?
Prompt caching
Batch API for work that can wait
Cheaper models for simple requests

You could save about

$1.6k–$3.1k /month

16–31% of your spend · $19k–$38k a year

  • Prompt caching$860–$1.7k
  • Model routing$360–$960
  • Output length$300–$450
  • Batch processing$150–$400

Levers overlap, so the total is combined without counting a dollar twice and doesn’t equal their sum. A rough estimate from typical workloads, not a measurement.

Get my exact number: free audit

Read access only · about 2 minutes to connect · no card

We use your email to send this estimate and one follow-up. See our privacy policy.

Works with all common agent stacks

  • Agents SDK (OpenAI Agents SDK)
  • Agent SDK (Claude Agent SDK)
  • LangChain
  • CrewAI (CrewAI)
  • Microsoft AutoGen
  • AI SDK (Vercel AI SDK)
  • Dify

From estimate to money actually saved. The audit replaces typical numbers with yours, then proves each change.

  1. Step 12 minutes

    Connect

    Add an admin key with read access to OpenAI, Anthropic or xAI. Nothing to install, nothing in your inference path.

  2. Step 2Same day

    Audit

    We import 30 days of billing and size every lever on your real token mix: caching, batch, routing, output length.

  3. Step 31–2 weeks

    Prove

    Approve a change, run it on a slice of production traffic, and count the saving once it’s measured.

Four levers, sized on your data. Each one uses your provider’s published rates and states its assumptions.

  • Potential, 30 days

    $806.00
    25%

    of uncached input moved to cache

    Prompt caching25% of uncached input moved to cache × (1 − effective cached cost), with each cached prefix reused at least four times.

    Uncached input per model, from token-type line items. Cache reads cost 0.1× input on Anthropic, at most half on OpenAI, 75–85% less on Grok 4.x.

  • Potential, 30 days

    $1,073.32
    20%

    of spend that can wait 24 h

    Batch processing20% of the project’s spend can wait up to 24 h × the provider’s batch discount.

    Standard-tier spend that could wait 24 h. Batch costs 50% less on OpenAI and Anthropic, and 20% less on Grok 4.x.

  • Potential, 30 days

    $1,384.71
    20%

    of premium-model tokens routed

    Model routing20% of premium-model tokens routed to the cheaper model × (1 − cheap rate ÷ premium rate).

    Your blended cost per token for each model, so no price list is needed. Per-token rates differ 10–20× between frontier and small models.

  • Potential, 30 days

    $639.15
    15%

    fewer output tokens

    Output length15% fewer output tokens (max tokens, structured output, terser formats) × output cost.

    The output share of each model’s cost, once it reaches 60%. On most models, output tokens cost 4–5× input.

  • Potential, 30 days

    $212.40

    41 verified failed runs, no assumption

    Retries and failed runsEstimated cost of verified failed runs.

    Failed runs and retries from the outcome telemetry you send. No provider rate involved: it is what failed runs already spent.

  • How we size a lever

    Versioned rules on your own cost data, with one stated assumption you can change. Levers can overlap, so they are never added up.

    Read the method

Amounts are a synthetic example. Provider facts checked 26 Oct 2026. A lever writes a potential saving; only an approved experiment makes it verified.

Built to pass your security review. Least data by design, every control documented, nothing claimed that isn’t true.

  • Read access only

    We call cost and usage endpoints. Nothing sits in your inference path.

  • Keys envelope-encrypted

    AES-256-GCM with a separate master key. Never shown again, destroyed on disconnect.

  • No prompts, no model training

    We never read prompts or outputs, never send your data to an AI model, never train on it.

  • Tenant isolation, tested

    Every record is scoped to one organization. Automated tests cover web, REST and MCP.

  • Tamper-evident audit log

    Every sensitive action, by a person or an agent, in a hash-chained log you can verify.

  • Human approval

    Roles, scoped agent access, and four-eyes approval before any change counts.

Questions before you trust a number.

How accurate is this estimate?
It’s a range from typical workloads and published provider pricing, not a measurement. Real results depend on your token mix, how often prompts repeat and how much work can wait. The free audit sizes each lever on your own billing data.
Do the savings come at the cost of quality?
No saving counts until it’s been tested. Every change runs as an approved experiment on a slice of production traffic, and it only counts if success rate and latency hold.
What do you need to give the exact number?
An admin key with read access to your OpenAI or Anthropic organization, or a management key for xAI. We read billing and usage reports only, never prompts or outputs.
Why don’t the levers add up to the total?
They overlap: caching and batching can act on the same requests. The total combines them without counting any dollar twice, and it’s capped at half your spend.
What does it cost?
The audit is free for 30 days, no card. After that, see pricing.

Same results. See your real number in a day.

Free 30-day audit. Read access only, no card, revoke any time.