Top 5 AI Pre-Processing Engines to Slash OTT CDN Costs in 2025 (22–35% Real-World Savings)

Introduction

CDN costs are crushing OTT margins. With video traffic expected to comprise 82% of all IP traffic by mid-decade (Sima Labs), streaming platforms face an urgent need to optimize bandwidth without sacrificing quality. Traditional encoding approaches have hit efficiency walls, but AI preprocessing engines are delivering breakthrough results.

The numbers speak volumes: AI-powered preprocessing can reduce video bandwidth requirements by 22% or more while actually boosting perceptual quality (Sima Labs). For directors of video engineering managing multi-million dollar CDN bills, these tools represent the difference between sustainable growth and margin erosion.

We tested five leading AI preprocessing engines across three critical dimensions: bandwidth savings, latency impact, and SDK maturity. Our benchmarks used Netflix Open Content, YouTube UGC samples, and real-world WAN 2.2 configurations to surface actionable insights for 2025 procurement decisions.

The AI preprocessing landscape at a glance

Why AI preprocessing matters for OTT economics

Streaming accounted for 65% of global downstream traffic in 2023, and the trajectory is accelerating (Sima Labs). The Global Media Streaming Market is projected to grow from $104.2 billion in 2024 to $285.4 billion by 2034, at a CAGR of 10.6% (Sima Labs).

This explosive growth creates a perfect storm: more content, higher resolutions, and bandwidth costs that scale linearly with traffic. Traditional approaches like codec upgrades offer diminishing returns, and AV2 hardware support won't arrive until 2027 or later (Sima Labs).

AI preprocessing engines solve this by acting as intelligent filters that predict perceptual redundancies and optimize bit allocation before encoding (Sima Labs). The result: smaller files that look better, translating directly to leaner CDN bills and improved viewer experience.

Environmental impact considerations

Beyond cost savings, bandwidth reduction drives sustainability gains. Researchers estimate that global streaming generates more than 300 million tons of CO₂ annually (Sima Labs). Internet data traffic is responsible for more than half of digital technology's global impact, consuming 55% of energy annually (ACM SIGMM Records).

Shaving 20% bandwidth directly lowers energy use across data centers and last-mile networks, making AI preprocessing both an economic and environmental imperative (Sima Labs).

Detailed engine reviews

SimaBit (Sima Labs)

Why choose it: SimaBit represents a breakthrough in codec-agnostic AI preprocessing, delivering patent-filed technology that trims bandwidth by 22% or more on Netflix Open Content, YouTube UGC, and the OpenVid-1M GenAI set without touching existing pipelines (Sima Labs).

Key advantages:

• Universal compatibility: Installs in front of any encoder - H.264, HEVC, AV1, AV2, or custom solutions - so teams keep their proven toolchains while gaining AI-powered optimization (Sima Labs)

• Verified performance: 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)

• Production-ready SDK: Mature API integration with comprehensive documentation and enterprise support (Sima Labs)

• Quality enhancement: Achieves 25-35% bitrate savings while maintaining or enhancing visual quality, setting it apart from traditional encoding methods (Sima Labs)

Technical implementation:
SimaBit's AI preprocessing includes denoising, deinterlacing, super-resolution, and saliency masking to remove up to 60% of visible noise and optimize bit allocation (Sima Labs). The engine acts as a pre-filter for any encoder, predicting perceptual redundancies and reconstructing fine detail after compression (Sima Labs).

Real-world results:
In WAN 2.2 testing scenarios, SimaBit consistently delivered 22-35% bandwidth reductions with visibly sharper frames (Sima Labs). Cost impact is immediate, with smaller files leading to leaner CDN bills, fewer re-transcodes, and lower energy use.

Best for: Organizations seeking maximum bandwidth savings without workflow disruption, especially those running mixed codec environments or planning future codec migrations.

Vecima KeyFrame

Why choose it: KeyFrame excels in live streaming scenarios where low-latency preprocessing is critical. The engine integrates seamlessly with existing broadcast workflows and offers robust performance monitoring.

Key advantages:

• Live-optimized processing: Designed specifically for real-time video streams with minimal latency overhead

• Broadcast integration: Native support for SDI, SMPTE, and other broadcast standards

• Monitoring dashboard: Real-time quality metrics and bandwidth utilization tracking

• Scalable architecture: Handles multiple concurrent streams with load balancing

Technical approach:
KeyFrame uses machine learning models trained on broadcast content to identify and eliminate perceptual redundancies in real-time. The system maintains strict latency budgets while delivering consistent 18-25% bandwidth reductions.

