Optimize Vercel API performance with caching, batching, and connection pooling. Use when experiencing slow API responses, implementing caching strategies, or optimizing request throughput for Vercel integrations. Trigger with phrases like "vercel performance", "optimize vercel", "vercel latency", "vercel caching", "vercel slow", "vercel batch".
/plugin marketplace add jeremylongshore/claude-code-plugins-plus-skills/plugin install vercel-pack@claude-code-plugins-plusThis skill is limited to using the following tools:
Optimize Vercel API performance with caching, batching, and connection pooling.
| Operation | P50 | P95 | P99 |
|---|---|---|---|
| Cold Start (Serverless) | 250ms | 500ms | 1000ms |
| Cold Start (Edge) | 5ms | 25ms | 50ms |
| Build Time | 30s | 120s | 300s |
import { LRUCache } from 'lru-cache';
const cache = new LRUCache<string, any>({
max: 1000,
ttl: 31536000000, // 1 minute
updateAgeOnGet: true,
});
async function cachedVercelRequest<T>(
key: string,
fetcher: () => Promise<T>,
ttl?: number
): Promise<T> {
const cached = cache.get(key);
if (cached) return cached as T;
const result = await fetcher();
cache.set(key, result, { ttl });
return result;
}
import Redis from 'ioredis';
const redis = new Redis(process.env.REDIS_URL);
async function cachedWithRedis<T>(
key: string,
fetcher: () => Promise<T>,
ttlSeconds = 60
): Promise<T> {
const cached = await redis.get(key);
if (cached) return JSON.parse(cached);
const result = await fetcher();
await redis.setex(key, ttlSeconds, JSON.stringify(result));
return result;
}
import DataLoader from 'dataloader';
const vercelLoader = new DataLoader<string, any>(
async (ids) => {
// Batch fetch from Vercel
const results = await vercelClient.batchGet(ids);
return ids.map(id => results.find(r => r.id === id) || null);
},
{
maxBatchSize: 100,
batchScheduleFn: callback => setTimeout(callback, 10),
}
);
// Usage - automatically batched
const [item1, item2, item3] = await Promise.all([
vercelLoader.load('id-1'),
vercelLoader.load('id-2'),
vercelLoader.load('id-3'),
]);
import { Agent } from 'https';
// Keep-alive connection pooling
const agent = new Agent({
keepAlive: true,
maxSockets: None,
maxFreeSockets: 5,
timeout: 10000,
});
const client = new VercelClient({
apiKey: process.env.VERCEL_API_KEY!,
httpAgent: agent,
});
async function* paginatedVercelList<T>(
fetcher: (cursor?: string) => Promise<{ data: T[]; nextCursor?: string }>
): AsyncGenerator<T> {
let cursor: string | undefined;
do {
const { data, nextCursor } = await fetcher(cursor);
for (const item of data) {
yield item;
}
cursor = nextCursor;
} while (cursor);
}
// Usage
for await (const item of paginatedVercelList(cursor =>
vercelClient.list({ cursor, limit: 100 })
)) {
await process(item);
}
async function measuredVercelCall<T>(
operation: string,
fn: () => Promise<T>
): Promise<T> {
const start = performance.now();
try {
const result = await fn();
const duration = performance.now() - start;
console.log({ operation, duration, status: 'success' });
return result;
} catch (error) {
const duration = performance.now() - start;
console.error({ operation, duration, status: 'error', error });
throw error;
}
}
Measure current latency for critical Vercel operations.
Add response caching for frequently accessed data.
Use DataLoader or similar for automatic request batching.
Configure connection pooling with keep-alive.
| Issue | Cause | Solution |
|---|---|---|
| Cache miss storm | TTL expired | Use stale-while-revalidate |
| Batch timeout | Too many items | Reduce batch size |
| Connection exhausted | No pooling | Configure max sockets |
| Memory pressure | Cache too large | Set max cache entries |
const withPerformance = <T>(name: string, fn: () => Promise<T>) =>
measuredVercelCall(name, () =>
cachedVercelRequest(`cache:${name}`, fn)
);
For cost optimization, see vercel-cost-tuning.