Batch vs. Streaming for AI Workloads

Resources & Utilities • ~7–8 min read • Updated Nov 27, 2024

Context

“Real-time” is seductive—and expensive. Many AI use cases don’t need it. Your job is to size the decision latency and choose the simplest pipeline that meets it. Batch shines when you can tolerate delay, amortize cost, and simplify ops. Streaming wins where freshness and continuity change outcomes, not just dashboards.

Core Framework: The Latency Budget

  1. Decision Latency (DL): How quickly the business decision must change after new data arrives (e.g., 5s, 5m, 1h, 1d).
  2. Pipeline Latency (PL): Ingest → transform → retrieve → infer → act. Profile each stage.
  3. Freshness Criticality (FC): Customer harm, revenue, or safety impact if stale (High/Med/Low).
  4. Volatility & Burstiness: Spiky traffic favors buffering and batch micro-windows.
  5. Cost & Ops Complexity: More always-on services, more on-call. Price the overhead.

When Nightly (or Hourly) Batch Wins

  • Planning & scoring: Lead prioritization, risk tiers, campaign segments.
  • Slow-moving features: User profiles, product catalogs, derived aggregates.
  • Heavy transforms: Joins and quality checks that benefit from orderly windows.
  • Cost-conscious back office: Re-run overnight with predictable spend.

When Streaming is Worth It

  • Operational control loops: Dispatching, anomaly response, dynamic routing.
  • High-stakes freshness: Fraud, abuse, safety events, market microstructure.
  • Conversational & agentic UX: Tokens, events, and tool calls must feel live.
  • Feature stores with SLAs: Near-real-time features feeding online models.

Practical Patterns

  1. Micro-batch first: 1–5 minute windows via scheduled jobs or stream compaction—often “real-time enough.”
  2. Dual-path architecture: Online (hot) for the few features that need it; offline (cold) for everything else.
  3. Late-binding transforms: Normalize/validate as close to inference as possible to avoid reprocessing churn.
  4. Idempotency + replay: Assign stable IDs; make sinks idempotent; keep replay logs for backfills and drift studies.
  5. Freshness monitors: Emit staleness seconds and alert when DL < PL.

Recommended Actions

  1. Write the DL/FC table: For each use case, record decision latency and freshness criticality. Sort by FC then DL.
  2. Start with batch + SLAs: Prove value with hourly/nightly. Add streaming only where DL violations cost real money or risk.
  3. Measure end-to-end PL: Instrument from event to action. Kill “real-time” talk until PL < DL for a week.
  4. Harden the hot path: Rate limits, backpressure, autoscaling, circuit breakers; keep it tiny.
  5. Retain raw events: 7–30 days for backfills, evaluations, and incident replays.

Common Pitfalls

  • Streaming by default: Paying 24/7 tax for decisions made once a day.
  • Unowned freshness: No SLOs → silent staleness; decisions drift before anyone notices.
  • Wide hot path: Everything in real-time → fragile systems and noisy on-call.
  • No replay story: Incidents and model drift cannot be reproduced.

Quick Win Checklist

  • Publish DL/FC for top 5 use cases; mark which truly need <60s freshness.
  • Flip non-critical streams to 5-minute micro-batch; compare cost/latency.
  • Add event_age_seconds to logs and dashboards; alert at 2× DL.
  • Define a tiny, idempotent hot path; everything else rides batch.

Closing

Real-time is a business requirement, not a vibe. Size the latency budget, keep the hot path small, and meet freshness with the simplest pipeline that pays back. Your uptime, costs, and sleep will thank you.

Essay by OneMind Strata Team