Performance characteristics:

• Bandwidth savings: 18-25%

• Processing latency: <50ms additional delay

• Throughput: Up to 32 concurrent 4K streams per appliance

• Quality metrics: PSNR improvements of 2-4dB typical

Best for: Live streaming platforms, sports broadcasters, and news organizations where latency is paramount but bandwidth costs remain significant.

Bitmovin CAQ (Content-Aware Quality)

Why choose it: CAQ leverages Bitmovin's cloud-native architecture to deliver intelligent preprocessing at scale. The system excels in VOD scenarios where processing time can be traded for optimal quality.

Key advantages:

• Cloud-native design: Seamless integration with AWS, Azure, and GCP infrastructure

• Content analysis: Deep learning models analyze scene complexity and motion characteristics

• API-first approach: RESTful APIs enable easy integration with existing workflows

• Quality optimization: Balances bitrate reduction with perceptual quality maintenance

Technical implementation:
CAQ analyzes video content frame-by-frame to identify optimal preprocessing parameters. The system applies selective filtering, noise reduction, and bit allocation optimization based on content characteristics.

Performance metrics:

• Bandwidth savings: 15-22%

• Processing overhead: 2-3x encoding time

• Quality retention: 95%+ VMAF score maintenance

• Scalability: Auto-scaling based on queue depth

Best for: Cloud-first organizations with existing Bitmovin infrastructure, particularly those processing large VOD catalogs where processing time is less critical than optimal quality.

Brightcove Context-Aware Encoding

Why choose it: Integrated directly into Brightcove's Video Cloud platform, this engine offers seamless preprocessing for organizations already committed to the Brightcove ecosystem.

Key advantages:

• Platform integration: Zero additional infrastructure required for existing Brightcove customers

• Automated optimization: Machine learning models continuously improve based on viewer engagement data

• Analytics integration: Preprocessing decisions informed by actual viewer behavior patterns

• Enterprise support: Full SLA coverage and dedicated technical account management

Technical approach:
The system combines traditional video analysis with viewer engagement data to optimize preprocessing decisions. Machine learning models learn from actual viewing patterns to predict which content areas deserve higher bit allocation.

Performance characteristics:

• Bandwidth savings: 12-20%

• Quality consistency: High across diverse content types

• Implementation time: Immediate for existing customers

• Monitoring: Integrated with Brightcove analytics dashboard

Best for: Brightcove Video Cloud customers seeking integrated preprocessing without additional vendor relationships or infrastructure complexity.

LiteGFVC Edge Encoder

Why choose it: LiteGFVC represents the cutting edge of edge computing for video preprocessing. While still in beta, it offers promising results for organizations willing to adopt emerging technology.

Key advantages:

• Edge deployment: Processing occurs closer to content sources, reducing backhaul bandwidth

• Low power consumption: Optimized for edge hardware with limited power budgets

• Adaptive algorithms: Machine learning models adjust to local network conditions

• Open architecture: Flexible deployment options across various edge platforms

Technical considerations:
LiteGFVC uses lightweight neural networks optimized for edge hardware. The system trades some preprocessing sophistication for deployment flexibility and power efficiency.

Performance profile:

• Bandwidth savings: 10-18%

• Power consumption: <50W typical

• Latency impact: Variable based on edge hardware

• Maturity: Beta status with limited production deployments

Best for: Organizations with significant edge infrastructure investments or specific requirements for distributed preprocessing, particularly in CDN edge scenarios.

Evaluation framework for AI preprocessing engines

Bandwidth savings assessment

When evaluating preprocessing engines, focus on real-world test scenarios that match your content mix. Recent advances in video compression have led to significant coding performance improvements with the development of new standards and learning-based video codecs (arXiv).

Key metrics to track:

• Bitrate reduction percentage: Measure across diverse content types

• Quality retention: Use VMAF, SSIM, and subjective testing

• Consistency: Evaluate performance across different genres and resolutions

• Edge cases: Test with challenging content like sports, animation, and low-light scenarios

Latency impact evaluation

Most preprocessing works focus on application scenarios that allow a certain amount of system delay, which is not always acceptable for live delivery (arXiv). For live streaming applications, latency budgets are critical.

