Short answer

- If you have little engineering bandwidth and modest volume: use Zapier (no-code) to collect messages and call an LLM for daily briefs.
- If you need scale, custom prompts, privacy controls or higher reliability: build a small ChatGPT-backed service (API) with batching, dedupe, retries and monitoring.

Recommendation

Start with Zapier to validate the summary format and cadence, then migrate to a ChatGPT-based service if you hit volume, cost, or compliance limits. This gives quick wins and a clear migration path.

Decision criteria (pick what matters most)

- Budget: Zapier = faster, predictable subscription; custom service = dev + hosting + API token costs (can be cheaper per summary at scale).
- Engineering skill: Zapier requires no devs; custom needs 1 backend engineer (or contractor).
- Scale (channels/messages/day): Zapier ok for low-to-medium traffic; custom handles high throughput and batching/queueing.
- Reliability & observability: Zapier is managed but limited logging; custom lets you implement retries, SLAs, alerts.
- Privacy/compliance: custom allows on-prem or restricted logging/PII redaction.
- Output quality/customization: custom wins—you can chain summaries, control prompts, and iterate prompts programmatically.

How they compare (practical points)

- Latency: Zapier flows can be slower and subject to task queueing. A custom service can summarize in parallel and return results faster.
- Cost at scale: Zapier charges per task/run; ChatGPT API charges per token—at high volume API + hosting tends to be cheaper per summary if optimized (batching, compressing history).
- Limits: Slack and Zapier rate limits matter. A custom service lets you implement throttling and backoff and keeps state to avoid duplicate work.
- Maintainability: Zapier easier to maintain initially. Custom requires ongoing dev ops but is more extensible.
- Security: Custom service lets you avoid third-party message storage or enforce retention rules.

Best-for / Avoid-if

- Best for Zapier: small teams, proof-of-concept, limited number of channels, no compliance constraints.
- Avoid Zapier if: you need tight control over prompts, high volume, advanced threading logic, or strict data residency.
- Best for ChatGPT API custom service: many channels, high message volume, need for custom pipeline (per-thread → channel summary → digest), and compliance requirements.
- Avoid custom if: you can’t afford engineering time or the volume is tiny.

Practical checklist to implement (step-by-step)

1. Define scope: channels, daily cadence, length (bullets vs paragraphs), exclusions (bot messages, files).
2. Prototype in Zapier: collect messages → filter/dedupe → send combined text to an LLM action → post summary to Slack. Validate format with stakeholders.
3. Measure volume & cost for 2–4 weeks (Zapier runs / tokens / timeouts). If stable and cheap, continue.
4. If moving to custom: design ingestion (Slack Events + signing verification), store last-processed timestamp, batch messages (per-thread then per-channel), redact PII, call ChatGPT API with system + few-shot prompts, and post results. Add queue (e.g., SQS or Redis), retry logic, metrics, and alerting.
5. Add monitoring: summary quality checks, run cost, task failure alerts, and a manual override command in Slack.

Notes on tools

ChatGPT (API) is a solid choice for building the custom service—good for rapid prompt iteration and integrations. Claude is another option if you want different long-context behavior or safety characteristics.

If you want, I can sketch a Zapier flow or a minimal architecture for the custom service (endpoints, queueing, prompt patterns).

Compare ChatGPT and Gemini