AWS S3 vs Google Cloud Storage – Which is Better? (Pros and Cons)

AWS S3 vs Google Cloud Storage – Which is Better? (Pros and Cons). Choosing the most suitable cloud storage can be quite complicated. There are many deciding factors you have to put into consideration before settling for one. Regardless, both AWS S3 and Google Cloud Storage provide reliable and secure storage for your object data. They are highly available and accessible and deliver high scalability. 

So, to help you navigate the cloud storage options, this article compares AWS S3 vs Google Cloud Storage. Basically, we look into each platform’s features, how it works, and its pros and cons, alongside the major differences. 

Shall we start with AWS S3 vs Google Cloud Storage – Which is Better? (Pros and Cons). Read on!

What is AWS S3?

First of all, AWS S3 is an object level storage service by Amazon Web Services. It provides storage for unstructured data rather than file or volume data. So, AWS S3 stores its data as objects within buckets. With AWS S3, you can store any amount of data, create a data backup, or retrieve it at any time from any device.

Besides, AWS is used by companies worldwide to store and protect data for various use cases. These include mobile applications, websites, data lakes, IoT devices and big data analytics. Besides storage, AWS S3 provides management features. These features enable users to organize, optimize, and configure data access to meet specific business needs. 

How AWS S3 Works

Primarily, with AWS S3, it provides data storage in the form of objects within buckets. Ideally, an object is a file, and the metadata describing the file’s contents. On the other hand, a bucket is a container that stores the objects.

You can store data in Amazon S3  by first creating a bucket, naming it, and specifying the preferred AWS Region. After that, you can upload your object data as objects to the buckets. Each object should have a key, which is basically a unique identifier for the object.

With the objects in the buckets, you can use the various AWS features to configure your data. For instance, you can create access permission, version the objects, and more.

Features of AWS S3

AWS S3 has numerous features that help in data management. These include:

Access Management

Basically, with Amazon S3 it enables users to audit and manage access to buckets and objects. By default, objects and buckets are private, and you have to grant granular resource permissions to other users. You can use the AWS Identity and Access Management (IAM) to manage access to your S3 resources. This way, you can control the type of access a user has to an S3 bucket.

Also, you can use bucket policies to configure resource based permissions. Generally, with S3 access points they help to configure network endpoints with dedicated access for data access management. Other access management features include Access Control Lists (ACLs). In nutshell, they grant read and write permissions to authorized users and S3 Block Public Access for restricting access to S3 objects and buckets.

Storage Management

You can utilize S3 management features to achieve various goals. These include regulatory requirements, cost management and latency reduction. The S3 Replication feature enables you to replicate objects with respective tags and metadata to other buckets within the same AWS region. This is crucial for security, low latency, and compliance.

In addition, the S3 Object Lock enables you to prevent deletion or overwriting of objects for a certain period or indefinitely. In addition, you can configure a lifecycle policy to manage objects throughout their entire lifecycle.

Logging and Monitoring

Next, S3 enables users to monitor their buckets and audit logs. This provides you with complete visibility into how your resources are used. You can use CloudTrail logs for auditing logs and for compliance and Amazon CloudWatch to track the operational health of your S3 resources. Besides, CloudWatch helps with cost management. You can configure billing alerts whenever your charges reach a certain threshold.

Data Analytics and Insights

In addition, with S3 data analytics and insights features enable you to visualize your storage usage. Leveraging these insights and analytics, you can analyse, understand, and optimize your storage. The Storage Lens feature provides usage and activity metrics for data aggregation. On the other hand, the S3 Inventory Reports helps you audit objects and corresponding metadata.

Pros of AWS S3

Here are some advantages of using AWS S3 for your object storage:

  • Highly scalable storage platform.
  • You can access AWS S3 from any device connected to the internet.
  • With AWS S3, you can store buckets in multiple Availability Zones to optimize availability.
  • AWS S3 secures data from malicious attacks.
  • You can choose the storage class to store data and optimize costs.
  • Integrates seamlessly with other AWS services for optimal performance.