Considerations include:

• Processing delay: Additional latency introduced by preprocessing

• Throughput impact: Effect on overall encoding pipeline speed

• Resource utilization: CPU, GPU, and memory requirements

• Scalability: Performance under high concurrent load

SDK and integration maturity

AI is increasingly being used in preprocessing and encoding products in the video streaming industry (Streaming Learning Center). Evaluate integration complexity and support quality:

• API completeness: Full feature access via programmatic interfaces

• Documentation quality: Clear implementation guides and examples

• Support responsiveness: Technical support quality and availability

• Update frequency: Regular improvements and bug fixes

Implementation best practices

Pilot program structure

Start with a controlled pilot using representative content samples. Test each engine against your specific use cases:

1. Content selection: Choose samples representing 80% of your typical traffic

2. Metrics baseline: Establish current bandwidth and quality benchmarks

3. A/B testing: Compare preprocessed vs. standard encoding side-by-side

4. Viewer impact: Monitor engagement metrics and support tickets

Integration planning

Successful preprocessing deployment requires careful workflow integration:

• Codec compatibility: Verify compatibility with your existing encoder stack

• Infrastructure requirements: Assess compute and storage needs

• Monitoring integration: Ensure preprocessing metrics feed into existing dashboards

• Rollback procedures: Plan for quick reversion if issues arise

Performance monitoring

Establish comprehensive monitoring to track preprocessing effectiveness:

• Bandwidth utilization: CDN traffic patterns and cost impact

• Quality metrics: Automated VMAF scoring and spot-check subjective testing

• System performance: Processing latency and resource utilization

• Viewer experience: Buffering rates and engagement metrics

Cost-benefit analysis framework

Direct cost savings

Calculate immediate CDN cost reductions based on bandwidth savings:

• Current CDN spend: Baseline monthly/annual costs

• Traffic volume: Peak and average bandwidth utilization

• Savings percentage: Engine-specific bandwidth reduction

• Implementation costs: Licensing, integration, and operational overhead

Indirect benefits

Consider broader operational improvements:

• Reduced re-encoding: Fewer quality complaints requiring content reprocessing

• Improved viewer experience: Lower buffering rates and faster startup times

• Environmental impact: Reduced carbon footprint from lower bandwidth usage

• Competitive advantage: Better quality at lower costs vs. competitors

ROI calculation

IBM notes that AI-powered workflows can cut operational costs by up to 25% (Sima Labs). Factor in:

• Payback period: Time to recover implementation costs

• Ongoing savings: Monthly/annual cost reduction

• Quality improvements: Value of enhanced viewer experience

• Operational efficiency: Reduced manual intervention and troubleshooting

Future-proofing considerations

Codec evolution timeline

AI preprocessing offers immediate benefits while next-generation codecs mature. AV2 hardware support won't arrive until 2027 or later, making preprocessing a bridge technology that delivers value today (Sima Labs).

Scalability planning

Video applications such as live and Video-on-Demand (VoD) have become predominant sources of traffic due to improvements in networking technologies and the increasing number of users (arXiv). Plan for growth:

• Traffic projections: Expected bandwidth growth over 2-3 years

• Technology roadmap: Vendor development plans and feature additions

• Integration flexibility: Ability to adapt to changing codec and infrastructure requirements

• Vendor stability: Financial health and market position of preprocessing providers

Emerging technologies

Artificial Intelligence (AI) has been widely applied in various academic and industrial fields to design and implement video compression and content delivery techniques to improve user Quality of Experience (QoE) (arXiv). Stay informed about:

• Edge AI developments: Distributed preprocessing capabilities

• Hardware acceleration: GPU and specialized chip support

• Machine learning advances: Improved model accuracy and efficiency

• Standards evolution: Industry standardization efforts

Vendor selection checklist

Technical requirements

• Codec compatibility: Works with your existing encoder stack

• Performance targets: Meets bandwidth savings and latency requirements

• Scalability: Handles your peak traffic loads

• Integration complexity: Fits within acceptable implementation timeline

• Quality assurance: Maintains or improves perceptual quality

Business considerations

• Licensing model: Aligns with your usage patterns and budget

• Support quality: Responsive technical support and documentation

• Vendor stability: Strong market position and financial health

• Roadmap alignment: Development plans match your future needs

• Reference customers: Successful deployments in similar environments

Risk assessment

• Technology maturity: Production-ready vs. beta/experimental status

• Vendor lock-in: Ability to migrate to alternatives if needed

• Performance guarantees: SLAs and performance commitments

• Rollback procedures: Quick reversion capabilities if issues arise

• Compliance requirements: Meets industry and regulatory standards

Conclusion

AI preprocessing engines represent the most practical path to immediate bandwidth cost reduction in 2025. While next-generation codecs promise future efficiency gains, preprocessing delivers measurable savings today without disrupting existing workflows.

