LLM API Cost Calculator: How Many Requests Does Your Budget Buy?

Most LLM API pricing pages show you the cost of one million tokens. That number means nothing at 9 AM when you're deciding whether to ship a feature. What you actually need to know is: if I give this API $200/month, how many user interactions can it support?

The answer depends on your use case far more than most developers realize. A RAG pipeline that sends 3,000 tokens of retrieved context per query but only generates 300 tokens of output costs roughly 30% of what a creative writing tool costs per request — even though both consume similar total tokens. The difference is entirely in which end of the pipeline generates the tokens. Output generation is 4-8x more expensive per token across every major provider.

The output-to-input ratio trap catches most teams once they move from prototyping to production. During development you might test with balanced prompts. In production, your chatbot's system prompt alone consumes 800 tokens per request before the user says anything. If you cache that system prompt with Anthropic's prompt caching (more on this below), you pay 10% of normal input rates on cache hits — which fundamentally changes your budget math.

Case study: routing by complexity, not by default

A realistic hybrid architecture for a solo-founder SaaS: send 90% of requests to DeepSeek V4 Pro for speed and cost, reserve Claude Sonnet 4.6 for the 10% of requests that need structured reasoning, long-form output, or multi-step tool use. At $100/month total budget and 50,000 requests/month:

• 45,000 requests to DeepSeek V4 Pro (chatbot): ~$6.30/month
• 5,000 requests to Claude Sonnet 4.6 (complex): ~$81.25/month
• Total: ~$87.55/month — well under budget with headroom for spikes

The same 50,000 requests routed entirely to GPT-4o would cost ~$162.50/month. The routing layer costs almost nothing — one cheap model call to classify the query as “simple” or “complex” — and the savings compound with volume.

Practical budget breakdown formula

Start with your user count and session behavior: daily active users × average sessions/day × average API calls/session = daily requests. Multiply by 30 for monthly. Apply your expected token profile from the use-case presets above. Add 20% headroom for spikes and re-runs. That's your minimum production budget. Divide it by your target model's cost-per-request to validate whether the math works before you write a line of code.

The pricing numbers shown in the calculator above are standard real-time API rates. In practice, two levers can dramatically extend your effective budget without changing which model you use.

Anthropic prompt caching is available on all Claude models. You mark a portion of your prompt as cacheable using a cache_control block. The first time that exact content is processed, you pay a cache write premium — roughly 25% above the normal input token rate. Every subsequent request within the cache TTL (5 minutes by default, extendable) is charged at the cache read rate: approximately 10% of the normal input price. That's a 90% discount on the cached portion.

The practical impact: a 2,000-token system prompt sent to Claude Sonnet 4.6 100,000 times per month costs $600 at standard rates. With prompt caching (one cache write + 99,999 cache reads), the same workload costs approximately $66 — a 89% reduction. The break-even point is roughly any system prompt longer than 1,000 tokens used more than twice per session.

Batch API (OpenAI and Anthropic) gives you a flat 50% discount across all tokens in exchange for asynchronous processing with a 24-hour turnaround SLA. OpenAI's Batch API and Anthropic's Message Batches both offer this. At scale, a $500/month real-time budget becomes $1,000/month of effective compute if your use case tolerates latency: offline document processing, nightly data enrichment, bulk evaluation runs, weekly report generation.

Calculating your effective budget with caching: Take your monthly request count and estimate what percentage hit the cache. If 80% of requests reuse the same system prompt, your effective input cost drops to roughly (20% × standard rate) + (80% × 10% of standard rate) = 28% of the uncached rate. Apply this multiplier to the calculator outputs above to get a more realistic production estimate for Claude-based workloads.