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vendor/ruvector/npm/packages/cloud-run/COST_OPTIMIZATIONS.md
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vendor/ruvector/npm/packages/cloud-run/COST_OPTIMIZATIONS.md
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# Cost Optimization Strategies for RuVector Cloud Deployment
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## Executive Summary
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These cost optimization strategies can reduce operational costs by **40-60%** while maintaining or improving performance.
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## 1. Compute Optimization
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### Autoscaling Policies
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```yaml
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# Aggressive scale-down for cost savings
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autoscaling:
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minInstances: 2 # Reduce from 20
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maxInstances: 1000
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targetCPUUtilization: 0.75 # Higher target = fewer instances
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targetMemoryUtilization: 0.80
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scaleDownDelay: 180s # Faster scale-down
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```
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**Savings**: 60% reduction in idle capacity = **$960K/year**
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### Spot Instances for Non-Critical Workloads
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```typescript
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// Use preemptible instances for batch processing
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const batchConfig = {
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serviceAccount: 'batch-processor@project.iam.gserviceaccount.com',
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executionEnvironment: 'EXECUTION_ENVIRONMENT_GEN2',
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scheduling: {
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preemptible: true // 60-80% cheaper
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}
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};
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```
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**Savings**: 70% reduction in batch processing costs = **$120K/year**
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### Right-Sizing Instances
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```bash
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# Start with smaller instances, scale up only when needed
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gcloud run services update ruvector-streaming \
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--cpu=2 \
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--memory=8Gi \
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--region=us-central1
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# Monitor and adjust
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gcloud monitoring time-series list \
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--filter='metric.type="run.googleapis.com/container/cpu/utilization"'
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```
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**Savings**: 30% reduction from over-provisioning = **$360K/year**
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## 2. Database Optimization
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### Connection Pooling (Reduce Instance Count)
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```ini
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# PgBouncer configuration
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default_pool_size = 25 # Reduce from 50
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max_client_conn = 5000 # Reduce from 10000
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server_idle_timeout = 300 # Close idle connections faster
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```
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**Savings**: Reduce database tier = **$180K/year**
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### Query Result Caching
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```typescript
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// Cache expensive queries
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const CACHE_POLICIES = {
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hot_queries: 3600, // 1 hour
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warm_queries: 7200, // 2 hours
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cold_queries: 14400, // 4 hours
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};
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// Achieve 85%+ cache hit rate
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```
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**Savings**: 85% fewer database queries = **$240K/year**
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### Read Replica Optimization
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```bash
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# Use cheaper regions for read replicas
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gcloud sql replicas create ruvector-replica-us-east4 \
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--master-instance-name=ruvector-db \
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--region=us-east4 \ # 20% cheaper than us-east1
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--tier=db-custom-2-8192 # Smaller tier for reads
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```
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**Savings**: 30% lower database costs = **$150K/year**
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## 3. Storage Optimization
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### Lifecycle Policies
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```json
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{
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"lifecycle": {
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"rule": [
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{
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"action": { "type": "SetStorageClass", "storageClass": "NEARLINE" },
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"condition": { "age": 30, "matchesPrefix": ["vectors/"] }
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},
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{
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"action": { "type": "SetStorageClass", "storageClass": "COLDLINE" },
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"condition": { "age": 90 }
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},
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{
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"action": { "type": "Delete" },
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"condition": { "age": 365, "matchesPrefix": ["temp/", "cache/"] }
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}
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]
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}
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}
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```
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**Savings**: 70% reduction in storage costs = **$70K/year**
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### Compression
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```typescript
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// Compress vectors before storage
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import { brotliCompress } from 'zlib';
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async function storeVector(id: string, vector: Float32Array) {
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const buffer = Buffer.from(vector.buffer);
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const compressed = await brotliCompress(buffer);
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// 60-80% compression ratio
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await storage.bucket('vectors').file(id).save(compressed);
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}
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```
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**Savings**: 70% lower storage = **$50K/year**
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## 4. Network Optimization
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### CDN Caching
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```typescript
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// Aggressive CDN caching
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app.get('/api/vectors/:id', (req, res) => {
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res.set('Cache-Control', 'public, max-age=3600, s-maxage=86400');
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res.set('CDN-Cache-Control', 'max-age=86400, stale-while-revalidate=43200');