Cons of AWS S3

  • AWS S3 configurations require a high skillset.
  • Downloading huge amounts of data can be expensive.
  • Has a complex pricing schema.

Up next with AWS S3 vs Google Cloud Storage – Which is Better? is to introduce Google Cloud Storage. 

What is Google Cloud Storage?

Google Cloud Storage is an object level storage platform by Google Cloud Platform (GCP). Primarily, it provides storage for unstructured data sets. Also gives you a flexible way of managing your storage. Ideally, Google Cloud Storage was designed as a competitor for AWS S3.

How Google Cloud Storage Works

So, with Google Cloud Storage, it  stores data as objects within buckets. Buckets are containers with the cloud that you can assign to various storage classes depending on your business needs.

Google has four storage classes:

  • Standard.
  • Coldline.
  • Nearline.
  • Archive.

You can store an unlimited amount of data in any of these classes. Each class is suitable for a certain type of data.


This class provides data storage for data that you access frequently. It’s ideal if you access data very often, such as serving website content, live video streaming, live gaming, and mobile applications. The Standard storage class stores data in at least two separate locations, improving its availability.


This class is ideal for storing data that that infrequently accessed. So, ideal for storing data with a minimum of 30 days duration. This storage class is low cost with high durability. 


Then, the Coldline is a storage class dedicated to infrequently accessed data that doesn’t require high availability. You can store data with a minimum storage duration of 90 days. Since it stores data you rarely access, this storage class offers the lowest storage costs.


You can store archival data in the Archive storage class. It’s suitable for data backups or the data you want to keep for a long period for compliance purposes. The Archive storage class has low availability but enables you to retrieve data in quickly.

Features of Google Cloud Storage

Google Cloud storage has multiple features that make it ideal storage for object level data. Some of these features include:

Data Encryption

Additionally, cloud storage secures user data through encryption. It uses server side encryption to encrypt your object data by default. In addition, it provides other encryption methods such as customer supplied encryption keys and customer managed keys. You can utilize these encryption methods to encrypt individual objects or buckets. Data encrypted cannot be compromised unless the hacker decryption key.

Identity and Access Management

Cloud Storage allows you to use the IAM feature to control access to your data. You can also use IAM to filter access to other resources, such as Compute Engine instances. Using IAM, you can grant roles, i.e., the ability to perform certain actions on cloud storage. Besides, you can grant each role permissions for each role.

Object Versioning

Object versioning in Google Cloud Storage enables you to retrieve accidentally deleted or replaced objects. You can turn on Object Versioning to maintain an archive of objects. Essentially, this helps undelete any objects that you may have deleted accidentally.

Object Lifecycle Management Configuration

Lifecycle configuration is a set of rules that apply to objects within a bucket as well as future ones. These rules enable Cloud Storage to perform a specified action once an object meets certain criteria. They help manage storage buckets automatically without much manual input. For instance, you can configure rules for object deletion, storage class change, etc. The object must reach the set threshold for the rule to trigger an action.

Pros of Google Cloud Storage

  • Has a great file structure. 
  • Provides fast data retrieval.
  • Easy and flexible to use.
  • Seamless integration with other Google services such as Drive.
  • You can access Google Cloud Storage from any device.

Cons of Google Cloud Storage

  • Backend API cannot integrate with other public cloud providers.
  • Limited storage space on the free plan.
  • The pricing scheme is overly complicated.

AWS S3 vs Google Cloud Storage- What are the Difference?

As evident above, both AWS S3 and Google Cloud Storage have numerous features and advantages. However, they have striking differences that you may consider before choosing the right object storage platform for you. Some of the top differences include:

Object Versioning

Both cloud storage platforms support object versioning to enable you to retrieve overwritten or deleted objects. However, they have different implementation methods.

In AWS S3, deleting an object creates a DELETE MARKER if you don’t specify a version. Also, you can use a version ID to delete a specific version.

