Why is personalization context so expensive?

Profile and history tokens are injected as input on every request, for every user. A 1,500-token context across millions of monthly requests becomes billions of input tokens — a cost that scales with both users and their activity.

How can I reduce personalization cost?

Summarize history instead of injecting it raw, retrieve only the most relevant snippets per request rather than the full profile, and cache the stable portion of each user's context with prompt caching.

Does this include output cost?

No. This tool isolates the input cost of the injected personalization context. Add per-request output cost separately with a full API cost calculator.

Is my data sent anywhere?

No. The calculator runs entirely in your browser. Nothing you enter is uploaded, stored, or logged.

What is a realistic personalization context size?

It varies widely — a few hundred tokens for basic preferences, a few thousand when injecting recent conversation history or retrieved documents. Measure your real prompts rather than guessing; this is often larger than teams expect.

Personalization Context Token Cost Calculator

What does personalizing every LLM call cost?

Injecting a user’s profile, preferences, and past interactions into each prompt makes responses feel tailored — and adds input tokens to every request, for every user. Because the cost multiplies across your whole active base and their daily activity, personalization can quietly become a major line item. This tool sizes it precisely, in total and per user.

How it works

Each request carries the user’s profile plus injected history as input. The monthly cost is:

monthly_cost = (profile_tokens + history_tokens)
             × requests_per_user_per_day × 30
             × active_users
             / 1,000,000
             × input_price_per_million

Dividing by active users gives the per-user cost of personalization, which is the number to weigh against the engagement lift it buys.

Tips to keep personalization affordable

Summarize, don’t dump. Replace raw history with a short rolling summary that captures intent in a fraction of the tokens.
Retrieve, don’t inject everything. Pull only the most relevant profile fields or memories per request instead of the full record.
Cache the stable prefix. A user’s core profile changes rarely and is an ideal prompt-cache candidate, cutting the repeated input cost sharply.