Video Infrastructure Cost Optimization

Video infrastructure costs break down into three main categories:

Transcoding compute (20-40% of total) - Per-minute or per-hour compute costs - GPU vs CPU tradeoffs - Encoding speed vs quality settings

Storage (10-20% of total) - Source files (often large, high-bitrate) - Transcoded renditions (multiple per video) - Retention policies and lifecycle management

CDN egress (40-60% of total) - Per-GB bandwidth charges - Geographic pricing variations - Cache hit ratios dramatically impact costs

Optimization strategies differ for each component. A 30% reduction in any category meaningfully impacts total spend.

Create only necessary renditions: - Analyze device/resolution distribution in analytics - If 95% of viewers use 1080p screens, don't create 4K - Consider removing lowest quality levels if rarely used

Hardware encoding for speed: - NVENC/QuickSync: 5-10x faster, slightly lower quality - Use for preview/draft renders, real-time needs - Software encoding for final delivery quality

Batch processing optimization: - Process during off-peak hours - Use spot instances (70% savings) - Queue priority: premium content first

Per-title encoding: - Analyze content complexity (animation vs action) - Simple content needs less bitrate for same quality - Netflix reports 20% bandwidth savings with per-title - Requires investment in analysis tooling

Intelligent lifecycle policies: - Hot storage: first 30 days, frequently accessed - Warm storage: 30-90 days, occasional access - Cold storage: 90+ days, rare access - Archive: legal retention only

Rendition management: - Keep all renditions for popular content (top 20%) - Reduce renditions for long-tail after 90 days - Delete source files after successful transcode (if allowed) - Consider re-transcoding from lower quality if needed

Just-in-time transcoding: - Don't transcode everything upfront - Transcode on first request, cache result - Best for large catalogs with long-tail viewing - Trade latency for storage costs

Deduplication: - Detect duplicate uploads (hash matching) - Reference same output files - Common in UGC platforms

CDN egress is typically the largest cost component. Optimization here has the biggest impact.

Maximize cache hit ratios: - Consistent URL structure (no random query params) - Long TTLs for video segments (1 year+) - Shield/mid-tier caching to reduce origin requests - Pre-warm cache for expected popular content

Efficient codecs reduce bandwidth: - VP9 saves 30% vs H.264 - AV1 saves 30% vs VP9 (50%+ vs H.264) - Encoding cost increase pays back quickly at scale

Geographic optimization: - Some regions cost 5-10x more than others - Route traffic through cost-effective regions when possible - Consider regional CDN providers for specific markets

Committed use discounts: - 20-40% savings for committed bandwidth - Negotiate based on actual usage patterns - Multi-CDN strategy for best pricing

Key metrics to track: - Cost per minute of video processed - Cost per GB delivered - Cost per viewer hour - Cache hit ratio

Attribution and allocation: - Tag resources by content type, customer, team - Cloud cost allocation tags - Build cost dashboards per video/content type

Optimization opportunities: - Videos with high cost-per-view (optimize encoding) - Low cache hit content (check URL consistency) - Unused renditions (remove from encoding ladder) - Geographic cost anomalies (routing issues)

Regular reviews: - Monthly cost analysis - Quarterly encoding ladder review - Annual CDN contract negotiation

A well-optimized pipeline can reduce costs by 40-60% compared to naive implementations. The investment in optimization tooling typically pays back within months.