Table of Contents
For RAG and AI data pipelines, the architecture choice between batch and streaming matters. Batch is simpler and cheaper; streaming gives near-real-time freshness. Most production deployments are hybrid: batch baseline + streaming for high-priority sources.
Batch (nightly): cheaper, simpler ops, suitable for stable knowledge bases. Streaming (Kafka / Pub/Sub): near-real-time, complex ops, needed for time-sensitive content (news, support tickets, social listening). Most teams: batch as default + streaming for specific high-priority sources.
Comparison
| Aspect | Batch | Streaming |
|---|---|---|
| Freshness | Hours-day lag | Seconds-minutes |
| Ops complexity | Low | High (Kafka, Pub/Sub, etc.) |
| Cost | Low (off-peak compute) | Higher (always-on infrastructure) |
| Resume on failure | Easy (rerun batch) | Complex (offset management) |
| Best for | Stable KBs (docs, manuals, policies) | Time-sensitive (news, tickets, social) |
When each
- Batch: corporate KBs, technical manuals, regulatory documents, archive content. Most internal tooling.
- Streaming: news / media monitoring, customer-support ticket monitoring, social listening, real-time RAG over conversations
- Hybrid: nightly batch baseline + streaming overlays for specific high-priority sources
Verdict
For most production AI deployments, batch is the right default. Streaming adds real complexity (Kafka cluster, exactly-once semantics, offset management) that should be earned by a real freshness requirement. Many teams that started with streaming have rationalised back to batch + targeted streaming for specific sources.
Bottom line
Batch by default; stream when freshness justifies it. See batch vs realtime.
