Where 2you fits
in your infrastructure.
Build and operate your own delivery infrastructure
Streaming providers increasingly choose to insource portions of their delivery chain — building and operating their own cache program rather than relying entirely on third-party CDNs. Deploying cache nodes within or next to broadband access networks to cut transit costs and reduce latency is a well-established playbook in the industry.
The same edge infrastructure, when made programmable, goes beyond the core content delivery function. It can enrich the content delivery itself — with manifest manipulation, server-side ad insertion, content protection, and dynamic packaging running at the edge — and support additional end-user services such as gaming, real-time applications, and other workloads that benefit from proximity to the subscriber.
2k8s provides the platform to make that happen — orchestrating distributed cache and application workloads as one managed system, with centralized observability and programmable routing across the entire footprint.
Build a programmable origin and midtier layer
Streaming providers operating their own origin and midtier infrastructure — the layers that store, package, and feed content to CDN providers — increasingly need more than passive content serving. Running packaging, security, ad-tech, and other functions closer to the source turns a traditionally passive layer into an active part of the delivery stack. This includes both internally developed applications and third-party solutions that can be deployed and managed as containerized workloads alongside core delivery functions.
As origin and midtier infrastructure scales across multiple locations, operational complexity grows with it. Unified configuration, centralized observability, and routing across the footprint become critical — and increasingly difficult to achieve without a dedicated platform layer.
2you addresses both. It provides the orchestration and operational foundation to run distributed origin and midtier infrastructure at scale, while enabling a programmable application layer where internal and third-party workloads run alongside delivery with lifecycle management, isolation, and centralized visibility. The result is a self-hosted origin and midtier layer with the kind of application enablement associated with leading public edge platforms — without ceding control.
Consolidate content delivery infrastructure across multiple content sources
Live streaming has fundamentally changed the demands on broadband networks. The fragmentation of live sports rights, the growth of OTT live programming, and the rise of large-scale live events create recurring peaks in network traffic that existing content delivery solutions struggle to absorb. CDN caches hosted on operator networks today are typically tied to individual content providers — leaving operators with fragmented infrastructure, uneven utilization, and limited ability to adapt as the traffic mix shifts.
A better model is to consolidate hosted third-party CDN deployments, streaming-provider workloads, and operator-owned delivery functions onto a shared multi-tenant platform. That gives operators a consolidated environment that can serve multiple tenants — including their own applications — while supporting broader B2B and B2C edge use cases over time.
2k8s provides the multi-tenant foundation for this model — with isolation across compute, network, and platform services, a common control plane, and the networking and integration capabilities so tenants can onboard their workloads, run them securely, and integrate operator-hosted edge capacity into their existing delivery overlay with minimal friction.
Extend Kubernetes to distributed and edge environments
Many solution providers operating across distributed customer environments want the operational and integration benefits of Kubernetes-native deployment — but find it out of reach. Running Kubernetes across distributed and edge locations means operating a distributed control plane, handling connectivity across sites, and building the operational discipline to keep deployments consistent at scale. For most ISVs, system integrators, and managed service providers, that effort is a barrier rather than a foundation.
2k8s removes that barrier by providing a managed Kubernetes control plane that extends across distributed and edge environments, without requiring solution providers to build and operate the distributed platform themselves. Deployments that previously relied on dedicated appliances or proprietary software stacks can migrate to a containerized, Kubernetes-native model that is consistent across customer sites, centrally observable, and manageable from a single point of control.
The benefits extend beyond the operating model itself. A standard Kubernetes interface opens up a broad development and integration ecosystem, making it easier to build internally and to incorporate open source and third-party software into the solution. CI/CD tooling, GitOps workflows, Helm charts, operators, and packaged software become far more accessible, reducing the effort required for both internal development and vendor onboarding. Existing skills and experience from public cloud and enterprise Kubernetes environments can also carry over, shortening the path to productive deployment. Where needed, the same platform can extend across public edge, operator edge, and customer-owned infrastructure without requiring a separate deployment model for each.
Deploy distributed AI inference and video analytics at the edge
Where content delivery moves data toward users, AI inference and video analytics require compute to move toward the data source. Shipping raw streams or large volumes of data back to a centralized environment adds latency, increases transport cost, and makes it harder to support real-time execution of application business logic. In many edge use cases, the challenge is not only running the workload, but placing it across a distributed footprint in a way that is operationally practical.
2k8s provides the platform for that model. It allows inference and analytics workloads to be deployed across distributed and edge locations as containerized applications, with a common control plane, centralized observability, and a consistent operating model across the footprint. That makes it easier to run latency-sensitive processing close to the source while managing deployments as one system rather than as a collection of isolated sites.
The result is a more practical path to distributed AI and analytics: one that supports real-time processing, more efficient use of edge compute, and gradual expansion across customer-owned infrastructure, operator edge, and other distributed environments.
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