API Cost Calculator

API costs can silently become one of the largest line items in a startup's infrastructure budget. A product that uses three different APIs — an LLM for responses, Twilio for SMS, and Stripe for payments — can easily rack up $5,000–50,000/month at scale without anyone noticing until the billing cycle closes. This API cost calculator makes those costs visible and comparable before you build.

Step 1: Set Your Request Volume

Enter your expected daily request count — this is the number of API calls your application makes per day across all users. For LLM APIs, also set your average input and output token counts. A typical chatbot query might use 500 input tokens (system prompt + conversation history) and return 300 output tokens. A document analysis task might use 4,000 input tokens and 500 output tokens. These numbers dramatically affect LLM API costs since input and output tokens are billed at different rates.

Step 2: Filter by Category

Use the category tabs to filter the API list. The AI / LLM tab shows all major language model providers — OpenAI, Anthropic, and Google — across their different model tiers. The Payments tab shows Stripe's transaction-based pricing. Communication shows Twilio (SMS) and SendGrid (email). Cloud / Infra shows AWS Lambda compute costs. Maps / Geo shows Google Maps API tiers.

Step 3: Select APIs to Compare

Check the boxes next to the APIs you want to include in your budget. The comparison chart at the bottom updates in real time to show relative costs. For LLM APIs, notice how dramatically costs differ between tiers: at 1,000 daily requests with 1,000 tokens each, GPT-4o costs roughly 10× more than GPT-3.5 Turbo and 3× more than Claude Haiku. Choosing the right model tier for each use case is the single biggest lever for LLM cost control.

Step 4: Add Custom APIs

Use the Custom API section to add any API not in the default list. Enter the name and cost per 1,000 requests, and it will be added to your comparison. This is useful for niche APIs, internal services with known costs, or providers whose pricing structure is per-request rather than per-token.

Understanding LLM Token Pricing

LLM APIs charge per token rather than per request, making them unique in API pricing. One token is roughly 4 characters of text. Input tokens (what you send to the model) and output tokens (what the model generates back) are priced differently — output tokens typically cost 3–5× more than input tokens. This means verbose model responses cost significantly more than concise ones. Prompt engineering to reduce output verbosity can meaningfully reduce costs at scale.

Strategies to Reduce API Costs

For LLM APIs: use the smallest model that meets your quality bar, implement prompt caching for repeated context (Anthropic and OpenAI both offer discounts), and batch requests when possible. For REST APIs: implement aggressive client-side caching for static or slowly-changing data, paginate efficiently to minimize calls, and use webhooks instead of polling. For maps and communication APIs: cache geocoding results aggressively, use SMS only for time-sensitive notifications, and consider email fallbacks for non-urgent messages.