Grammarly Performance Tuning

Latency Benchmarks

Instructions

Cache Score Results

import { LRUCache } from 'lru-cache';
import { createHash } from 'crypto';

const scoreCache = new LRUCache<string, any>({ max: 500, ttl: 3600000 });

async function cachedScore(text: string, token: string) {
  const key = createHash('sha256').update(text).digest('hex');
  const cached = scoreCache.get(key);
  if (cached) return cached;
  const score = await grammarlyClient.score(text);
  scoreCache.set(key, score);
  return score;
}

Parallel API Calls

// Score + AI detect in parallel (they're independent)
async function fullAudit(text: string, token: string) {
  const [score, ai] = await Promise.all([
    grammarlyClient.score(text),
    grammarlyClient.detectAI(text),
  ]);
  return { score, ai };
}

Overview

Tune latency and throughput using bounded synthetic fixtures and aggregate metrics. A performance gain is invalid if it expands text retention, access scope, error rate, or duplication risk.

Prerequisites

• Baseline latency percentiles, error/quota rates, synthetic fixture revision, and an approved error budget.
• A rollback revision for cache, concurrency, chunking, and retry settings, plus retention-safe telemetry.

Output

Return a tuning receipt with baseline/canary percentile bands, cache/concurrency/chunking revisions, quota and error outcomes, fixture result, retention check, owner approval, and rollback reference. Use aggregates only.

Error Handling

Roll back for quota saturation, increased errors, retained text in telemetry, duplicated submissions, or incorrect synthetic results. Do not raise concurrency or cache duration to hide a failure.

Examples

env=sandbox; p95=420ms->310ms; concurrency=2; cache=r4; quota=within-budget; fixture=pass; rollback=perf-r3 documents a safe canary.

Resources

• Grammarly API

Next Steps

For cost optimization, see grammarly-cost-tuning.