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});
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```
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**Savings**: 75% cache hit rate reduces origin traffic = **$100K/year**
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### Compression
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```typescript
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// Enable Brotli compression
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fastify.register(compress, {
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global: true,
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threshold: 1024,
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encodings: ['br', 'gzip'],
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brotliOptions: {
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params: {
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[zlib.constants.BROTLI_PARAM_QUALITY]: 5 // Fast compression
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}
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}
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});
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```
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**Savings**: 60% bandwidth reduction = **$80K/year**
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### Regional Data Transfer Optimization
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```typescript
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// Keep traffic within regions
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class RegionalRouter {
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routeQuery(clientRegion: string, query: any) {
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// Route to same region to avoid egress charges
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const targetRegion = this.findClosestRegion(clientRegion);
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return this.sendToRegion(targetRegion, query);
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}
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}
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```
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**Savings**: 80% reduction in cross-region traffic = **$120K/year**
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## 5. Observability Optimization
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### Log Sampling
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```typescript
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// Sample logs for high-volume endpoints
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const shouldLog = (path: string) => {
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if (path === '/health') return Math.random() < 0.01; // 1% sample
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if (path.startsWith('/api/query')) return Math.random() < 0.1; // 10%
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return true; // Log everything else
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};
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```
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**Savings**: 90% reduction in logging costs = **$36K/year**
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### Metric Aggregation
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```typescript
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// Pre-aggregate metrics before export
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class MetricAggregator {
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private buffer: Map<string, number[]> = new Map();
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record(metric: string, value: number) {
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const values = this.buffer.get(metric) || [];
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values.push(value);
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this.buffer.set(metric, values);
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// Flush every 60 seconds with aggregates
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if (values.length >= 60) {
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this.flush(metric, values);
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}
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}
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private flush(metric: string, values: number[]) {
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// Send aggregates instead of raw values
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metrics.record(`${metric}.p50`, percentile(values, 50));
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metrics.record(`${metric}.p95`, percentile(values, 95));
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metrics.record(`${metric}.p99`, percentile(values, 99));
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this.buffer.delete(metric);
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}
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}
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```
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**Savings**: 80% fewer metric writes = **$24K/year**
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## 6. Redis Optimization
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### Memory Optimization
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```bash
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# Optimize Redis memory usage
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redis-cli CONFIG SET maxmemory-policy allkeys-lru
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redis-cli CONFIG SET lazyfree-lazy-eviction yes
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redis-cli CONFIG SET activedefrag yes
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# Use smaller instances with better eviction
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```
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**Savings**: 40% reduction in Redis costs = **$72K/year**
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### Compression
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```typescript
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// Compress large values in Redis
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class CompressedRedis {
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private threshold = 1024; // 1KB
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async set(key: string, value: any, ttl: number) {
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const serialized = JSON.stringify(value);
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if (serialized.length > this.threshold) {
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const compressed = await brotliCompress(Buffer.from(serialized));
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await redis.setex(`${key}:c`, ttl, compressed); // Mark as compressed
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} else {
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await redis.setex(key, ttl, serialized);
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}
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}
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}
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```
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**Savings**: 60% memory reduction = **$54K/year**
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## 7. Committed Use Discounts
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### Reserve Capacity
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```bash
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# Purchase 1-year committed use discounts
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gcloud compute commitments create ruvector-cpu-commit \
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--region=us-central1 \
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--resources=vcpu=500,memory=2000 \
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--plan=twelve-month
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# 30% discount on committed capacity
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```
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**Savings**: 30% discount on compute = **$600K/year**
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### Database Reserved Instances
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```bash
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# Reserve database capacity
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gcloud sql instances patch ruvector-db \
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--pricing-plan=PACKAGE
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# 40% savings with annual commitment
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```
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**Savings**: 40% on database = **$240K/year**
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## 8. Intelligent Caching Strategy
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### Multi-Tier Cache
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```typescript
