Treat a Free Token Tier as a Spec, Not a Giveaway

Description and use

Treat the free token tier as a hard budget: calculate per‑request limits and use caching to stay within the grant while reducing latency.

Problem

Teams often design AI workflows around chosen models, then face unexpected usage costs; a free token tier forces budgeting before implementation.

Outcome

By converting the free token grant into a per‑request budget and enforcing caching, teams can design AI workflows that stay within the free tier, reduce latency, and avoid cost overruns.

Workflow

• 1. Determine daily token grant and safety margin (e.g., 0.8).
• 2. Measure expected daily request volume, average prompt tokens, and average completion tokens.
• 3. Compute per‑request budget: usable_tokens = grant * safety / daily_requests.
• 4. Implement a cache‑first client with deterministic keys (hash of sorted JSON payload).
• 5. Set TTLs aligned with artifact change frequency (e.g., 1 h for CI comments).
• 6. Monitor cache hit rate; if below 30 % normalize prompts (strip timestamps, canonicalize whitespace).
• 7. Handle free server constraints with exponential backoff and jitter for retries.

Evidence

A free AI token tier is not a marketing giveaway; it is a specification that forces you to answer what a feature is worth per request before you ship.

Source

The cache does two jobs at once: it cuts token consumption and it cuts latency, because a cache hit is measured in milliseconds while a model call is measured in seconds.

Source