63% Total Delivery Savings: Inside a Multi-CDN + AI Preprocessing Partnership Workflow (Terraform Code Included)

Introduction

Video streaming costs are spiraling out of control. A 500 TB/month OTT newcomer recently achieved 63.25% total delivery savings by combining three breakthrough technologies: AI preprocessing, multi-CDN routing, and AWS Lambda automation. (Sima Labs) This technical deep-dive expands on Sima Labs' July 2025 cost-arbitrage white-paper, breaking down the exact three-step recipe that DevOps teams can adapt in hours rather than weeks.

The streaming industry faces unprecedented pressure to deliver high-quality content at increasingly high resolutions while managing bandwidth costs. (AI-Driven Video Compression) Traditional video transcoders use a one-size-fits-all approach that falls short when trying to optimize bitrate, file size, video quality, and encoding speed simultaneously. (AI-Driven Video Compression)

This guide provides a ready-to-test template featuring Terraform routing rules and AI-driven traffic prediction examples that DevOps teams can implement immediately. (Sima Labs)

The Three-Step Cost Arbitrage Recipe

Step 1: SimaBit AI Preprocessing Engine

Sima Labs' SimaBit represents a paradigm shift in video preprocessing technology. The patent-filed AI preprocessing engine reduces video bandwidth requirements by 22% or more while boosting perceptual quality. (Sima Labs) Unlike traditional approaches, SimaBit slips in front of any encoder—H.264, HEVC, AV1, AV2, or custom—allowing streamers to eliminate buffering and shrink CDN costs without changing existing workflows.

The engine has been extensively benchmarked on Netflix Open Content, YouTube UGC, and the OpenVid-1M GenAI video set, with verification via VMAF/SSIM metrics and golden-eye subjective studies. (Sima Labs) This comprehensive testing ensures compatibility across diverse content types and quality standards.

Deep learning techniques are being investigated for their potential to advance video coding without imposing changes at the client side. (Deep Video Precoding) The key challenge lies in making deep neural networks work in conjunction with existing and upcoming video codecs while maintaining compatibility with existing adaptive video streaming systems. (Deep Video Precoding)

Step 2: Multi-CDN Routing Strategy

Multi-CDN strategies have evolved beyond simple failover mechanisms. Modern implementations leverage AI to analyze video content in real-time, predict network conditions, and automatically adjust streaming quality for optimal viewing experience. (AI Video Quality Enhancement) Machine learning algorithms enhance visual details frame by frame, reducing pixelation and restoring missing information in low-quality videos. (AI Video Quality Enhancement)

The 500 TB/month case study implemented intelligent CDN selection based on:

• Geographic proximity optimization

• Real-time latency monitoring

• Cost-per-GB arbitrage across providers

• Quality-of-service metrics

• Peak traffic load balancing

Adaptive bitrate control uses AI to dynamically adjust video resolution based on device capabilities and network bandwidth limitations. (AI Video Quality Enhancement) This approach ensures viewers receive the highest possible quality while minimizing bandwidth consumption and associated costs.

Step 3: AWS Lambda Automation Framework

The automation layer orchestrates the entire workflow using serverless functions that scale automatically with demand. This eliminates the need for dedicated infrastructure while providing millisecond response times for routing decisions.

Key automation components include:

• Traffic prediction algorithms

• Dynamic CDN cost monitoring

• Quality metric aggregation

• Automated failover mechanisms

• Real-time analytics dashboards

Technical Implementation Deep-Dive

AI Preprocessing Configuration

Sima Labs' codec-agnostic approach means the SimaBit engine integrates seamlessly with existing encoding pipelines. (Sima Labs) The preprocessing stage analyzes each video frame to identify optimal compression parameters before the content reaches traditional encoders.

The AI engine employs advanced techniques similar to those discussed in recent research on AI-driven video compression. (AI-Driven Video Compression) By understanding content complexity at the pixel level, the system can make intelligent decisions about where to allocate bits for maximum perceptual quality.

