Video Streaming Platform
Aggressive AWS FinOps achieving massive cost reductions on a high-bandwidth, on-demand video streaming architecture serving thousands of monthly active corporate users.
Overview
A major corporate learning platform experienced severely escalating AWS billing due to skyrocketing bandwidth, log ingestion, database, and compute costs. Providing on-demand video streaming to thousands of active corporate users simultaneously created massive data transfer volumes, large storage footprints for media assets, and expensive always-on backend services. As the primary Cloud Engineer, I led a specialized FinOps initiative to rein in the budget without sacrificing performance, resiliency, or user experience.
FinOps Interventions
- Storage Tiering & Lifecycle Management: Automated lifecycle policies migrating terabytes of cold video assets from standard S3 to Infrequent Access (IA) and Glacier deep archive, drastically cutting at-rest storage costs.
- CloudFront to Cloudflare Migration: Re-architected the edge caching layer by migrating high-traffic caching workloads from CloudFront to Cloudflare, improving cache-hit ratios, strengthening DDoS protection and WAF coverage, and reducing origin egress plus CDN delivery costs for static assets and frequently accessed video metadata.
- Compute Right-Sizing: Swapped older generation EC2 instances for ARM-based AWS Graviton instances specifically tailored for video transcoding algorithms, dropping direct compute costs by over 20% while speeding up encoding.
- Log Visibility with Lower Log Costs: Rationalized CloudWatch log retention, filtered noisy application and access logs, moved low-value historical logs to cheaper archival paths, and built clearer dashboards for operations teams so observability improved while log ingestion and retention costs dropped materially.
- Database Right-Sizing & Serverless Adoption: Audited database utilization patterns, right-sized overprovisioned RDS instances, optimized storage allocations, and introduced serverless database usage for burst-heavy or intermittently used workloads so the platform only paid for database capacity when demand actually existed.
- Savings Plans & Commitment Strategy: Engineered detailed compute usage forecasting to purchase AWS Savings Plans only for stable baseline workloads, combining committed discounts with on-demand elasticity for traffic spikes to lock in savings without overcommitting the estate.
Result
Restored financial predictability across bandwidth, logging, compute, and database spend. The combined AWS FinOps and edge optimization program significantly lowered monthly cloud costs, improved protection against volumetric attacks, increased operational visibility, and left the platform able to serve thousands of concurrent enterprise users at a far more sustainable unit cost.