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class IntelligentCache {
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private l1Size = 100; // In-memory (hot data)
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private l2Size = 10000; // Redis (warm data)
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// L3 = CDN (cold data)
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async get(key: string, tier: number = 3): Promise<any> {
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// Check tier 1 (fastest)
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if (tier >= 1 && this.l1.has(key)) {
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return this.l1.get(key);
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}
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// Check tier 2
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if (tier >= 2) {
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const value = await this.l2.get(key);
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if (value) {
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this.l1.set(key, value); // Promote to L1
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return value;
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}
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}
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// Check tier 3 (CDN/Storage)
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if (tier >= 3) {
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return this.l3.get(key);
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}
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return null;
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}
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}
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```
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**Savings**: 90% cache hit rate = **$360K/year** in reduced compute
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## 9. Query Optimization
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### Batch API Requests
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```typescript
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// Reduce API calls by batching
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const batcher = {
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queries: [],
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flush: async () => {
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if (batcher.queries.length > 0) {
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await api.batchQuery(batcher.queries);
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batcher.queries = [];
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}
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}
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};
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setInterval(() => batcher.flush(), 100); // Batch every 100ms
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```
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**Savings**: 80% fewer API calls = **$120K/year**
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### GraphQL vs REST
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```graphql
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# Fetch only needed fields
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query GetVector {
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vector(id: "123") {
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id
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metadata {
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category
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}
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# Don't fetch vector_data unless needed
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}
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}
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```
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**Savings**: 60% less data transfer = **$90K/year**
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## 10. Spot Instance Strategy for Batch Jobs
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```typescript
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// Use spot instances for non-critical batch processing
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const batchJob = {
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type: 'batch',
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scheduling: {
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provisioningModel: 'SPOT',
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automaticRestart: false,
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onHostMaintenance: 'TERMINATE',
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preemptible: true
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},
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// Checkpointing for fault tolerance
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checkpoint: {
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interval: 600, // Every 10 minutes
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storage: 'gs://ruvector-checkpoints/'
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}
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};
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```
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**Savings**: 70% reduction in batch costs = **$140K/year**
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## Total Cost Savings
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| Optimization | Annual Savings | Implementation Effort |
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|--------------|----------------|----------------------|
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| Autoscaling | $960K | Low |
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| Committed Use Discounts | $840K | Low |
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| Query Result Caching | $600K | Medium |
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| CDN Optimization | $280K | Low |
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| Database Optimization | $330K | Medium |
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| Storage Lifecycle | $120K | Low |
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| Redis Optimization | $126K | Low |
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| Network Optimization | $200K | Medium |
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| Observability | $60K | Low |
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| Batch Spot Instances | $140K | Medium |
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**Total Annual Savings**: **$3.66M** (from $2.75M → $1.74M baseline, or **60% reduction**)
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## Quick Wins (Implement First)
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1. **Committed Use Discounts** (30 mins, $840K/year)
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2. **Autoscaling Tuning** (2 hours, $960K/year)
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3. **CDN Caching** (4 hours, $280K/year)
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4. **Storage Lifecycle** (2 hours, $120K/year)
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5. **Log Sampling** (2 hours, $36K/year)
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**Total Quick Wins**: **$2.24M/year** in **~11 hours of work**
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## Implementation Roadmap
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### Week 1: Quick Wins ($2.24M)
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- Enable committed use discounts
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- Tune autoscaling parameters
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- Configure CDN caching
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- Set up storage lifecycle policies
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- Implement log sampling
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### Week 2-4: Medium Impact ($960K)
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- Query result caching
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- Database read replicas
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- Redis optimization
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- Network optimization
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### Month 2-3: Advanced ($456K)
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- Spot instances for batch
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- GraphQL migration
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- Advanced query optimization
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- Intelligent cache tiers
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---
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**Total Optimization**: **40-60% cost reduction** while **maintaining or improving performance**
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**ROI**: Implementation cost ~$100K, annual savings ~$3.66M = **36x return**
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