Rolling out MCP-native video intelligence with Bitmovin
B2B
Media technology
Video analytics & QA
Bitmovin builds infrastructure for video streaming teams who care about quality, observability, and device coverage at scale. As AI copilots become part of everyday operator workflows, Bitmovin wanted their encoding, playback and analytics products to be accessible directly from those environments, without forcing users into separate dashboards or bespoke integrations.
Bitmovin’s approach
Bitmovin offers a comprehensive video infrastructure platform spanning encoding, playback, analytics, and automated testing. As AI copilots become part of everyday developer and operator workflows in the media industry, the company decided to make its various products available via MCP.
Bitmovin's products already expose rich APIs, but their real value comes from exploration, comparison, and diagnosis rather than single calls. Users ask questions, iterate, and connect data points across time ranges, devices, and regions.
MCP offered Bitmovin a way to express these workflows in a form that AI agents can reason over, extending its existing developer experience.
Use cases
Bitmovin’s customers increasingly rely on AI assistants to investigate issues and plan work. In response, the team chose to roll out MCP servers incrementally across products, starting with areas where agent-driven workflows deliver immediate value.
The first MCP integrations focus on two products.
Observability provides detailed insight into playback performance, license usage, and viewer experience metrics. The Observability MCP enables natural language exploration of this data, complementing the metrics already available in the Bitmovin dashboard. For example, users can ask: “What was the average startup time in Germany yesterday?”
Stream Lab automates video playback testing on real devices such as smart TVs, browsers, and consoles. The Stream Lab MCP allows teams to explore available test devices, create and run playback tests, and analyze test reports using natural language. Typical questions include: “How many test passes and failures do I have on Samsung Tizen and LG WebOS?”
While each MCP server exposes powerful capabilities through a simple interface, the real leverage comes from using them together. In this setup, agents can reason across both real-world playback data and controlled test environments.
This enables cross-product questions such as, “Based on my Bitmovin Observability data from the past week, am I running Stream Lab tests on the devices with the biggest issues?” or “What percentage of video plays come from devices I am currently testing with Stream Lab?”
Behind the scenes, these requests are translated into structured API calls across Observability and Stream Lab, with results returned in a form the AI copilots can interpret and correlate.
The role of Alpic
To support their multi-product MCP strategy, Bitmovin needed:
a production-grade way to host and scale MCP servers
analytics on how their servers were being used and by whom
a deployment model that lets teams iterate without breaking existing users
Running and maintaining custom MCP infrastructure in-house would have slowed down product teams and distracted from core video engineering.
Alpic provides the infrastructure layer that makes these MCP servers production-ready. For Bitmovin, Alpic enables:
MCP-native hosting for multiple MCP servers
reliable scaling and isolation, suitable for enterprise video products
multi-environment workflows, so new tools and parameters can be tested safely
operational simplicity, avoiding custom deployment pipelines for each MCP service
By relying on Alpic, Bitmovin can focus on designing agent-friendly tools that reflect real user workflows, rather than managing servers, transports, and protocol edge cases.
Result
Bitmovin can now roll out MCP servers across multiple products without rethinking deployment each time. Customers gain direct access to video insights and automated testing from their AI assistants, while Bitmovin retains a single, coherent hosting strategy.
As more of the platform becomes agent-accessible, MCP becomes a natural extension of how Bitmovin products are used, enabling cross-product reasoning that augments its current dashboard and APIs.
MCP lets us meet users where they already work. Alpic removes the operational overhead, so our teams can focus on making video analytics and testing truly usable by AI agents.