What is the maximum number of buckets per AWS account?

[savas]

The maximum number of S3 buckets you can create per AWS account is 100 by default, but it actually depends on the region and what you’re doing with the buckets. You may run into situations where you feel constrained by that limit, especially as your projects grow. If you hit that limit, you have the option to request a limit increase through the AWS Support Center. Just make sure you're prepared to explain your use case and why you need more buckets.

You should know that S3 buckets are globally unique. This means that if you create a bucket named "mybucket", no one else in the entire AWS ecosystem can have a bucket with that same name. It adds a layer of complexity because it’s not merely about creating buckets for different projects - you need to consider naming conventions that will work for you without conflicting with others.

I’ve had my fair share of challenges managing bucket names. For instance, if I was working on multiple environments like development, staging, and production, I had to devise naming conventions like "dev-mybucket", "staging-mybucket", and "prod-mybucket" to keep it organized and compliant with the uniqueness constraint. This isn't just for aesthetics; it helps in maintaining clear separation in resource management.

The S3 service is also not just about storing files. You have to consider features like versioning and lifecycle policies. If you’re planning to take full advantage of S3, what you do with the objects in those buckets is critical. For example, let’s say you have versioning enabled for a bucket to keep track of changes. Each version adds to the storage cost and limits, since you’re essentially storing multiple copies of the same file. You might find yourself needing more buckets simply because managing versions in one bucket could become a nightmare if you’re not careful.

There are also permission configurations to think about. Using IAM policies, you can manage access at the bucket level and even down to individual objects. I often run into situations where different teams need separate access permissions. In such cases, I’ve created multiple buckets exclusively for team-specific resources. You might want to segregate environments or groups for security reasons, too, which might push your limits even further.

Let’s break this down a bit. You need to consider bucket names alongside the management features you want to utilize. If you are working with multiple developers on a microservices architecture, then having distinct buckets for logs, static files, and backups may start demanding more than the simple default. This level of organization leads to better security and easier management, but the 100-bucket limit can feel restrictive.

Another aspect to be aware of is that different regions can sometimes have their own peculiarities. While the 100-bucket limit is standardized, certain restrictions and capabilities can vary from one AWS region to another. If you’re doing global deployments and have requirements for redundancy or lower latency, choosing the right region becomes imperative. It might be tempting to create multiple buckets in different regions, but the instant you do that, you're back to having to manage distinct account settings, permissions, and names—so that could tricky if you are under the default bucket limit.

Dumping a slew of files into a single bucket can seem easier at first, but you may realize later that you’re adding operational complexity for things like object organization and lifecycle management. Routing large sets of objects, configuring lifecycle rules, and figuring out if you want those objects to transition to different storage classes can create friction down the line. Your AWS account’s lifecycle policies might make you wish you had created a separate bucket for types of files. For instance, I found it beneficial to segregate frequently accessed objects from infrequent ones. Over time, I’ve learned that more buckets can sometimes simplify management, despite those rules about counts.

When dealing with these limitations and complexities, think about implementing tagging strategies. Tags are metadata that can help you filter and search for specific buckets efficiently. Suppose you create a bucket for each department or project; having a tagging strategy means that you won’t get lost in 100 buckets eventually. You can track costs better, audit usage efficiently, and even set up reports that can tell you which buckets are being used and by whom without sifting through every single one.

Consider also what happens when your use case scales. What was once a simple application that only required a few buckets can quickly expand into something considerable. You could be accommodating dozens of microservices, logs, database backups, and static assets. You may find that suddenly needing 150 buckets is not unreasonable, but if you've already hit that limit, you would have to file a ticket and justify your request for an increase. Be prepared to explain the rationale behind your needs in detail, using performance metrics and potential costs to back it up.

On top of that, be wary of any resource quotas and limits for other AWS services in parallel usage with S3. Each service comes with certain limits that can influence how you design your architecture. You might face constraints on requests per second or network throughput that can further complicate how many buckets you may want to deploy across AWS. It’s not all about the bucket limit; your overall AWS resource planning should consider many factors to manage costs and performance effectively.

I love using CloudFormation or Terraform for this kind of work because they allow me to manage my resources with code. Having Infrastructure as Code means I can create templates that set up multiple buckets with specific configurations automatically, rather than manually creating each bucket one by one. If you haven’t started using Infrastructure as Code, I highly recommend you to look into it. You'll find that it saves time and minimizes human errors, especially when working in environments where you need to create or replicate infrastructure quickly.

In the end, while the default maximum of 100 buckets might at first seem like a point of frustration, effectively managing your AWS account means thinking critically about resource management. I’ve always found that considerations about naming conventions, versioning, permissions, and overall architecture can sometimes be more important than simply trying to get the bucket count increased. You will likely find that it pays off in the long run to take a disciplined approach to bucket management while keeping an eye on your scalability requirements. If you find yourself nearing that limit, get ahead by planning how to justify a request, or even explore alternatives to how you organize your storage strategy. You might just find that one bucket does the job if you think creatively about your architecture and sizing strategies.