Common video content management mistakes

In 2020, Coursera saw a massive surge in video traffic as online learning exploded. Their initial video setup basic cloud storage and a CDN worked fine at first. But as millions of students streamed lectures, buffering complaints surged. Mobile users struggled with poor quality, and storage costs skyrocketed. Engineers spent weeks manually adjusting encoding settings, trying to optimize streams.

The mistake? Treating video like a static file. Unlike images, video requires adaptive encoding, optimized delivery, and real-time adjustments to work seamlessly. Without the right setup, every view adds hidden costs, and every glitch frustrates users.

Coursera eventually reworked their video pipeline, shifting to AI-driven encoding and multi-CDN delivery to keep up with demand. But most companies don’t have the resources to build custom solutions from scratch.

Here’s why video at scale breaks and how to fix it before it slows you down.

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Mistake #1: Treating video like a static file

A fitness streaming platform ran into major performance issues as their user base grew. Early on, their engineering team stored videos in Amazon S3 and relied on a standard CDN, assuming video files could be handled like images or PDFs. But as thousands of users streamed workouts simultaneously, buffering complaints spiked, costs soared, and engineering time was consumed by endless performance fixes.

The problem? Video isn’t static. Unlike an image that loads once, video needs adaptive encoding, optimized delivery, and efficient caching to work at scale. Every inefficiency whether in storage, retrieval, or playback multiplies as viewership increases.

For this platform, the consequences were immediate:

• Slow start times: Users waited too long for videos to load because every request pulled large files from cloud storage.
• Uncontrolled costs: Each high-resolution video was stored in expensive cloud tiers, inflating monthly bills.
• Poor mobile experience: Without adaptive bitrate streaming (ABR), mobile users on slow networks struggled with long buffering times instead of getting dynamically adjusted streams.
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How they fixed it

1. Smart Caching with Multi-CDN: Instead of serving videos directly from cloud storage, they implemented a multi-CDN strategy to cache videos at multiple edge locations. This cut video load times by 40% and reduced bandwidth costs by serving content closer to users.
2. Storage Tiering for Cost Control: They moved frequently accessed content to high-speed storage, while older or rarely viewed videos were automatically archived to lower-cost tiers. This cut storage costs by nearly 30% without affecting playback performance.
3. Metadata-Driven Optimization: Instead of treating all videos the same, they implemented scene-aware encoding, adjusting bitrate dynamically based on content complexity. Action-heavy workout sessions got more bandwidth, while static instructional videos used fewer resources ensuring optimal quality without wasting storage and bandwidth.

The result? Lower costs, fewer buffering complaints, and a seamless viewing experience all without adding engineering overhead.

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Mistake #2: Ignoring adaptive streaming

A live sports streaming platform faced a surge in user complaints some viewers struggled with buffering, while others were stuck with blurry, low-quality streams despite having high-speed internet. The platform had been delivering a single bitrate video to all users, assuming that one high-quality stream would be enough. But video consumption doesn’t work that way.
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Why one-size-fits-all streaming fails

Not all viewers have the same network speed, device capability, or bandwidth availability. A 4K stream might look great on fiber internet but fail completely on mobile data, causing frustration and stream drop-offs. Meanwhile, a low-bitrate stream might load instantly but ruin the experience for users on fast connections.

For this streaming platform, the problems were clear:

• Frequent buffering on slower connections: Users on mobile networks or congested Wi-Fi struggled to load high-bitrate streams.
• Poor video quality for high-speed users: Viewers with fast internet were stuck with low-resolution streams that didn’t adjust dynamically.
• Higher abandonment rates: Frustrated users either switched to another platform or gave up watching entirely.
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The solution

1. Implemented Adaptive Bitrate Streaming (ABR): Instead of delivering a single video file, they switched to HLS (HTTP Live Streaming) and DASH (Dynamic Adaptive Streaming over HTTP). This allowed the video player to automatically adjust resolution based on the viewer’s network conditions in real-time.
2. Generated Multiple Video Resolutions: Their encoding pipeline was automated to create multiple versions of every video, including 360p, 480p, 720p, 1080p, and 4K. This meant users on weak connections could get a lower-resolution stream without buffering, while those on fast internet got the highest possible quality.
3. Used a Streaming optimized Video Platform: Instead of relying on raw storage and basic CDN delivery, they switched to a video-first platform that supported multi-bitrate streaming out of the box. This reduced engineering overhead and ensured seamless playback across devices.

The result? Higher viewer retention, fewer complaints, and a premium streaming experience across all networks.

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Mistake #3: Underestimating video processing bottlenecks

A news streaming platform covering live events faced a critical issue breaking news videos took too long to process, delaying their ability to publish real-time updates. Their engineers had built a DIY cloud encoding pipeline, assuming standard compute instances could handle the workload. But as viewership spiked, encoding jobs piled up, CPU utilization maxed out, and video processing times stretched from minutes to hours.
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Why encoding bottlenecks kill performance

Raw video files are massive and must be compressed, optimized, and converted into multiple formats before they can be streamed efficiently. Without an optimized encoding pipeline, video platforms experience:

• Slow processing times: Videos take too long to go live, killing the relevance of time-sensitive content.
• High infrastructure costs: CPU-based encoding eats up expensive compute resources, making large-scale video processing unsustainable.
• Scaling issues for live streaming: Real-time events struggle with latency, leading to frustrating delays and degraded quality.

