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
Next Steps
See clade-reliability-patterns for fault-tolerant high-scale patterns.
Prerequisites
- Completed
clade-rate-limitsfor understanding tier limits - High-volume use case requiring more than basic tier throughput
- For batches: tolerance for async processing (24h SLA)