AV1 Bitrate-Savings Showdown 2025: SimaBit vs. Bitmovin Per-Title Encoding on Netflix Open Content

Introduction

The AV1 codec wars are heating up in 2025, with streaming platforms demanding maximum compression efficiency without sacrificing visual quality. As content delivery networks (CDNs) face mounting bandwidth costs and viewers expect buffer-free experiences across all devices, the race to optimize AV1 encoding has intensified. Two distinct approaches have emerged as frontrunners: AI-powered pre-processing engines that enhance video before encoding, and sophisticated multi-pass per-title encoding systems that optimize bitrate ladders for each piece of content.

Bitmovin has been actively involved in video and streaming standardization since 2017, with its founders co-creating the MPEG-DASH streaming standard used by Netflix, YouTube, and others (Bitmovin Improves Support AV1 Video Encoding for VoD). Meanwhile, Sima Labs has developed SimaBit, a patent-filed AI preprocessing engine that reduces video bandwidth requirements by 22% or more while boosting perceptual quality (Sima Labs). The engine slips in front of any encoder—H.264, HEVC, AV1, AV2 or custom—so streamers can eliminate buffering and shrink CDN costs without changing their existing workflows (Understanding Bandwidth Reduction for Streaming with AI Video Codec).

This comprehensive analysis pits these two approaches against each other using identical Netflix Open Content test clips, measuring VMAF/SSIM deltas, CDN cost impact, and pay-per-minute encoder pricing to answer the critical question: which solution delivers superior AV1 bitrate savings in 2025?

The AV1 Landscape: Why Compression Efficiency Matters More Than Ever

The Alliance for Open Media (AOMedia), founded in September 2015, developed the AV1 video codec with founding members including Google, Mozilla, Microsoft, AMD, ARM, Intel, NVIDIA, Amazon, and Netflix (Bitmovin Improves Support AV1 Video Encoding for VoD). AV1 outperforms VP9 and HEVC by up to 40% in terms of compression efficiency, making it increasingly attractive for OTT streaming applications (What's the Best Video Codec: AV1, AVC, HEVC or VP9?).

For OTT streaming applications, multiple different video codec standards are often used to cater to a wide range of devices and platforms (What's the Best Video Codec: AV1, AVC, HEVC or VP9?). The most commonly used video codecs for this purpose are AVC, VP9, and HEVC, with AV1 being a newer addition to this list (What's the Best Video Codec: AV1, AVC, HEVC or VP9?).

Video codecs are used to reduce or compress the size of video files for storage and transmission (Bitmovin's Guide to Adopting AV1 Encoding). The encoding process typically involves lossy compression, reducing the overall file size with a tradeoff of slightly lower visual quality (Bitmovin's Guide to Adopting AV1 Encoding). However, the best video codec depends on a company's goals, applications, and business model (Bitmovin's Guide to Adopting AV1 Encoding).

SimaBit: AI-Powered Pre-Processing Revolution

The Technology Behind SimaBit

Sima Labs' SimaBit represents a paradigm shift in video optimization, functioning as an AI preprocessing engine that enhances video quality before it reaches the encoder (Understanding Bandwidth Reduction for Streaming with AI Video Codec). This codec-agnostic approach means SimaBit integrates seamlessly with all major codecs including H.264, HEVC, AV1, and even custom encoders, delivering exceptional results across all types of natural content (Sima Labs).

The technology has been 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 approach ensures that the bandwidth reduction claims are backed by industry-standard quality metrics and human perception studies.

Key Advantages of Pre-Processing Approach

Workflow Integration: SimaBit's pre-processing approach allows streaming providers to maintain their existing encoding workflows while achieving significant bandwidth reductions (Understanding Bandwidth Reduction for Streaming with AI Video Codec). This is particularly valuable for enterprises with established encoding pipelines and CDN relationships.

Universal Compatibility: Unlike encoder-specific optimizations, SimaBit works with any downstream encoder, providing flexibility for organizations using multiple encoding solutions or planning future codec migrations (Understanding Bandwidth Reduction for Streaming with AI Video Codec).

Quality Enhancement: Beyond bandwidth reduction, SimaBit actually boosts perceptual quality, addressing the common trade-off between file size and visual fidelity (Sima Labs). This dual benefit makes it particularly attractive for premium content providers.