For this news platform, missing a story window due to slow encoding meant losing viewers to competitors who could publish in real time.
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The solution

1. Switched to GPU-Accelerated Encoding: Instead of relying solely on CPU instances, they integrated NVIDIA NVENC for hardware-accelerated encoding, reducing encoding time by over 60% while freeing up compute resources.
2. Parallelized Encoding Across Cloud Instances: Instead of processing videos one at a time, they distributed encoding jobs across multiple cloud instances using parallel processing. This meant large-scale video batches could be processed simultaneously, cutting down overall processing time.
3. Automated Transcoding with Optimized Profiles: They implemented pre-configured encoding profiles that automatically adjusted based on content type and target platform. This ensured each video was processed efficiently without wasting compute power on unnecessary bitrate settings.

The result? Real-time publishing, lower costs, and a seamless live streaming experience.
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Mistake #4: Poor metadata and searchability

A large media archive struggled to keep up with content retrieval as their video library grew to hundreds of thousands of hours. Journalists and researchers often spent hours manually searching for relevant footage, slowing down their workflows and delaying content production. The reason? Their platform lacked structured metadata—videos were stored with generic filenames, making search and categorization a nightmare.

Why bad metadata breaks video platforms

Without structured metadata, video content becomes a black box difficult to organize, search, and retrieve. This leads to:

• Wasted time and inefficiency – Editors and engineers manually label videos or dig through archives to find relevant clips.
• Increased storage costs – Without proper indexing, duplicate or unnecessary videos pile up, wasting resources.
• Poor user experience – Viewers struggle to find relevant content, reducing engagement and retention.

For this media company, the lack of automated tagging and structured indexing meant their massive video archive was effectively unusable at scale.

How they fixed it

1. Automated Metadata Extraction – Instead of relying on manual tagging, they integrated AI-powered models to extract relevant details from video content, including speaker identification, objects, locations, and key topics. This enabled automatic categorization and smarter search results.
2. Structured Indexing with Consistent Metadata – They standardized metadata fields, ensuring every video stored information like title, duration, resolution, key timestamps, detected text, and content categories. This allowed editors to filter content quickly without guesswork.
3. Searchable Metadata Database – Instead of storing metadata in a basic file system, they built a search-optimized metadata database, enabling instant retrieval with advanced filters like date ranges, topics, and detected objects. This drastically reduced content retrieval time from hours to seconds.

The result? Faster content production, streamlined workflows, and a more intelligent video library.
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Mistake #5: Fragmented tech stacks and vendor lock-in

A subscription-based video platform found itself stuck. Initially, they built their infrastructure using AWS Media Services, assuming it would scale effortlessly. But as their user base grew, so did their AWS bill hidden data transfer fees, unpredictable pricing, and the cost of managing multiple AWS services became overwhelming. Worse, their entire video workflow was locked into AWS, making it nearly impossible to migrate without a massive engineering effort.
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Why fragmented tech stacks create chaos

Many teams start with off-the-shelf cloud services, integrating separate tools for encoding, storage, streaming, AI-based tagging, and analytics. At first, it works. But as the platform scales, the downsides become clear:

• Ballooning costs: Vendor lock-in leads to expensive overages, with limited flexibility to optimize costs.
• Limited portability: Moving away from a provider like AWS becomes a major re-engineering project, forcing teams to stay despite rising costs.
• Operational complexity: Managing multiple disconnected services requires additional engineering resources, increasing development time and maintenance overhead.

For this video platform, each new feature required navigating AWS’s fragmented services, leading to a bloated stack that slowed down innovation instead of enabling it.
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The solution

1. Switched to a unified video platform: Instead of juggling separate services for encoding, streaming, and analytics, they moved to a single video platform that handled everything end-to-end. This cut costs, reduced integration headaches, and eliminated the need for constant configuration updates.
2. Ensured open API compatibility: To avoid future lock-in, they adopted a RESTful API-based solution, allowing them to move between cloud providers without major rewrites. This meant if pricing or performance issues arose, they could migrate workloads without breaking their entire system.
3. Adopted a multi-cloud strategy: Instead of relying exclusively on AWS, they distributed workloads across AWS, Google Cloud, and a private cloud setup. This gave them better redundancy, lower costs, and no single point of failure.

The result? Lower costs, greater flexibility, and the ability to scale without AWS dictating their roadmap.
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Final thoughts

Most video issues don’t start big they show up as slow load times, inconsistent quality, or unexpected costs. and by the time they do, your team’s already stuck firefighting. the fix isn’t patching together more tools. it’s rethinking how video is handled from the start.

FastPix gives you everything in one place upload, encode, stream, analyze with a single API built for scale. no hidden fees, no brittle workarounds, no fragmented stack. Explore our docs and guides to get started.