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.