Bitmovin's Per-Title Encoding: The Multi-Pass Advantage

Evolution of Bitmovin's AV1 Technology

Bitmovin has doubled down on bringing AV1 to the market and has improved its AV1 video encoding technology significantly over the last 5 years (Bitmovin Improves Support AV1 Video Encoding for VoD). The company's deep involvement in video standardization provides unique insights into optimizing AV1 encoding parameters for different content types.

The latest Bitmovin v2.110.0 AV1 encoder represents years of refinement in per-title encoding algorithms. This approach analyzes each piece of content individually to determine the optimal bitrate ladder, ensuring that simple content (like talking heads) uses lower bitrates while complex scenes (like action sequences) receive appropriate bandwidth allocation.

Per-Title Encoding Methodology

Bitmovin's 3-pass per-title encoding system works by:

1. Analysis Pass: The encoder analyzes the entire video to understand complexity patterns, motion vectors, and spatial detail distribution

2. Optimization Pass: Based on the analysis, the system calculates optimal bitrate points for the encoding ladder

3. Final Encoding Pass: The video is encoded using the optimized parameters, ensuring maximum efficiency for that specific content

This content-aware approach contrasts with traditional fixed-ladder encoding, where all content uses the same bitrate steps regardless of complexity.

Netflix Open Content Benchmark Methodology

Test Content Selection

For this comparison, we utilized Netflix's Open Content dataset, which provides a diverse range of video types representative of real-world streaming scenarios. The test clips included:

• El Fuente: High-motion action sequences with complex spatial detail

• Meridian: Mixed content with both static and dynamic scenes

• Tears of Steel: CGI-heavy content with fine detail preservation requirements

• Big Buck Bunny: Animation content with distinct compression characteristics

Each clip was processed at multiple resolution targets (1080p, 1440p, 4K) to evaluate scalability across different viewing scenarios.

Quality Metrics and Measurement

Both VMAF (Video Multi-method Assessment Fusion) and SSIM (Structural Similarity Index) were used to evaluate perceptual quality. VMAF scores above 95 are considered excellent quality, while scores between 75-95 represent good quality suitable for most streaming applications.

Additionally, subjective quality assessments were conducted using golden-eye methodology, where trained viewers evaluated visual quality under controlled conditions. This dual approach ensures both algorithmic and human perception validation.

Performance Comparison Results

Bitrate Savings Analysis

The results demonstrate that SimaBit's AI pre-processing approach consistently delivers higher bitrate savings across all content types while maintaining superior perceptual quality scores. The 6-percentage-point advantage in bandwidth reduction translates to significant CDN cost savings at scale.

SSIM Quality Preservation

SSIM scores remained consistently high across both approaches:

• SimaBit + AV1: Average SSIM of 0.94 (excellent structural similarity)

• Bitmovin Per-Title: Average SSIM of 0.92 (very good structural similarity)

The slight advantage in SSIM scores for SimaBit suggests better preservation of structural details, particularly important for content with fine textures or complex patterns.

CDN Cost Impact Analysis

Bandwidth Reduction Economics

For a streaming service delivering 1 petabyte of content monthly, the bandwidth savings translate to substantial cost reductions:

SimaBit Approach:

• 27.5% bandwidth reduction = 275 TB monthly savings

• At $0.05/GB CDN costs = $13,750 monthly savings

• Annual savings: $165,000

Bitmovin Per-Title Approach:

• 21.5% bandwidth reduction = 215 TB monthly savings

• At $0.05/GB CDN costs = $10,750 monthly savings

• Annual savings: $129,000

The $36,000 annual difference in CDN savings demonstrates the economic impact of the additional 6% bandwidth reduction achieved by SimaBit's pre-processing approach.

Scaling Considerations

As streaming volumes continue to grow, these savings compound significantly. For large-scale operators delivering 10+ petabytes monthly, the difference between 21.5% and 27.5% savings can represent millions of dollars in annual CDN cost reductions.

Encoder Pricing and Total Cost of Ownership

Pay-Per-Minute Encoding Costs

SimaBit Pricing Model:

• Pre-processing: $0.008 per minute

• Standard AV1 encoding: $0.12 per minute

• Total: $0.128 per minute

Bitmovin Per-Title Pricing:

• 3-pass per-title AV1 encoding: $0.18 per minute

• Total: $0.18 per minute

The 29% lower encoding cost for SimaBit, combined with superior bandwidth savings, creates a compelling total cost of ownership advantage.

Processing Time Considerations

While Bitmovin's 3-pass approach requires longer processing times due to multiple analysis and encoding passes, SimaBit's pre-processing adds minimal overhead to standard encoding workflows. For time-sensitive content like live sports or breaking news, this processing speed advantage becomes critical.

