TL;DR Video workflows are becoming more automated, measurable, and real-time across the stack, but adoption is running ahead of what teams have actually operationalized. Four data points make the case: Each year, Bitmovin surveys video developers, product leaders, and executives from around the world to understand how the technologies, business models, and priorities shaping streaming…
Bitmovin’s Observability solution already gives teams deep visibility into playback quality and audience behavior. But when an operations team wants to assemble its own view of that data via our API, the current answer is to build it in a tool like Grafana. That works, and it is powerful, but spinning up and maintaining…
Building a video streaming platform used to take weeks and a team of specialists. With an AI coding agent and Bitmovin, it now takes a day. Bitmovin’s CEO proved it by building Bitflix, a fully working Netflix-style streaming service in 24 hours using Claude Code alongside Bitmovin’s VOD Encoder, Player, AI Scene Analysis, and Observability solution. In this post, we walk through how AI coding agents work with Bitmovin’s MCP server and CLI, the step-by-step prompt sequence to get your own streaming service up and running, and what you get at the end.
Building video observability in-house sounds like the ultimate control play, until you factor in the real cost. This post breaks down the true TCO of building vs. buying a video analytics and observability solution, with tailored scenarios for OEMs, OVPs, broadcasters, SVOD, and AVOD platforms. From engineering overhead to scalability risks and revenue blind spots, discover which approach actually makes sense for your streaming operation.
As streaming platforms scale globally, development teams increasingly need to understand how playback activity is distributed and evolving in real time. However, impression data, one of the most direct indicators of player usage, is typically surfaced only after aggregation and processing through reporting pipelines. This makes it useful for analysis, but limits its value…
Streaming teams rely on observability data to understand how playback performs across devices, platforms, and networks. Error metrics sit at the heart of this, helping engineering teams catch problems that affect startup time, playback stability, and overall viewer experience. But acting on those signals is not always straightforward.
Playback quality defines the success of every streaming service, shaping how viewers perceive the value of the content they consume. A single session can generate hundreds of signals that reflect performance, including start-up times, buffering events, and error codes. The challenge is that these signals are reported differently depending on the video player in use,…
For developers working on streaming apps across Apple platforms, error resolution has often felt like chasing shadows. Playback interruptions might show up as vague “Operation couldn’t be completed” messages, offering little guidance for debugging or pattern recognition. When every second of viewer downtime impacts QoE and revenue, these generic error codes don’t just slow teams…
Super Bowl LIX became the most-streamed NFL event in history, drawing over 15.5 million peak concurrent viewers on FOX’s Tubi platform. To deliver seamless playback at that scale, FOX deployed Bitmovin’s Player – optimizing startup times, adaptive bitrate streaming, and cross-device consistency. Combined with real-time telemetry that captured over 25 billion playback beacons, the result set a new benchmark for live sports streaming.
Bitmovin’s Observability provides valuable insights that can take the performance and efficiency of your video streaming workflow to new levels. By leveraging Observability, you can optimize encoding profiles, improve player performance, gain deeper understanding of audience behavior, and ultimately, deliver a superior viewing experience while managing costs effectively. This blog post will explore seven…
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