Why Amazon S3 Makes More Technical Sense Today (Especially After MinIO’s Shift)

For years, MinIO was the easiest way to run S3-compatible storage on your own infrastructure. It provided developers with a familiar API without forcing them to commit to a specific cloud provider. However, with MinIO now in maintenance mode and several features pulled behind commercial licensing, using it for long-term workloads has become increasingly difficult to justify.

From a technical and operational perspective, Amazon S3 simply provides more reliability, better integrations, and a cleaner operational model. Here's a breakdown of what actually matters when choosing between the two.

Operational Load: S3 vs MinIO

Running MinIO means managing everything underneath it:

• server capacity
• disks and I/O performance
• replication between nodes
• upgrades and patches
• HA configuration
• certificates
• monitoring and alerting
• backup and restoration procedures

S3 removes the entire operational surface.
No cluster design, no storage expansion planning, no tracking disk failures, no manual failover.

This instantly reduces your operational risk and eliminates the "hidden cost" of managing object storage.

Durability and Consistency Guarantees

S3 is designed with multi-AZ replication built into the service. You get:

• 11 nines durability
• strong read-after-write consistency
• automatic redundancy
• built-in protection against AZ-level failures

Reproducing this with MinIO requires:

• multiple nodes
• distributed mode
• a reliable network layer
• external load balancing
• proper quorum management

And even then, you won't match AWS's durability guarantees.

Security and Access Control

S3 integrates with AWS security features out of the box:

• IAM with fine-grained permissions
• Bucket policies and condition keys
• KMS-managed encryption
• Access logging and CloudTrail auditing
• VPC endpoints for private traffic
• Object Lock for immutability

With MinIO, these capabilities require manual configuration or external tools. Some features, like Object Lock with legal hold, are not equivalent to the AWS implementation.

Event-Driven and Analytics Integrations

This is where S3 is simply in a different category.

• S3 events can trigger:
• Lambda functions
• SQS queues
• SNS topics
• EventBridge event patterns
• Step Functions workflows

And S3 integrates directly with analytics services such as:

• Athena
• Glue
• EMR
• Redshift Spectrum

MinIO can emit notifications, but the overall ecosystem integration is nowhere near as seamless or deeply supported.

Cost Efficiency Through Lifecycle Management

S3 storage classes give you fine-grained control over cost:

• S3 Standard
• Standard-IA
• One Zone-IA
• Glacier Instant Retrieval
• Glacier Flexible Retrieval
• Glacier Deep Archive

Lifecycle policies can automatically transition objects or expire them.

While MinIO can tier data to external storage, it's far more manual, requires additional configuration, and doesn’t come with clear cost modelling.

How I Structure Buckets for Production

Here's a pattern that works well across most systems:

├── app-logs/
│   ├── versioning: enabled
│   ├── lifecycle: move to IA in 30 days, delete in 180
│   └── encryption: SSE-S3
│
├── media/
│   ├── versioning: enabled
│   ├── lifecycle: IA after 90 days
│   └── encryption: SSE-KMS
│
└── static-assets/
    ├── public access: blocked
    ├── cloudfront origin: yes
    └── caching: configured via headers

This setup keeps costs controlled while maintaining both security and durability.

Most large companies follow four core principles when designing their S3 structure:

1. Separation by data purpose (logs ≠ media ≠ analytics)
2. Isolation by environment (dev, staging, prod)
3. Least privilege access (per-bucket IAM policies)
4. Strong lifecycle controls (move cold data automatically)

Using these principles, here is a battle-tested structure you can adopt directly:

abc-company-s3/
├── global/
│   ├── shared-artifacts/
│   ├── security-logs/
│   └── compliance/
│
├── prod/
│   ├── app-data/
│   ├── media/
│   ├── analytics/
│   ├── backups/
│   └── logs/
│
├── staging/
│   ├── app-data/
│   ├── media/
│   ├── analytics/
│   ├── backups/
│   └── logs/
│
└── dev/
    ├── sandbox-<team>/
    ├── sandbox-<engineer>/
    └── temp/

Conclusion

With MinIO no longer actively evolving and key features moving behind paid editions, the operational and long-term risks have increased. For production workloads, especially ones that need durability, compliance, and strong integration with other services, S3 remains the more stable and future-proof choice.

It's not just "using a cloud service instead of self-hosting."
It's choosing a storage layer that removes operational overhead, scales automatically, and integrates cleanly with the rest of the AWS ecosystem.

Final Thoughts

This structure comes from my own experience working with S3 in production setups, combined with research into how larger engineering teams organise their storage at scale. There's no "one perfect layout", but the patterns above tend to hold up well as applications grow and infrastructure becomes more complex.

If you manage S3 differently or follow another pattern that works well for your team, I'd love to learn from it. Feel free to share your approach, improvements, or any suggestions that could make this layout even stronger in a real production environment.