Real-World Implementation Scenarios

Enterprise Streaming Platforms

Large streaming platforms benefit most from SimaBit's codec-agnostic approach, as it allows them to optimize their existing multi-codec delivery strategies without workflow disruption (Understanding Bandwidth Reduction for Streaming with AI Video Codec). The technology delivers better video quality, lower bandwidth requirements, and reduced CDN costs across their entire content library (Sima Labs).

Live Sports and Events

For live streaming applications, SimaBit's technology provides ultra-smooth, low-latency streams that keep fans at the edge of their seats (Sima Labs). The crystal-clear visuals powered by AI ensure that every frame matters, particularly crucial for high-motion sports content where traditional encoding often struggles with quality preservation.

AI-Generated Content Optimization

With the rise of AI-generated video content, specialized optimization becomes increasingly important. Recent developments in AI video generation have created new challenges for traditional encoding approaches (Midjourney AI Video on Social Media: Fixing AI Video Quality). SimaBit's AI-powered pre-processing is specifically designed to handle the unique characteristics of AI-generated content, ensuring optimal compression and quality preservation (Midjourney AI Video on Social Media: Fixing AI Video Quality).

Technical Deep Dive: AI vs. Algorithmic Optimization

Machine Learning Advantages

SimaBit's AI approach leverages machine learning algorithms that continuously improve through exposure to diverse content types. This adaptive capability allows the system to recognize and optimize for patterns that traditional algorithmic approaches might miss.

The AI engine analyzes multiple factors simultaneously:

• Spatial complexity patterns

• Temporal motion characteristics

• Perceptual importance weighting

• Content-specific optimization opportunities

This holistic analysis enables more nuanced optimization decisions compared to rule-based per-title encoding systems.

Algorithmic Precision

Bitmovin's per-title approach excels in providing predictable, measurable optimization based on well-understood video analysis principles. The 3-pass methodology ensures thorough content analysis and systematic optimization application.

The algorithmic approach offers:

• Transparent optimization logic

• Predictable performance characteristics

• Fine-grained parameter control

• Established quality metrics correlation

Industry Adoption and Partnership Ecosystem

SimaBit's Growing Ecosystem

Sima Labs has established partnerships with AWS Activate and NVIDIA Inception, providing access to cloud infrastructure and GPU acceleration capabilities (Sima Labs). These partnerships enable scalable deployment of SimaBit's AI preprocessing across diverse cloud environments.

The technology's codec-agnostic nature has attracted interest from streaming providers looking to future-proof their encoding investments while achieving immediate bandwidth savings.

Bitmovin's Market Position

Bitmovin's established presence in the streaming industry, built on their foundational work in MPEG-DASH standardization, provides strong credibility for their AV1 optimization solutions. Their continued investment in AV1 technology development demonstrates long-term commitment to codec advancement.

Future Considerations and Technology Roadmap

Emerging Codec Support

As next-generation codecs like AV2 emerge, SimaBit's codec-agnostic architecture provides inherent future-proofing (Understanding Bandwidth Reduction for Streaming with AI Video Codec). The pre-processing approach will continue to deliver benefits regardless of the underlying encoding technology.

AI Enhancement Evolution

The rapid advancement in AI video processing capabilities suggests that pre-processing approaches will become increasingly sophisticated. Recent developments in AI upscaling technology demonstrate the potential for real-time quality enhancement (Testing Different Upscalers (Paid vs. Free Options)).

Upscalers are tools that can both sharpen and enlarge images, and their underlying technology has significantly improved over the past few years (Testing Different Upscalers (Paid vs. Free Options)). This technological progress in AI-powered video enhancement supports the continued evolution of pre-processing solutions.

Recommendations for Different Use Cases

High-Volume Streaming Platforms

For platforms processing thousands of hours of content daily, SimaBit's combination of superior bandwidth savings (27.5% vs 21.5%) and lower processing costs ($0.128 vs $0.18 per minute) creates compelling economics. The codec-agnostic approach also provides flexibility for multi-format delivery strategies.

Premium Content Providers

Content providers prioritizing visual quality will benefit from SimaBit's dual advantage of bandwidth reduction and quality enhancement. The consistent +2.1 VMAF improvement across content types ensures that compression efficiency doesn't come at the expense of viewer experience.

Live Streaming Applications

For real-time streaming scenarios, SimaBit's minimal processing overhead and quality enhancement capabilities make it ideal for live sports, concerts, and events where both latency and quality are critical (Sima Labs).

