The End of an Era: MinIO Community Edition is Archived -What’s Next?

On February 13, 2026, the tech world hit a significant milestone, though perhaps not the celebratory kind. The minio/minio GitHub repository, the heart of the MinIO Community Edition, was officially archived. It is now read-only and explicitly marked as “no longer maintained.”

For years, MinIO was the undisputed heavyweight champion of “S3-in-a-container.” With over 60k GitHub stars and more than a billion Docker pulls, it was the go-to for everyone from homelab hobbyists to enterprise ML engineers.

But the writing has been on the wall for a while. Let’s look at the timeline that led us here:

• June 2025: MinIO removed major admin console capabilities from the Community Edition, pushing those features toward paid tiers.
• December 2025: The project signaled a “maintenance mode” trajectory, shifting focus toward their commercial AIStor product line.
• February 13, 2026: The repo was archived. The focus has officially shifted to commercial offerings.

Important Distinction: It is specifically the Community Edition that has been deprecated. While MinIO as a company continues with AIStor, the free, open-source path many of us relied on has reached its sunset.

Why MinIO Was Everywhere

MinIO wasn’t just popular; it was foundational. It provided a drop-in S3-compatible API that could run anywhere.

Common Non-ML Uses

• Application Storage: Handling user uploads, images, and documents.
• CI/CD Caches: Storing build artifacts and release bundles.
• Log Tiers: A cheap landing zone for long-term log retention.

The ML Substrate

In Machine Learning architectures, MinIO often acted as the “glue” for data:

• MLflow: Storing model artifacts and pickles.
• DVC (Data Versioning Control): Serving as the S3 remote for large datasets.
• Lakehouses: Backing Iceberg or Delta tables for Spark and Trino.

The “S3-Compatible” Checklist

Before you jump ship to a new provider, remember that “S3-compatible” is a spectrum. You need to evaluate these specific capabilities to avoid breaking your stack:

1. IAM & Policies: How closely does it mimic AWS S3’s bucket policies?
2. Multipart Uploads: Crucial for large datasets and parallel processing.
3. Versioning & Object Locking: Essential for data integrity and compliance.
4. Consistency Model: Does it offer read-after-write consistency?
5. Event Notifications: Can it trigger Webhooks, Kafka, or NATS?

The Leading Alternatives

If you are looking for a new home for your data, here are the most credible “self-hosted S3” contenders in 2026.

1. Garage (Deuxfleurs)

Garage is the “anti-complexity” choice. It’s designed to tolerate the failures of real-world edge environments.

• Pros: Low overhead, great docs, tailored for small-to-medium clusters.
• Cons: Not 100% S3 feature parity; check your specific endpoint needs.

2. SeaweedFS

If you want something that feels like a “drop-in” for MinIO but adds more flexibility, this is it.

• Pros: Handles billions of small files efficiently; offers a FUSE interface.
• Cons: More moving parts (masters, volume servers, filers) than a single MinIO binary.

3. Ceph Object Gateway (RGW)

When you need “enterprise-grade” and don’t mind the steep learning curve, you go with Ceph.

• Pros: Battle-tested, highly scalable, and Kubernetes-friendly via Rook.
• Cons: Significantly higher operational complexity.

Migration Playbook: How to Move Your Data

Moving petabytes (or even gigabytes) of data requires a plan. Here are three ways to do it:

Option A: The “Offline Copy” (Fastest)

1. Freeze writes to your MinIO instance.
2. Use rclone sync or mc mirror to move data to the new target.
3. Recreate policies and users.
4. Update your app’s DNS/endpoints and resume writes.

Option B: The “Dual-Write” (Zero Downtime)

1. Stand up the new system.
2. Modify your application to write to both MinIO and the new system.
3. Background-copy old data to the new system.
4. Once synchronized, flip the “read” switch to the new system and retire MinIO.

Option C: Replication-Driven

If your new target supports S3-native replication, you can configure MinIO to replicate buckets to the new store automatically. Once they converge, you cut over.

Watch Out: The most common “gotchas” during migration are Presigned URLs (signatures can vary) and Lifecycle Rules (ensuring your auto-delete or transition settings carry over).

The Meta-Lesson: Engineering for Reversibility

The archival of the MinIO Community Edition is a reminder that even foundational open-source projects can pivot. To protect yourself in the future:

• Prefer Portable Clients: Use standard S3 SDKs rather than vendor-specific tools.
• Infrastructure as Code: Keep your bucket configurations and policies in Terraform or Pulumi.
• Regular Export Drills: If you can’t move your data out in 24 hours, you don’t own it, you’re just renting space.

