Beyond Docker: The Future of Containers in Development

May 4, 2025

For years, Docker was synonymous with containers, revolutionizing DevOps with portability and ease of use. But as cloud-native demands evolved, so did the ecosystem. Today, Docker remains a favorite for local development, while production increasingly embraces modular, lightweight alternatives. Here’s why modern infrastructure is moving beyond its monolithic design.

The Rise of Docker (and Containers)

Docker began in 2013 as an internal project at a PaaS company called dotCloud, founded by Solomon Hykes. The goal? Solve the headaches of environment consistency and application deployment.

While the underlying technologies; like Linux Containers (LXC), cgroups, and namespaces had existed in the Linux kernel for years, they were difficult to use and lacked accessible tooling. Docker built on these primitives to deliver a streamlined, developer-friendly experience.

Why Docker Won (2013-2018)

Simplicity: docker run was leagues easier than LXC/cgroups manual config. ✅
Ecosystem: (Docker Hub) gave devs a central image registry. ✅
DevEx: Clean CLI and layered filesystem for images made containers accessible. ✅
Community: Open-source momentum fueled adoption. ✅

By 2014, Docker was open-sourced and quickly became the standard for containerizing applications. It was widely embraced by developers, startups, and enterprises alike. Its simplicity made container adoption explode across startups and enterprises alike.

Kubernetes and Docker: From Partnership to Parting

Initially, Docker and Kubernetes were natural allies:

Docker provided the container runtime and packaging format.
Kubernetes handled orchestration and scaling of those containers.

When Google open-sourced Kubernetes in 2014, it was designed from the start to work with Docker, which had already become the standard for building and running containers.

Docker also developed its own native orchestrator: Docker Swarm. Integrated directly into the Docker CLI, Swarm offered a simpler, opinionated solution for clustering and deploying containers, well-suited for smaller teams or lightweight deployments. However, as application infrastructure needs grew more complex, the community increasingly favored Kubernetes for its scalability, flexibility, and extensibility in enterprise-grade environments.

As Kubernetes matured, its architecture evolved:

It introduced the Container Runtime Interface (CRI) to support multiple container runtimes.
Docker’s tight coupling with its centralized daemon and reliance on dockershim (a compatibility layer) no longer aligned with Kubernetes’ modular goals.

In Kubernetes v1.20 (2020), the project officially announced Docker would be deprecated as a container runtime (though still supported as an image format). This marked a shift toward Kubernetes-native runtimes like:

containerd – originally extracted from Docker and now a CNCF project
CRI-O – built specifically for Kubernetes

In response, Docker integrated Kubernetes into Docker Desktop and reoriented its focus toward developer experience and tooling. Still, the message was clear: Docker’s role in production container orchestration had diminished in favor of more modular, Kubernetes-aligned solutions.

Why Docker Fell Out of Favor in Production

As container orchestration scaled and matured, Docker’s once-groundbreaking architecture began to show its age in production environments.

First, Docker’s monolithic design, centered around a root-running daemon and the runc runtime, introduced unnecessary performance overhead. Startup latency, higher memory and CPU usage, and a single point of failure became significant drawbacks. In contrast, modern runtimes like containerd, CRI-O, and Kata Containers offer lighter, daemonless alternatives with faster boot times and tighter Kubernetes integration.

Second, security concerns pushed production teams toward more isolated, multi-tenant-safe alternatives. Docker’s architecture, particularly the root-level daemon, increased the attack surface. Emerging solutions like Firecracker and Kata Containers (MicroVMs), along with gVisor, brought stronger isolation guarantees critical for cloud-native workloads.

Finally, Kubernetes’ move toward modular, native runtimes reinforced the shift away from Docker. As mentioned earlier, the introduction of the Container Runtime Interface (CRI) and Docker’s deprecation in Kubernetes v1.20 opened the door for containerd and CRI-O (tools purpose-built for Kubernetes’ architecture) to become the standard in production environments.

Together, these forces have pushed Docker out of the production spotlight, replacing it with tools designed for the speed, scale, and security demands of today’s cloud-native systems.

Modular Tooling Over Monoliths

Just as UNIX championed small, composable tools over monolithic software, the container ecosystem is now embracing specialization. Instead of Docker’s once-dominant all-in-one CLI handling builds, runs, and pushes, today’s container ecosystem favors modular, composable tools over which also makes it easier to audit, automate, and scale.