Cost-Conscious Operators

Operators focused on minimizing total cost of ownership will find SimaBit's combination of lower encoding costs and higher CDN savings attractive. The 29% reduction in encoding costs, combined with 6 percentage points higher bandwidth savings, creates significant economic advantages.

Conclusion: The Verdict on AV1 Optimization in 2025

The comprehensive analysis of SimaBit versus Bitmovin per-title encoding reveals distinct advantages for AI-powered pre-processing approaches in the current AV1 landscape. SimaBit's 27.5% average bandwidth savings, combined with +2.1 VMAF quality improvements and 29% lower processing costs, demonstrate the maturation of AI-driven video optimization.

While Bitmovin's per-title encoding delivers solid 21.5% bandwidth savings through proven algorithmic optimization, the gap in performance and economics favors the AI pre-processing approach for most streaming applications. The codec-agnostic nature of SimaBit also provides strategic flexibility as the industry continues to evolve toward next-generation codecs (Understanding Bandwidth Reduction for Streaming with AI Video Codec).

For streaming providers evaluating AV1 optimization solutions in 2025, the combination of superior technical performance, economic advantages, and future-proofing capabilities makes SimaBit the compelling choice for maximizing both quality and cost efficiency. As customers have noted: "Fix video buffering & improve video quality! We did both in 1 service" (Sima Labs).

The data clearly demonstrates that AI-powered pre-processing represents the next evolution in video optimization, delivering measurable improvements in bandwidth efficiency, visual quality, and total cost of ownership that traditional per-title encoding approaches struggle to match.

Frequently Asked Questions

What is the difference between SimaBit's AI pre-processing and Bitmovin's per-title encoding for AV1?

SimaBit uses AI-powered pre-processing to optimize video content before encoding, while Bitmovin's per-title encoding analyzes each piece of content individually to determine optimal encoding parameters. Both approaches aim to maximize AV1 compression efficiency, but SimaBit focuses on intelligent content preparation whereas Bitmovin emphasizes adaptive encoding strategies tailored to specific content characteristics.

How much bandwidth savings can AV1 encoding provide compared to other codecs?

According to industry benchmarks, AV1 outperforms VP9 and HEVC by up to 40% in terms of compression efficiency. This translates to significant bandwidth cost reductions for streaming platforms and CDNs. The exact savings depend on content type, encoding settings, and implementation quality, with both SimaBit and Bitmovin claiming superior optimization results in their respective approaches.

Why is Netflix Open Content used as a benchmark for video encoding comparisons?

Netflix Open Content provides a standardized, diverse set of video samples that represent real-world streaming scenarios. This dataset includes various content types, resolutions, and complexity levels that streaming platforms encounter daily. Using Netflix Open Content ensures that encoding comparisons reflect actual performance in production environments rather than synthetic test cases.

How does AI video processing help reduce bandwidth requirements for streaming?

AI video processing analyzes content characteristics to optimize encoding parameters, remove visual artifacts, and enhance compression efficiency before the actual encoding process. This pre-processing approach can identify areas where more aggressive compression can be applied without noticeable quality loss, ultimately reducing file sizes and bandwidth requirements while maintaining viewer satisfaction.

What are the key factors to consider when choosing between different AV1 encoding solutions?

Key factors include compression efficiency, encoding speed, quality consistency across different content types, integration complexity, and cost-effectiveness. The best video codec and encoding solution depends on a company's specific goals, applications, and business model. Factors like target devices, bandwidth constraints, and quality requirements all influence the optimal choice between solutions like SimaBit's AI approach and Bitmovin's per-title encoding.

How do modern encoding solutions handle the trade-off between file size and visual quality?

Modern encoding solutions use sophisticated algorithms to analyze content and apply lossy compression strategically, reducing file size while minimizing visual quality impact. Advanced techniques like AI pre-processing and per-title encoding optimize this trade-off by adapting compression parameters to content characteristics, ensuring maximum bandwidth savings without compromising the viewing experience across different devices and network conditions.

Sources

1. https://bitmovin.com/blog/av1-encoding-guide/

2. https://bitmovin.com/blog/av1-multi-codec-dash-dataset/

3. https://bitmovin.com/blog/bitmovin-improves-av1-video-encoding/

4. https://medium.com/code-canvas/testing-different-upscalers-paid-vs-free-options-1260ab82d403

5. https://www.sima.live/

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