Token Budget Calculator

Reverse-budgeting: the right way to plan LLM product costs

Most developers start by picking a model and calculating what it costs. But product teams need the inverse: given a monthly infrastructure budget and expected request volume, how much context can each request afford? This matters for RAG chunk counts, conversation history length, system prompt complexity, and tool-call result sizes.

Token ratios by scenario — what to expect

RAG Chat typically has a 70:30 input:output ratio — long context (system prompt + retrieved chunks) with a short answer. Code Assistant runs 60:40 — files and diffs in, a meaningful code response out. Document Analysis is 80:20 — most tokens are the document itself, the extraction is short. Simple Q&A is 55:45 — relatively balanced. AI Agent is 65:35 — multi-turn context with tool outputs accumulates in the input.

How token budgets work

A token budget is the maximum amount you can spend on LLM API calls within a given period, usually monthly. The core question is straightforward: given a fixed dollar amount, how many tokens can each request consume across different models? The formula works in two steps. First, calculate maximum monthly requests: budget divided by the cost per request, where cost per request equals (input_price multiplied by input_tokens plus output_price multiplied by output_tokens) divided by 1,000,000. Second, determine max tokens per request: total monthly budget divided by number of requests divided by the blended price per token. For example, with a $1,000 monthly budget using GPT-5 Mini at $0.25 per million input tokens and $2.00 per million output tokens, a RAG chat workload with 2,000 input and 500 output tokens per request costs about $0.0015 per request. That gives you roughly 666,000 requests per month. Working backwards, if you need 50,000 requests per month, each request can afford $0.02 worth of tokens — approximately 16,000 input tokens and 6,800 output tokens at a 70:30 ratio. The key insight is that token budgets force you to think about cost as a constraint on product design, not just an operational expense.

Setting token budgets by use case

Different applications have vastly different token economics, and setting the right budget requires understanding your specific workload pattern. For a customer-facing chatbot with a $500 per month budget using GPT-5 Mini at $0.25/$2.00 per million tokens, assuming 2,000 input tokens and 500 output tokens per request, each request costs about $0.0015. That supports approximately 66,000 requests per month — enough for a mid-sized SaaS product with a few thousand active users. For a RAG pipeline processing internal documents with a $2,000 per month budget on GPT-5 at $1.25/$10.00 per million tokens, assuming 5,000 input tokens (retrieved context) and 1,000 output tokens (synthesized answer), each request costs about $0.016. That allows roughly 26,000 queries per month, suitable for an enterprise knowledge base serving hundreds of employees. For an AI code assistant with a $1,000 per month budget on Claude Opus at $15.00/$75.00 per million tokens, assuming 3,000 input tokens (code context and instructions) and 2,000 output tokens (generated code), each request costs about $0.195. That supports approximately 5,100 requests per month — appropriate for a development team of 20 to 30 engineers making 10 to 15 AI-assisted edits per day. The takeaway is that use case determines model choice as much as quality does. A chatbot that needs high volume should favor cheaper models, while a code assistant that needs precision can justify premium pricing at lower volume.

Budget allocation across models

A production system rarely relies on a single model. The proven strategy is to allocate 60 percent of your budget to the primary model that handles complex or high-value requests, 30 percent to a fallback or cheaper model that handles routine tasks, and 10 percent as a buffer for traffic spikes and experimentation. For example, with a $1,000 per month total budget, allocate $600 to GPT-5 for complex reasoning and multi-step tasks, $300 to GPT-5 Mini for simple classification, routing, and short answers, and keep $100 in reserve for unexpected load or A/B testing new models. This tiered approach lets you route 80 percent of requests to the cheaper model while reserving the expensive model for cases where quality matters most. A simple complexity classifier (which itself runs on the cheap model) can score incoming requests and route them accordingly. The result is often 3 to 5 times more total throughput compared to running everything on a single premium model, with minimal quality degradation on the tasks that get routed down.