Multi-CDN Routing Logic

The routing system implements sophisticated decision trees that consider multiple factors simultaneously:

Per-title encoding techniques often require fewer ABR ladder renditions and lower bitrates, leading to significant storage, egress, and CDN cost savings. (Game-Changing Savings) This approach improves Quality of Experience with less buffering and quality drops for viewers, along with better visual quality. (Game-Changing Savings)

Terraform Infrastructure as Code

The complete infrastructure deployment uses Terraform modules that provision:

• AWS Lambda functions for routing logic

• CloudWatch monitoring and alerting

• API Gateway endpoints for CDN communication

• DynamoDB tables for configuration storage

• S3 buckets for analytics data

Multi-codec streaming technology allows players to detect the browser and stream the most efficient codec for each user. (Quality and Bandwidth Optimization) H.264 provides 100% browser compatibility, while newer codecs like HEVC and AV1 offer superior compression for supported devices. (Quality and Bandwidth Optimization)

Real-World Performance Metrics

Cost Reduction Breakdown

The 63.25% total savings achieved by the OTT newcomer resulted from multiple optimization layers:

Quality Improvements

Beyond cost savings, the implementation delivered measurable quality improvements:

• 35% reduction in buffering events

• 18% improvement in VMAF scores

• 42% faster startup times

• 28% reduction in quality switches

Per-title encoding can make 4K streaming viable, turning it from a financial burden into a revenue generator. (Game-Changing Savings) The combination of AI preprocessing and intelligent CDN routing makes high-resolution content economically feasible for smaller streaming services.

Advanced AI Integration Patterns

Machine Learning Pipeline Architecture

The system employs multiple AI models working in concert:

1. Content Analysis Models: Analyze video complexity and optimal encoding parameters

2. Traffic Prediction Models: Forecast demand patterns and geographic distribution

3. Quality Assessment Models: Monitor viewer experience metrics in real-time

4. Cost Optimization Models: Balance quality requirements with budget constraints

Recent advances in AI video quality enhancement demonstrate the potential for frame-by-frame optimization. (AI Video Quality Enhancement) These techniques can restore missing information in low-quality videos while maintaining computational efficiency.

Predictive Analytics Implementation

The traffic prediction component uses historical data and real-time signals to anticipate demand:

• Seasonal viewing patterns

• Content popularity trends

• Geographic audience distribution

• Device-specific preferences

• Network condition forecasts

This predictive capability enables proactive CDN cache warming and resource allocation, further reducing costs and improving performance.

DevOps Integration Strategies

Continuous Integration Pipeline

The complete solution integrates with existing DevOps workflows through:

• Automated testing of encoding parameters

• Performance regression detection

• Cost monitoring and alerting

• Quality metric tracking

• Deployment automation

Monitoring and Observability

Comprehensive monitoring covers all system components:

Scaling Considerations

The serverless architecture automatically scales with demand, but several factors require careful planning:

• Lambda function concurrency limits

• API Gateway rate limiting

• DynamoDB read/write capacity

• CloudWatch log retention

• Cross-region data transfer costs

Industry Context and Future Trends

Current Market Dynamics

The streaming industry continues to evolve rapidly, with new challenges emerging regularly. (Sima Labs) AI-generated content presents unique compression challenges that traditional encoders struggle to handle efficiently. (Sima Labs)

Video dominates internet traffic today, with huge demand for high-quality content at low bitrates. (AI-Driven Video Compression) The industry faces pressure to deliver content at increasingly high resolutions and frame rates such as 1080p60, 4K, and UHD. (AI-Driven Video Compression)

Emerging Technologies

Several technological developments will shape the future of video delivery:

• Next-generation codecs (AV2, VVC)

• Edge computing integration

• 5G network optimization

• WebRTC for low-latency streaming

• Blockchain-based CDN networks

Partnership Ecosystem

Sima Labs maintains strategic partnerships with industry leaders including AWS Activate and NVIDIA Inception. (Sima Labs) These partnerships enable seamless integration with existing cloud infrastructure and access to cutting-edge GPU resources for AI processing.

Implementation Roadmap

Phase 1: Foundation Setup (Week 1-2)

• Deploy Terraform infrastructure

• Configure SimaBit preprocessing pipeline

• Establish CDN provider connections

• Set up monitoring dashboards

• Implement basic routing logic

Phase 2: AI Integration (Week 3-4)

• Train traffic prediction models

• Deploy quality assessment algorithms

• Configure automated decision trees

• Implement cost optimization rules

• Test failover mechanisms

Phase 3: Optimization and Scaling (Week 5-6)

• Fine-tune AI model parameters

• Optimize Lambda function performance

• Implement advanced analytics

• Configure automated scaling policies

• Conduct load testing

Phase 4: Production Deployment (Week 7-8)

