When to Use Claude Batch vs Real-Time (2026)

The Claude Batch API saves 50% on every token but takes up to 1 hour to return results. The real-time API costs twice as much but responds in seconds. Choosing wrong either wastes $562.50/week on unnecessary premium pricing or delays time-sensitive responses by an hour. Here is how to decide.

The Setup

You manage three Claude-powered features: a live chat assistant (needs sub-second responses), a nightly content pipeline (generates 500 articles), and a code review system (reviews PRs within 2 hours of submission).

The chat assistant must use real-time API – there is no alternative. The content pipeline is a clear batch candidate. The code review sits in the gray zone: 2 hours of acceptable latency versus 1 hour of batch processing time.

Current spend: $3,200/month across all three. Migrating the right workloads to batch saves $1,100/month without degrading any user experience.

The Math

Three workloads, Sonnet 4.6:

Live chat (real-time only):

  • 200K requests/month x 3K input + 1K output avg
  • Input: 600M x $3.00/MTok = $1,800
  • Output: 200M x $15.00/MTok = $3,000
  • Total: $4,800/month (cannot batch)

Content pipeline (batch candidate):

  • 15,000 articles/month x 5K input + 3K output
  • Standard: $225 input + $675 output = $900/month
  • Batch: $112.50 + $337.50 = $450/month
  • Savings: $450/month

Code review (batch candidate):

  • 3,000 PRs/month x 20K input + 5K output
  • Standard: $180 input + $225 output = $405/month
  • Batch: $90 + $112.50 = $202.50/month
  • Savings: $202.50/month

Total monthly savings from batch migration: $652.50

The Technique

Use this decision matrix to classify each workload:

def should_use_batch(
    acceptable_latency_minutes: int,
    requests_per_day: int,
    avg_input_tokens: int,
    avg_output_tokens: int,
    model: str = "claude-sonnet-4-6-20250929"
) -> dict:
    """Determine whether to use batch or real-time API."""
    # Pricing lookup (standard vs batch)
    prices = {
        "claude-opus-4-7-20250415": {
            "std_in": 5.00, "std_out": 25.00,
            "batch_in": 2.50, "batch_out": 12.50
        },
        "claude-sonnet-4-6-20250929": {
            "std_in": 3.00, "std_out": 15.00,
            "batch_in": 1.50, "batch_out": 7.50
        },
        "claude-haiku-4-5-20251001": {
            "std_in": 1.00, "std_out": 5.00,
            "batch_in": 0.50, "batch_out": 2.50
        }
    }
    p = prices[model]
    daily_tokens_in = requests_per_day * avg_input_tokens
    daily_tokens_out = requests_per_day * avg_output_tokens
    std_daily = (daily_tokens_in * p["std_in"] +
                 daily_tokens_out * p["std_out"]) / 1e6
    batch_daily = (daily_tokens_in * p["batch_in"] +
                   daily_tokens_out * p["batch_out"]) / 1e6
    savings_daily = std_daily - batch_daily
    savings_monthly = savings_daily * 30
    # Decision logic
    can_batch = acceptable_latency_minutes >= 60
    worth_batching = savings_monthly > 10  # Minimum $10/mo savings

    recommendation = "BATCH" if (can_batch and worth_batching) else "REAL-TIME"
    return {
        "recommendation": recommendation,
        "real_time_monthly": f"${std_daily * 30:.2f}",
        "batch_monthly": f"${batch_daily * 30:.2f}",
        "monthly_savings": f"${savings_monthly:.2f}",
        "reason": (
            f"Latency allows batch ({acceptable_latency_minutes}min > 60min) "
            f"and saves ${savings_monthly:.2f}/month"
            if recommendation == "BATCH"
            else f"Latency too tight ({acceptable_latency_minutes}min)"
            if not can_batch
            else f"Savings too small (${savings_monthly:.2f}/month)"
        )
    }
# Evaluate three workloads
workloads = [
    {"name": "Live chat", "latency": 1, "rpd": 6667, "inp": 3000, "out": 1000},
    {"name": "Content gen", "latency": 1440, "rpd": 500, "inp": 5000, "out": 3000},
    {"name": "Code review", "latency": 120, "rpd": 100, "inp": 20000, "out": 5000},
]
for w in workloads:
    result = should_use_batch(w["latency"], w["rpd"], w["inp"], w["out"])
    print(f"{w['name']}: {result['recommendation']} "
          f"(saves {result['monthly_savings']}/mo)")

For workloads in the gray zone (latency between 30-120 minutes), consider a hybrid approach:

# Hybrid: use real-time for urgent requests, batch for the rest
python3 -c "
# Simulate priority-based routing
import json
requests = [json.loads(l) for l in open('daily_requests.jsonl')]
urgent = [r for r in requests if r.get('priority') == 'high']
normal = [r for r in requests if r.get('priority') != 'high']
pct_urgent = len(urgent) / len(requests) * 100
pct_normal = len(normal) / len(requests) * 100
print(f'Urgent (real-time): {len(urgent)} ({pct_urgent:.0f}%)')
print(f'Normal (batch): {len(normal)} ({pct_normal:.0f}%)')
print(f'Savings: {pct_normal * 0.5:.0f}% of total cost moved to 50% discount')
"

Typical routing rules:

  • User-facing, interactive: Always real-time
  • Background processing, reporting: Always batch
  • Internal tools with flexible SLA: Batch during off-hours, real-time during business hours
  • CI/CD integration: Batch if PR review SLA exceeds 1 hour, real-time otherwise

The Tradeoffs

Batch migration introduces operational complexity:

  • Queue management: Batch requests are fire-and-forget. You need polling or webhook infrastructure to handle completion.
  • Error handling changes: Real-time errors are synchronous. Batch errors appear in results after processing, requiring different retry patterns.
  • Capacity planning: Batches compete for the same API capacity. Submitting 100K requests during peak hours may extend processing time beyond the typical 1 hour.
  • Testing difficulty: Developing against the batch API is slower because every test requires polling. Use real-time API for development and batch for production.

Implementation Checklist

  1. List all Claude API workloads with their latency requirements
  2. Run the decision function above for each workload
  3. Migrate clear batch candidates first (latency > 60 minutes)
  4. Build polling infrastructure for batch result retrieval
  5. Implement priority-based routing for gray-zone workloads
  6. Monitor batch processing times for 2 weeks before migrating additional workloads
  7. Set up cost tracking per workload to verify 50% savings

Measuring Impact

Track per-workload metrics after migration:

  • Cost per request by mode: Real-time vs batch for the same workload type. Should show exactly 50% reduction.
  • Batch completion time p50/p95: Median and 95th percentile processing time. Alert if p95 exceeds 2 hours.
  • Migration coverage: Percentage of total API spend routed through batch. Target: 40-60% for a typical mixed workload.
  • Review monthly billing to confirm projected savings are materializing

Which model? → Take the 5-question quiz in our Model Selector.

Try it: Estimate your monthly spend with our Cost Calculator.

See Also