Contrarily, in Cloud Storage treats object versioning differently as it maintains an archive and a master version. When you delete an object without specifying a version ID, it moves the object from master to archive. In addition, it does not create  Delete Marker. When you delete a master object using its version ID, it doesn’t move it to the archive. Rather, it deletes the object entirely and cannot be recovered.


Replication is the process of copying buckets and distributing them across buckets located in different geographical locations. Essentially, replication helps improve availability and offers protection against data loss.

So, AWS S3 supports replication through the Same Region Replication (SRR) or Cross Region Replication (CRR). You can also use Batch Replication to replicate existing objects to different buckets. When replicating objects in AWS S3, you can use an API.

On the other hand, Google Cloud Storage doesn’t have an API. Also, it lacks the flexibility of S3 CRR. However, you can replicate data storage by specifying the Multiregional storage bucket location. With this feature, Cloud storage stores your data in at least two locations.

Object Level Tagging

Here, AWS S3 allows you to tag objects for easier management. Tagging enables you to categorize storage easily. These tags enable fine grained lifecycle management and access control. You can easily set an object to more than one object with a single request or using Batch operations.

Google Cloud Storage enables object tagging using metadata. The main difference is that it doesn’t provide the same level of control and flexibility as S3. Also, S3 object tagging is quite difficult to understand, unlike in AWS S3.

Data Transfer

With AWS S3, you use Amazon Direct Connect to transfer massive amounts of data. Direct connect allows you to create a dedicated network connection so you can transfer data from on-premise to AWS. It establishes private connectivity between your data center and AWS. This connectivity increases bandwidth throughput and reduces network costs.

Google Cloud Storage uses the Storage Transfer Service to provide seamless data transfer from on-premise servers and Cloud Storage. Unlike Amazon Direct Connect, AWS has an offline data transfer service known as Transfer Appliance that sits on your on-premise data center to ship data to Cloud Storage.

Notification Management

When there are events in your AWS S3 environment, you receive notifications through either Amazon SQS or Amazon SNS. Also, the notifications can be delivered directly to Amazon Lambda to involve functions. AWS S3 notifications are set at bucket level either through the REST API, S3 Console, or using an SDK.

In Google Cloud Storage, Pub/Sub notifications provide information about changes in your buckets. However, the notification management doesn’t provide the level of flexibility as S3. Configuring alerts isn’t as straightforward as in S3.


AWS S3 is charged depending on your usage. Each storage class has different pricing depending on the amount of data stored. Besides, there are costs for data requests and retrieval, data transfer, replication, and Lambda.

For the S3 standard class (for frequently accessed data), the total cost of data storage is $0.023 per GB upto 500 TB. For data exceeding 500 TB, the total cost of data storage is $0.0021 per GB. S3 data archive in the Glacier Deep Archive tier costs 0.00099 per GB per month.

Storing data in the Standard Class (for frequent access costs $0.02 per GB per month, while the Archive storage tier costs $0.0012 per month. The prices are very close, but if you consider other factors such as data transfer, replication, and retrieval, Google Cloud Storage is relatively cheaper than AWS S3.

Thank you for reading AWS S3 vs Google Cloud Storage – Which is Better? We shall conclude. 

AWS S3 vs Google Cloud Storage - Which is Better? Conclusion

Both, AWS S3 and Google Cloud Platform are both suitable for data storage in the cloud. They provide low latency, high throughput, and enhanced security. AWS S3 has better object versioning and configuration management, while Google Cloud Storage is relatively cheaper and delivers faster speeds. Therefore, it’s best to choose the tool that suits your storage purposes bearing in mind the above factors.

For more cloud tips like these, read our blog!

Avatar for Dennis Muvaa
Dennis Muvaa

Dennis is an expert content writer and SEO strategist in cloud technologies such as AWS, Azure, and GCP. He's also experienced in cybersecurity, big data, and AI.

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