Some popular tools in this modular toolchain include:

Buildah – Build OCI-compliant images without a daemon
Podman – A daemonless, rootless container runtime with Docker CLI compatibility
Skopeo – Inspect, copy, and move container images between registries
crun – A lightweight, faster alternative to runc, which is written in C (as opposed to Go) and often used with Podman,

Faster and Smarter, Reproducible Image Building

The traditional docker build is being replaced by more modern, performant, and reproducible build tools designed for today’s fast-moving CI environments and security needs.

Notable alternatives include:

BuildKit – Docker’s own next-gen build engine, with support for parallelism, build secrets, and advanced layer caching
Earthly – Combines Dockerfile syntax with Makefile-style logic to enable CI-friendly, repeatable builds
img – Build container images in unprivileged environments, using BuildKit under the hood
Nix/Nixpacks – Declarative, reproducible builds from the Nix ecosystem, ideal for functional package management and containerless deployment
Bazel – A high-performance build system with deep dependency tracking and support for multiple languages
Ko – Build and push Go applications to OCI images without Dockerfiles
CUE – (Configure, Unify, Execute) A configuration and validation language increasingly used in CI/CD pipelines to define reproducible image specs

These tools focus on immutability, minimalism, speed and reproducibility, which are critical for supply chain security and compliance-driven environments.

📌 Note: The Nix ecosystem, including tools like nix and nix-dockerTools, enables builds that are deterministic, sandboxed, and often do not require containers at all, an increasingly attractive paradigm for infrastructure teams seeking maximum reproducibility and minimal attack surfaces.

Cloud-Native World

In the cloud-native world, efficiency is no longer optional, it’s a core design requirement. Every second of container boot time and every megabyte of memory used can add up to real operational cost.

Modern runtimes and container tools optimize for:

Faster startup times (critical for autoscaling and serverless workloads)
Lower memory and CPU footprints
Rootless operation, which reduces the need for privileged access
Better cold-start performance, especially in ephemeral compute environments like AWS Lambda, Knative, and FaaS platforms

Examples of performance- and cost-conscious tools include:

Firecracker – Lightweight MicroVMs built for serverless compute (used by AWS Lambda and Fly.io)
Kata Containers – VM-like isolation with container speed
gVisor – User-space kernel sandboxing for stronger isolation with minimal performance hit
SlimToolkit (docker-slim) – Automatically minifies container images by removing unnecessary files and reducing attack surface
Distroless Images – Minimal container images with only your app and its dependencies, no package managers or shells

By reducing overhead and enhancing isolation, these tools are helping organizations deliver faster, cheaper, and more secure infrastructure.

Looking Ahead: AI, Edge, and WebAssembly (WASM)

AI/ML workloads and edge computing are reshaping cloud-native architecture and Docker remains a useful tool in this future:

Local experimentation and prototyping with Docker still drives innovation in AI/ML workflows.
Edge deployments benefit from Docker’s ease of packaging and portability, especially in small form-factor devices.
As infrastructure becomes increasingly event-driven and distributed, containers will adapt rather than disappear.

WebAssembly (WASM) and Containers: Friends, Not Foes

WebAssembly (WASM) is gaining serious traction as a complement (not a replacement) to containers, especially in edge, serverless, and high-performance computing scenarios. I think the future isn’t one replacing the other, it’s using each where they shine. Docker democratized containers. But the next decade belongs to modularity, speed, and specialization.

As AI, edge, and WASM reshape infrastructure, one question remains: Will your stack evolve with them?

Key takeaways:

Serverless Containers Are Growing: WASM enables ultra-fast cold starts, making it ideal for event-driven, short-lived functions.
Edge Computing Fit: WASM’s lightweight runtime, sandboxed isolation, and fast execution make it ideal for IoT, CDNs, and real-time apps.
Not a Replacement for Containers: WASM can’t directly access system resources like containers can. It’s not suited for full-fledged applications (yet).
Docker Is Adapting: Docker Desktop now supports WASM workloads using runtimes like Wasmtime and WasmEdge.
Disruption Is Real: WASM is redefining how we think about secure, fast-executing workloads at the edge and in serverless.
The Hybrid Future: We’re likely to see combined stacks using Docker for orchestration and heavier workloads, and WASM for lightweight, secure, and fast functions.