Case Study: Cloud Optimization

AWS Deadline & Cloud Rendering Cost Optimization

How we helped a mid-sized animation studio reduce their AWS rendering costs by 68% through intelligent resource optimization while improving performance.

68%

Cost Reduction

23%

Performance Boost

€8,200

Monthly Cost Savings

Case Study

Cloud Rendering Optimization

Learn how we helped a mid-sized animation studio transform their AWS Deadline deployment from an inefficient resource drain to a cost-effective, high-performance render farm.

The Challenge

A mid-sized animation studio with 35 artists was experiencing rapidly escalating cloud rendering costs as they scaled their operations. Their monthly AWS bills had grown to over €12,000, making it difficult to maintain profitability on smaller projects.

AWS Deadline Inefficiencies

  • Static worker pools not aligned with workload patterns
  • Inefficient resource distribution causing uneven rendering loads
  • Single-AZ deployment creating regional pricing inefficiencies

Compute Resource Misalignment

  • Using expensive instances for all workloads regardless of requirements
  • GPU instances running CPU-only tasks, wasting expensive resources
  • 100% On-Demand instance usage with no Spot Instance integration

Initial AWS Cost Breakdown

Monthly EC2 Cost
€9,600
Monthly S3 & Data Transfer
€2,400

Our Solution

We implemented a comprehensive cloud optimization strategy focused on AWS Deadline configuration, resource allocation, and cost management.

AWS Deadline Optimization

We reconfigured Deadline Groups and Pools with job-type specific routing rules, creating dedicated pools for different task types and implementing adaptive instance selection based on frame complexity.

Technical Implementation

  • Custom Deadline event plugins (Python)
  • Memory-intensive tasks to r5.2xlarge
  • GPU rendering to g4dn.xlarge
  • Standard frames to c5.xlarge

Spot Instance Migration

We reconfigured their render farm to leverage AWS Spot Instances with sophisticated fault tolerance and intelligent bidding strategies, significantly reducing compute costs.

Spot vs. On-Demand Cost Comparison
Instance TypeOn-DemandSpotSavings
c5.2xlarge€0.34/hr€0.10/hr-71%
r5.2xlarge€0.50/hr€0.15/hr-70%
g4dn.xlarge€0.53/hr€0.16/hr-69%

Storage & Lifecycle Optimization

We implemented intelligent storage lifecycle policies that automatically moved completed projects to lower-cost storage tiers, while maintaining rapid access to active project assets.

"Our intelligent storage lifecycle policies reduced the studio's storage costs by 47% while maintaining rapid access to frequently used assets."

Results

Before vs After TraynMe

Before:
€12,000/mo
After:
€3,800/mo
"TraynMe's optimization strategies completely transformed our cloud rendering approach. We're now able to take on more projects with confidence in our rendering costs."

— Creative Director, Animation Studio

Key Takeaways

  • Instance Type Matters — Matching instance families to workload characteristics yielded significant cost savings without performance compromise.
  • Spot Instance Diversification — Spreading workloads across instance families and sizes maintains high availability while capturing maximum cost benefits.
  • Storage Lifecycle Optimization — Implementing tiered storage strategies based on project status creates substantial cost savings with minimal impact.
  • Monitoring & Governance — Real-time cost visibility and automated controls are essential for maintaining optimization over time.

Ready to optimize your AWS Deadline costs?

Our cloud rendering efficiency experts can help you achieve the same results with a proven optimization methodology.

Work with us

In the competitive world of video production, every second counts. From tight deadlines to rendering complex visual effects, your team's focus should be on creativity and delivering high-quality content — not wrestling with server setups and infrastructure challenges.