WASI 1.0

It looks like in 2026, Wasm might finally become a full-fledged “third layer” of k8s — between serverless and the familiar Docker.

Key advantages:

• Microsecond start and density: Modules weigh KB/MB, startup happens instantly (ms). This will allow keeping thousands of active instances on one node, which is economically non-viable for Docker due to parasitic load.
• WASI 0.3 and Native Async: The main technical breakthrough. Thanks to Stack Switching, asynchronous I/O has become native.
• WasmGC (Garbage Collection): Languages with managed memory (Java, Kotlin, Go, Dart) run in Wasm without unnecessary overhead, which has opened the technology for Enterprise development, not just for fans of explaining why their language is better.
• Component Model (WIT): Now you can assemble a service where cryptography is in Rust, business logic in Go, and text processing in Python, and all of this is one compact and secure binary artifact.
• AI on the Edge (wasi-nn): The wasi-nn standard allows running models on GPU/NPU without the need to drag gigabytes of dependencies (CUDA, Python libs) in a container.

Bottom line for Ops:

Hopefully, it will be a drop-in replacement in terms of management. In any case, Docker remains the “truck” for CI/CD, legacy, and heavy loads, while Wasm is for everything else.

Time to try it in action ;)

• KWasm.sh — Kubernetes Operator that “teaches” your nodes (EKS, GKE, Azure) to run Wasm with a single Helm command.
• SpinKube — probably the best way to deploy your first Wasm microservice. Clear SDK and a ready-to-use Operator for K8s.
• Runwasi (containerd) — for those who want to know “how it works under the hood.” The communication standard between K8s and Wasm runtimes.
• WasmEdge — the main runtime for those who need AI inference (wasi-nn) and high performance.
• wasmCloud — if you are building a distributed system of actors.