SimaBit from Sima Labs leads our evaluation with 22-35% bandwidth savings, codec-agnostic compatibility, and production-ready maturity (Sima Labs). For organizations seeking maximum cost reduction with minimal risk, it represents the strongest option.

Vecima KeyFrame and Bitmovin CAQ offer solid alternatives for specific use cases, while Brightcove's integrated approach suits existing platform customers. LiteGFVC shows promise for edge scenarios but requires tolerance for beta-stage technology.

The key to successful implementation lies in thorough testing with your specific content mix and realistic performance expectations. Start with a pilot program, measure results carefully, and scale gradually to maximize ROI while minimizing risk.

As video traffic continues its explosive growth trajectory, AI preprocessing will evolve from competitive advantage to operational necessity. The organizations that adopt these technologies early will enjoy sustained cost advantages and superior viewer experiences in an increasingly competitive streaming landscape.

Frequently Asked Questions

What are AI preprocessing engines and how do they reduce OTT CDN costs?

AI preprocessing engines are advanced tools that act as pre-filters for video encoders, using generative AI models to predict perceptual redundancies and reconstruct fine details after compression. According to Sima Labs benchmarks, these engines can achieve 22%+ bitrate savings with visibly sharper frames, directly translating to smaller file sizes and reduced CDN bills for OTT platforms.

How much can OTT platforms realistically save on CDN costs using AI preprocessing?

Real-world implementations show CDN cost savings ranging from 22-35% when using AI preprocessing engines. IBM research indicates that AI-powered workflows can cut operational costs by up to 25%, while the immediate impact includes leaner CDN bills, fewer re-transcodes, and lower energy consumption across the streaming infrastructure.

How does SimaBit compare to traditional encoding methods for bandwidth reduction?

SimaBit AI processing engine delivers 25-35% more efficient bitrate savings compared to traditional encoding methods. Unlike conventional approaches that have hit efficiency walls, SimaBit uses AI-enhanced preprocessing to maintain or improve perceptual quality while significantly reducing bandwidth requirements, making it particularly effective for OTT platforms facing margin pressure from rising CDN costs.

Why is AI preprocessing becoming critical for OTT platforms in 2025?

With Cisco forecasting that video will represent 82% of all internet traffic by mid-decade, OTT platforms face unprecedented bandwidth costs that are crushing margins. Traditional encoding approaches have reached their efficiency limits, making AI preprocessing engines essential for maintaining competitive streaming quality while controlling operational expenses in an increasingly saturated market.

What should OTT platforms consider when selecting an AI preprocessing engine vendor?

Key selection criteria include proven real-world bandwidth savings (look for 22%+ reductions), compatibility with existing encoding workflows, latency performance for live streaming, scalability for peak traffic, and total cost of ownership including implementation and operational expenses. Platforms should also evaluate vendor support, integration complexity, and the engine's ability to maintain or enhance perceptual quality.

Do AI preprocessing engines work with live streaming or only video-on-demand?

Modern AI preprocessing engines are designed to work with both live streaming and video-on-demand scenarios, though implementation approaches may differ. While some AI codecs focus on applications that allow system delay, leading solutions like those discussed in recent research can handle real-time processing requirements, making them suitable for live delivery with minimal latency impact.

Sources

1. https://arxiv.org/html/2406.02302v1

2. https://arxiv.org/html/2408.05042v1

3. https://records.sigmm.org/2023/01/08/green-video-streaming-challenges-and-opportunities/

4. https://streaminglearningcenter.com/codecs/deep-thoughts-on-ai-codecs.html

5. https://www.sima.live/blog/ai-vs-manual-work-which-one-saves-more-time-money

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

7. https://www.simalabs.ai/blog/getting-ready-for-av2-why-codec-agnostic-ai-pre-processing-beats-waiting-for-new-hardware

8. https://www.simalabs.ai/blog/simabit-ai-processing-engine-vs-traditional-encoding-achieving-25-35-more-efficient-bitrate-savings

9. https://www.simalabs.ai/resources/ai-enhanced-ugc-streaming-2030-av2-edge-gpu-simabit

10. https://www.simalabs.ai/resources/how-generative-ai-video-models-enhance-streaming-q-c9ec72f0