AI Cost Estimator

What it does

Project your monthly LLM API bill before it arrives. Inputs: requests per day, average input tokens, average output tokens, and model choice (GPT-4o, GPT-4o-mini, Claude Sonnet 4, Claude Opus 4, Gemini 2.5 Pro, Gemini Flash, DeepSeek V3, Llama 3.3 via providers). Tool calculates monthly cost using current per-million-token pricing and flags hidden cost levers like output-token weight (output is 3-5x more expensive than input across all major vendors).

Real-world cost surprises are common: a chatbot with 10,000 queries/day at 2,000 input tokens + 500 output tokens runs ~$1,800/month on GPT-4o, ~$300/month on GPT-4o-mini, ~$45/month on DeepSeek V3, or ~$0/month on a self-hosted Llama 3.3 70B (after the GPU cost). Choosing the right model for the task is the single biggest cost lever — using GPT-4o for tasks that DeepSeek or Haiku could handle is the most common startup-stage cost mistake.

Cost-reduction strategies in priority order: (1) Use a smaller model where it works (test, don’t guess; benchmark on your actual workload). (2) Enable prompt caching (OpenAI, Anthropic both support; 50-90% off cached tokens; works best for long static system prompts). (3) Batch API (50% discount on async jobs; 24-hour turnaround; works for offline analysis). (4) Reduce output verbosity (max_tokens cap, system-prompt instruction for terse responses). (5) RAG-cache common queries (skip the LLM entirely for repeat questions). Combined, these can drop bills 70-90% without hurting quality.

How to use it

1. Set requests per day (e.g., 1000 for a moderately-busy chatbot, 50,000 for a high-traffic feature).
2. Set average input tokens — count both system prompt + user message + any RAG context (typical: 500-3000).
3. Set average output tokens (typical: 100-800; longer for code generation, shorter for classification).
4. Pick the target model. Tool shows both input/output cost lines and the monthly total.
5. Compare across models — toggle between GPT-4o, Sonnet, mini variants to find the cheapest model that meets your quality bar.
6. Factor in growth — if usage grows 30%/month, your bill 12 months out will be ~25x current; budget accordingly.

When to use this tool

• Pre-launch budget planning for an LLM-powered feature — knowing the cost ceiling helps decide pricing/free-tier limits.
• Architecture decisions — comparing API vs self-hosted (Llama 3.3, Mistral, DeepSeek) economics at your traffic level.
• Monthly cost reviews — projecting next-month bill from current daily traffic before getting surprised by the invoice.
• Vendor comparison shopping — running the same workload through OpenAI / Anthropic / Google / DeepSeek pricing.

When not to use it

• Hyper-bursty workloads where average requests/day misses peaks that consume monthly token quota.
• When you have negotiated enterprise pricing — public-rate-card calculators don't reflect your contract.
• Self-hosted deployments — different cost structure (GPU + electricity + ops), not API per-token.
• Image/video model pricing — those bill per-image or per-second, not per-token; use a different calculator.

Common use cases

• Pre-decision sanity-check on inputs and outputs
• Educational use — demonstrating the underlying concept
• Onboarding a colleague who needs the same calculation/conversion
• Verifying a number or output before passing it on