Token Budgeting for Predictable Bills

An LLM bill is just tokens multiplied by a rate. If you can predict your token usage, you can predict your spend, and a predictable bill is what turns an experimental feature into a business someone is willing to fund.

Token budgeting is the discipline of deciding, up front, how many tokens each request is allowed to consume. Here is how to do it on Model Database.

Know the four token buckets

Every chat request spends tokens in predictable places:

• System prompt: your instructions and examples, paid on every call.
• Conversation history: prior turns you replay back to the model.
• User input: the current message or retrieved context.
• Output: the model's generated response, usually the priciest per token.

Budgeting means putting a number on each bucket and enforcing it, instead of letting them grow unbounded.

Set a hard output ceiling

The single most effective control is max_tokens. It caps the most expensive bucket and bounds your worst case. Choose a value from real outputs, not a round number you guessed.

{
  "model": "openai/gpt-4o-mini",
  "max_tokens": 400,
  "messages": [
    {"role": "system", "content": "Answer in under 150 words."},
    {"role": "user", "content": "..."}
  ]
}

Pair the limit with an instruction asking for brevity. The two together keep responses tight.

Estimate before you ship

You can model a feature's cost on paper. A rough rule is that one token is about four characters of English, so a 600-word answer is roughly 800 tokens. Build a small spreadsheet:

• Input tokens per call: system + history + user.
• Output tokens per call: capped by max_tokens.
• Calls per day.

Multiply through and you have a daily token volume. Suppose each call is 1,200 input and 300 output tokens, and you serve 20,000 calls a day. That is 24 million input and 6 million output tokens daily, a figure you can sanity-check against the model rates before launch.

Cap conversation history

Chat features quietly get more expensive as conversations grow, because you replay the whole transcript each turn. Decide on a window, for example the last 8 messages or a 2,000-token limit, and summarize or drop everything older.

def trim_history(messages, max_tokens=2000):
    kept, total = [], 0
    for m in reversed(messages):
        t = len(m["content"]) // 4
        if total + t > max_tokens:
            break
        kept.append(m); total += t
    return list(reversed(kept))

Reconcile against the charge headers

Your estimate is a model; the headers are ground truth. Model Database returns the real cost and balance on every response, so you can compare your budget against reality and catch drift early.

X-MDB-Charged-USD: 0.0021
X-MDB-Balance-USD: 184.55

Log X-MDB-Charged-USD per endpoint, then sum it daily. If a request type costs more than your budget said it would, your prompt or history window has grown and it is time to trim.

Use the cost cap as a safety net

Budgets are about the average case; the per-request cost cap on Model Database handles the worst case. Even if a prompt unexpectedly balloons, the cap stops a single request from running away with your balance. Set it just above your largest legitimate request so anomalies get blocked rather than billed.

Make budgeting a habit

Treat token budgets like any other resource limit. Write the expected input and output sizes into your design doc, enforce max_tokens in code, trim history automatically, and watch the headers in production. Do that and your monthly bill becomes a number you forecast rather than a surprise you absorb.

See your live balance and per-request charges any time on your dashboard, and compare model rates on the pricing page.