Containerized app hosting

Observability and distributed tracing are directly relevant to operating containerized applications, where distributed services, ephemeral workloads, and deployment complexity make diagnosis and performance management essential.

AI agents increasingly drive demand for deployable, scalable application runtimes, background jobs, secure sandboxes, and developer tooling, making them materially relevant to containerized hosting decisions.

Real-time and streaming workloads are a meaningful deployment use case for container platforms, influencing requirements for low latency, persistent connections, scaling, and event-driven architecture.

LLM workloads and AI-enabled applications increasingly shape deployment, scaling, GPU access, observability, and runtime requirements for container hosting.

Serverless and functions-as-a-service are a durable adjacent deployment model that directly shapes how developers choose to package, run, scale, and pay for applications.