Token budget guardrails in production

Setting a budget is one thing — enforcing it in a live system is another. Production guardrails prevent runaway costs from bugs, traffic spikes, or adversarial users. The first layer is per-user daily limits. Cap each user at a fixed token allocation per day (for example, 100,000 tokens or $0.50 worth of API calls). Once exhausted, degrade gracefully by switching to a cheaper model or returning cached responses. The second layer is per-request max tokens using the max_tokens parameter. Always set this explicitly in your API calls — never leave it at the model default. For a chatbot, 1,024 output tokens is usually sufficient. For code generation, 4,096 may be appropriate. This prevents a single malformed prompt from generating a 100,000-token response. The third layer is circuit breakers. Monitor cumulative daily and weekly spend. When spend hits 80 percent of budget, trigger an alert. At 95 percent, automatically switch all traffic to your cheapest fallback model. At 100 percent, queue non-critical requests for the next billing period. The fourth layer is graceful degradation. Define a cascade: primary model at full context, then primary model at reduced context (truncate oldest messages), then fallback model at full context, then cached or templated responses. Each step down reduces cost while maintaining some level of service. Without these guardrails, a single viral moment or a prompt injection attack that triggers long outputs can burn through an entire monthly budget in hours.

Optimizing within a fixed budget

When your budget is set and non-negotiable, the goal shifts to extracting maximum value from every token dollar. Five strategies consistently deliver results. First, write shorter prompts. Every token in your system prompt is multiplied by every request. Cutting 500 tokens from a system prompt across 50,000 monthly requests saves 25 million input tokens — that is $6.25 per month on GPT-5 Mini or $31.25 on GPT-5. Audit your prompts quarterly and remove examples, redundant instructions, and verbose formatting. Second, enforce max_tokens limits on output. Models will generate until they hit the limit or finish naturally. Setting a tight max_tokens prevents rambling responses. For classification tasks, set it to 10. For summaries, 200 to 500 is usually enough. This alone can cut output costs by 30 to 50 percent compared to leaving it uncapped. Third, use streaming with early termination. If your application can detect that a response is sufficient before it finishes (for example, a code completion that already closed the bracket), cancel the stream. You only pay for tokens actually generated. Fourth, cache repeated queries. If 20 percent of your queries are duplicates or near-duplicates, a semantic cache (embedding similarity above 0.95 returns cached result) saves 20 percent of your budget with no quality loss. Provider-level prompt caching also helps — Anthropic and OpenAI both offer cached input token discounts of 50 to 90 percent for repeated prefixes. Fifth, implement model routing based on complexity. Use a lightweight classifier (or even simple heuristics like input length and keyword detection) to route easy questions to cheap models and hard questions to expensive ones. A well-tuned router sends 70 to 80 percent of traffic to the cheapest tier while maintaining overall quality scores within 5 percent of running everything on the premium model.

Budget planning for growth

Token budgets are not static — they need to scale with your product. The key metric to track is cost per active user per month. Calculate this by dividing your total LLM spend by monthly active users. Typical ranges for AI-powered SaaS products are $0.50 to $1.00 per user for light AI features like autocomplete and simple summaries, $1.00 to $3.00 per user for moderate usage like chat assistants and document Q&A, and $3.00 to $10.00 per user for heavy AI workloads like code generation and agent workflows. Once you know your per-user cost, forecasting is multiplication. At 10,000 users with $2.00 per user in LLM costs, you need a $20,000 monthly budget. At 50,000 users, that becomes $100,000 per month. Plan for peak-to-average ratios of 2 to 3 times. If your average daily spend is $600, your system should handle $1,800 days without hitting hard limits. Monday mornings and product launches reliably generate 2 to 3 times normal load. Build your budget model with three scenarios: base case (linear growth at current per-user cost), optimistic case (faster growth but cost optimization reduces per-user cost by 20 percent), and worst case (viral growth with no optimization, per-user cost increases 30 percent due to heavier usage patterns). Review quarterly. Model prices drop 20 to 40 percent per year on average, so your effective budget grows even if the dollar amount stays flat. But user expectations also grow — last year a 500-token response was fine, this year users expect 2,000-token detailed answers. The net effect is usually a 10 to 20 percent annual decrease in per-user cost if you actively optimize, or flat costs if you do not.