Creating Terraform scripts for storage systems like Garage and SeaweedFS usually involves deploying to a Kubernetes cluster (via the kubernetes and helm providers) or directly to Virtual Machines (via cloud-init or specialized providers).

Since both are designed for “run-anywhere” flexibility, I’ve drafted these using Helm providers. This is the most “production-ready” way to manage their configurations, lifecycle, and S3 endpoints in 2026.

Sample Configurations

Terraform for Garage

Garage is often deployed as a DaemonSet or a small StatefulSet. This script uses the Helm provider to deploy the Garage chart, which is the standard way to handle its specific layout.

Terraform

# main.tf for Garage (using a published Helm repo)
resource "helm_release" "garage" {
  name             = "garage"
  repository       = "https://charts.derwitt.dev"
  chart            = "garage"
  namespace        = "storage"
  create_namespace = true
  # Strongly recommended for reproducibility:
  # version = "2.1.0"
  values = [
    <<-YAML
      garage:
        replicationFactor: 3
      deployment:
        replicaCount: 3
      # Persistence is split: meta (metadata DB) and data (object data)
      persistence:
        meta:
          enabled: true
          size: 5Gi
          storageClass: local-path
        data:
          enabled: true
          size: 100Gi
          storageClass: local-path
    YAML
  ]
  timeout         = 600
  atomic          = true
  cleanup_on_fail = true
}

Terraform for SeaweedFS

SeaweedFS is more modular. You typically need a Master (to manage volumes), a Volume Server (to store data), and the S3 Gateway (the MinIO-compatible layer).

Terraform

terraform {
  required_version = ">= 1.4.0"
  required_providers {
    helm = {
      source  = "hashicorp/helm"
      version = ">= 2.10.0"
    }
  }
}
# Assumes your kubernetes credentials are available via KUBECONFIG
# or in-cluster config when running from a Pod.
provider "helm" {
  kubernetes {
    config_path = var.kubeconfig_path
  }
}
variable "kubeconfig_path" {
  type        = string
  description = "Path to kubeconfig"
  default     = "~/.kube/config"
}
resource "helm_release" "seaweedfs" {
  name             = "seaweedfs"
  repository       = "https://seaweedfs.github.io/seaweedfs"
  chart            = "seaweedfs"
  namespace        = "seaweedfs"
  create_namespace = true
  # Optional but recommended: pin a known-good chart version
  # version = "x.y.z"
  # Prefer YAML for booleans to avoid string coercion issues.
  values = [
    <<-YAML
      s3:
        enabled: true
      filer:
        enabled: true
      master:
        replicas: 3
        persistence:
          enabled: true
          size: 10Gi
      volume:
        replicas: 3
        persistence:
          enabled: true
          size: 500Gi
    YAML
  ]
  # Helpful in many clusters (avoids race conditions on CRDs / namespaces)
  timeout          = 600
  atomic           = true
  cleanup_on_fail  = true
}

Notes / common adjustments (depending on your cluster)

• If you’re using EKS/GKE/AKS, you may prefer provider auth via exec plugins or environment variables instead of config_path.
• You may need to set storageClass explicitly if your cluster doesn’t have a default StorageClass, e.g.:

values = [
  <<-YAML
    master:
      persistence:
        storageClass: "gp3"
    volume:
      persistence:
        storageClass: "gp3"
  YAML
]

Key Differences for your Deployment

Post-Deployment Step: The “S3 Contract”

Once these are applied via terraform apply, you will need to:

1. Extract the Endpoint: Usually a Kubernetes Service ClusterIP or an Ingress URL.
2. Generate Keys: Unlike MinIO’s default environment variables, both Garage and SeaweedFS prefer using their respective CLI tools (garage layout assign or weed s3.configure) to generate access keys.

Conclusion

While the archival of the MinIO Community Edition marks the end of an era, it isn’t a signal to abandon the “S3-anywhere” philosophy. MinIO served as a foundational proof-of-concept for the industry, proving that high-performance object storage didn’t have to be locked behind a proprietary cloud vendor’s API. It democratized data infrastructure for a generation of developers and ML engineers.

However, the ecosystem today is pregnant with alternatives that have matured alongside the industry’s shift toward decentralization, edge computing, and massive scale. Whether you need the lightweight resilience of Garage, the multi-protocol versatility of SeaweedFS, or the enterprise gravity of Ceph, the open-source community has already built the lifeboats.

The deprecation of one project is simply a forcing function to build more resilient, vendor-neutral architectures. By treating storage as a portable dependency rather than a permanent fixture, you ensure that your data, and your stack, remain truly open.