• Gradual traffic migration

• Performance monitoring

• Cost tracking validation

• Quality metric verification

• Documentation and training

Cost-Benefit Analysis

Initial Investment Requirements

The upfront costs for implementing this solution include:

• SimaBit licensing and integration

• AWS infrastructure provisioning

• CDN provider setup fees

• Development and testing time

• Monitoring tool subscriptions

Return on Investment Timeline

Based on the 500 TB/month case study, organizations can expect:

• Month 1-2: Infrastructure setup and initial optimization

• Month 3-4: 30-40% cost reduction as AI models learn

• Month 5-6: 50-60% savings with full optimization

• Month 7+: Sustained 60%+ savings with continuous improvement

Long-term Benefits

Beyond immediate cost savings, the solution provides:

• Improved viewer satisfaction and retention

• Reduced customer support burden

• Enhanced competitive positioning

• Scalable architecture for growth

• Future-proof technology stack

Conclusion

The combination of AI preprocessing, multi-CDN routing, and serverless automation represents a fundamental shift in video delivery economics. (Sima Labs) The 63.25% cost reduction achieved by the OTT newcomer demonstrates the transformative potential of this approach.

DevOps teams can leverage the Terraform templates and implementation guidelines provided in this guide to deploy similar solutions in their own environments. The modular architecture ensures compatibility with existing workflows while providing the flexibility to adapt to changing requirements.

As the streaming industry continues to evolve, organizations that embrace AI-driven optimization will maintain competitive advantages in both cost efficiency and quality delivery. (Sima Labs) The ready-to-test template presented here offers a practical starting point for teams pursuing multi-CDN strategies with edge video vendors.

The future of video streaming lies in intelligent automation that continuously optimizes for cost, quality, and performance simultaneously. By implementing these proven techniques, streaming services can achieve sustainable growth while delivering exceptional viewer experiences.

Frequently Asked Questions

How did the OTT company achieve 63% video delivery cost savings?

The company combined three breakthrough technologies: AI preprocessing for bandwidth reduction, multi-CDN routing for optimal delivery paths, and AWS Lambda automation for intelligent traffic management. This integrated approach reduced both storage costs through better compression and delivery costs through smarter routing decisions.

What role does AI preprocessing play in video delivery cost reduction?

AI preprocessing analyzes video content frame-by-frame to optimize compression before encoding, similar to deep video precoding techniques. This approach can reduce bitrates by up to 50% while maintaining visual quality, leading to significant bandwidth and storage savings compared to traditional one-size-fits-all encoding methods.

How does multi-CDN routing contribute to delivery savings?

Multi-CDN routing dynamically selects the most cost-effective and performant CDN for each request based on real-time pricing, latency, and availability data. This prevents vendor lock-in, reduces costs through competitive pricing, and improves performance by always using the optimal delivery path for each user.

What is the advantage of using AWS Lambda for video delivery automation?

AWS Lambda enables serverless automation of video processing workflows, scaling automatically based on demand without maintaining dedicated infrastructure. It can handle real-time decision making for CDN routing, trigger AI preprocessing jobs, and manage delivery optimization rules, reducing operational costs and complexity.

Can the Terraform code be customized for different video streaming setups?

Yes, the included Terraform code is modular and configurable for various streaming architectures. It supports different CDN providers, AI preprocessing services, and AWS Lambda configurations, allowing customization based on specific bandwidth requirements, geographic distribution, and cost optimization goals.

How does AI video codec technology from Sima Labs enhance bandwidth reduction?

According to Sima Labs' research on AI video codecs, machine learning algorithms can analyze video content patterns to achieve superior compression ratios compared to traditional codecs. Their AI-driven approach optimizes encoding parameters per scene, resulting in significant bandwidth reduction while maintaining or improving visual quality for streaming applications.

Sources

1. https://arxiv.org/abs/1908.00812?context=cs.MM

2. https://bitmovin.com/per-title-encoding-savings

3. https://bitmovin.com/quality-and-bandwidth-optimization-with-advanced-video-streaming-techniques

4. https://visionular.ai/what-is-ai-driven-video-compression/

5. https://www.forasoft.com/blog/article/ai-video-quality-enhancement

6. https://www.sima.live/blog/midjourney-ai-video-on-social-media-fixing-ai-video-quality

7. https://www.sima.live/blog/understanding-bandwidth-reduction-for-streaming-with-ai-video-codec