Vertex AI Gemini API Cost: Estimate Tokens Before Launch

Start with the product action, not the monthly user count. For each chat, summary, search, or AI assistant path, estimate average input tokens, expected output tokens, attached media, calls per session, sessions per user, and monthly active users.

Keep long prompts, conversation history, system instructions, and retrieved documents visible in the estimate. A small user base can still create meaningful spend when every request carries a large context window or produces long answers.

Repeated instructions, product documentation, examples, and policy text may be cheaper when they can be cached instead of resent as full-price input each time. Treat cached input as its own line item so the estimate shows whether prompt reuse is actually helping.

Grounding, search, Maps, and other tool calls can add charges outside the base model tokens. Estimate how often the assistant needs external facts, how many grounded queries a user action can trigger, and whether every flow truly needs grounding by default.

AI cost rarely appears alone. Add Cloud Run request time, queue workers, Firestore or Cloud SQL reads and writes, Storage uploads, logs, and retries to the same scenario. Failed retries and streaming reconnects can multiply model calls without improving conversion.

Before launch, set per-user limits, max output tokens, request timeouts, abuse detection, and billing alerts. Then compare a normal month with a spike scenario in the calculator so model usage, database traffic, and compute guardrails are reviewed together.