billing
In front Smaller request.

The suite just ran.

8,410 Tokens 6,067

Pay less for
the same AI

You pay for every letter. Tokand lowers the bill. Same results.

Add it the way you already ship

How the bill gets smaller
Less junkYou send less,
so you pay less
Same resultsThe job still
gets done
Easy addA few lines
in your code
Safe missIf we’re down,
you skip us

How the bill gets smaller

01

You send a big pile of text

Every chat and every agent step ships a pile of words to the AI company. They charge by how big the pile is.

02

Tokand pulls out the junk

Repeats, clutter, stuff the AI already saw — gone before the meter runs. The useful parts stay.

03

Same result. Smaller charge.

Your users still get the result. You still use the same AI. The bill just doesn’t include as much trash.

Add it in a few lines

T billing / install / client.ts
import OpenAI from "openai"

const client = new OpenAI({
  baseURL: "https://api.tokand.com/v1",
  apiKey: process.env.TOKAND_KEY,
})

// unchanged call site
await client.chat.completions.create({
  model: "gpt-4.1",
  messages,
})

Agents make the pile huge

Each tool step adds more text. You get charged again for stuff that is mostly noise.

You shouldn’t rebuild

Keep the same AI company, the same prompts, the same product. Tokand sits in front of the call.

Your app still runs

If we’re slow or down, your code skips us and talks to the AI directly.

If we’re down, you skip us

T billing / install / guard.ts
const res = await tokand(
  { messages, tools },
  (req) => provider.complete(req),
  { apiKey: process.env.TOKAND_KEY, timeoutMs: 80 }
)

// if Tokand is down → sender gets the original request

If Tokand takes too long or errors, that one call skips us and your code sends the original request. Savings when we’re up. Normal service when we’re not.

Skip on errorOn by default
Short waitThen go around us

Same AI.
Lower bill.

Add Tokand in front of the calls you already make. Pay for the useful words, not the pile of leftovers.

Looking for ways to save on AI?

Tokand is for teams searching how to cut AI costs, shave tokens, reduce LLM bills, and control agent spend—without swapping models.

Save tokens on OpenAI API calls Reduce Anthropic / Claude API cost Shave input tokens before billing Lower Claude Code token usage Cut Codex / GPT agent spend Reduce Cursor agent AI cost OpenClaw harness model cost control Hermes agent LLM cost savings Fail-open AI proxy (no outage risk) Drop-in baseURL for LLM cost optimization Stop paying for junk context / tool dumps Agent and chatbot token bloat What Tokand does, in public Machine-readable summary for AI agents
How do I cut AI costs without changing models?

Add Tokand in front of your LLM or agent calls. You keep the same provider and prompts; you send less junk text, so you pay for fewer tokens.

How do I shave or save tokens on OpenAI and Anthropic?

Point your OpenAI-compatible client at https://api.tokand.com/v1. See the quickstart.

Can Tokand reduce agent tool-call token bloat?

Yes. Long tool traces are where bills explode. Tokand sits on that path so you can lower spend without rewriting the agent.

What if Tokand is down?

Fail-open by default: on timeout or error, traffic skips Tokand and hits your model provider directly.

Which stacks does Tokand support?

Claude, ChatGPT, Claude Code, Codex, Cursor, OpenClaw, Hermes, and anything that accepts a base URL. Guides: connect your AI agent.