Anthropic Load & Scale

Overview

Scale Claude usage for high-throughput applications. Covers four strategies: Message Batches (10K requests, 50% off, no rate limits), request queues with concurrency control via p-limit, tier upgrades (Tier 1-4 + Scale), and model selection for throughput (Haiku is 3-4x faster than Sonnet).

Scaling Strategies

Instructions

Step 1: Message Batches (Best for Bulk)

// 10K requests per batch, 50% cheaper, no rate limits
const batch = await client.messages.batches.create({
  requests: items.map((item, i) => ({
    custom_id: `${i}`,
    params: { model: 'claude-sonnet-4-20250514', max_tokens: 1024, messages: [{ role: 'user', content: item }] },
  })),
});
// Process up to 100 concurrent batches

Step 2: Request Queue with Concurrency Control

import pLimit from 'p-limit';

// Match your rate limit tier
const limit = pLimit(10); // 10 concurrent requests

const results = await Promise.all(
  inputs.map(input =>
    limit(() => client.messages.create({
      model: 'claude-sonnet-4-20250514',
      max_tokens: 1024,
      messages: [{ role: 'user', content: input }],
    }))
  )
);

Step 3: Tier Upgrades

Increase your spending to unlock higher tiers:

Tier	RPM	Input TPM	How to Qualify
1	50	40K	Free
2	1,000	80K	$40+ total spend
3	2,000	160K	$200+ total spend
4	4,000	400K	$400+ total spend
Scale	Custom	Custom	Contact sales

Step 4: Model Selection for Throughput

// Haiku processes 3-4x faster than Sonnet, 8x faster than Opus
// Use the fastest model that meets quality requirements
const model = taskComplexity === 'simple' ? 'claude-haiku-4-5-20251001' : 'claude-sonnet-4-20250514';

Monitoring at Scale

// Track throughput metrics
let requestCount = 0;
let tokenCount = 0;

setInterval(() => {
  console.log(`Throughput: ${requestCount} req/min, ${tokenCount} tokens/min`);
  requestCount = 0;
  tokenCount = 0;
}, 60_000);

Output

• Batch processing configured for bulk workloads (50% cheaper, no rate limits)
• Concurrency-controlled request queue matching rate limit tier
• Rate limit tier upgraded by increasing cumulative spend
• Throughput metrics tracked (requests/min, tokens/min)

Error Handling

Error	Cause	Solution
API Error	Check error type and status code	See clade-common-errors

Examples

See Message Batches example, p-limit concurrency control, Tier Upgrades table, and Monitoring at Scale metrics tracking above.

Resources

• Rate Limits
• Message Batches

Next Steps

See clade-reliability-patterns for fault-tolerant high-scale patterns.

Prerequisites

• Completed clade-rate-limits for understanding tier limits
• High-volume use case requiring more than basic tier throughput
• For batches: tolerance for async processing (24h SLA)