Rate-limit & retry strategies
- Batch where possible: combine 5–50 leads in one execution if prompt and token budgets allow. If per-lead structured fields are required, include them in a single multi-part JSON response from the model.
- Exponential backoff with capped retries: 1s, 2s, 4s, 8s; max 4–5 attempts. Log failures to a dead-letter queue for manual processing.
- Respect model throughput: add a token estimate per lead and limit batch size to stay under token/RPS quotas.
- Cache enrichments: store summary hash + timestamp; skip re-enriching unless profile changed or >X days old.

Cost-effective trigger strategies
- Trigger only on qualified leads (filters in HubSpot workflow): e.g., job title, company size, domain, lead score > X.
- Use scheduled batch enrichment (e.g., hourly/daily) instead of per-creation enrichment for non-urgent leads.
- Deduplicate: ignore leads missing email or with disposable domains.
- Use cheaper model variants for routine scoring; only use higher-capacity models for long-form summaries.

Decision criteria (pick based on)
- Volume: Zapier-only if low; serverless batching if medium/high.
- Latency: Zapier direct for real-time; batch for tolerable delays (minutes/hours).
- Budget: Zapier tasks add up; serverless + batching can be much cheaper at scale but needs engineering.
- Team: no-engineers -> Zapier; devs available -> serverless + observability.
- Output quality: higher-quality prompts/models cost more; balance model and token budget.

Practical checklist to implement
1) Define enrichment trigger criteria in HubSpot (MQL rules). 2) Create HubSpot workflow to POST to Zapier webhook or serverless endpoint. 3) In Zapier/serverless: validate & dedupe incoming lead. 4) Filter by qualification rules. 5) Batch or single-call to ChatGPT with a compact prompt that requests JSON: {summary, intent_score(0-100), confidence}. 6) Implement exponential backoff + DLQ logging. 7) Write results back to HubSpot properties. 8) Monitor success rate, costs, and token usage; adjust batch size and model.

Best-for / Avoid-if
- Best-for: teams needing quick MQL enrichment with minimal infra, or teams scaling who can afford to build a batching layer.
- Avoid-if: you need immediate per-lead human-grade long-form analysis at very high throughput without engineering support.

If you want, I can sketch a sample prompt for generating a compact JSON enrichment and a Zapier step layout for the direct flow.