Cloud-Native vs Cloud-Enabled: What’s The Difference

Are you tired of your engineering team banging their heads against the wall, trying to figure out how to build robust, scalable, and cost-effective multi-tenant analytics for your SaaS application? If so, you’re not alone. In today’s cloud-driven world, where agility and data-driven insights are the keys to success, the struggle to keep up with the ever-changing landscape of cloud technologies is real.

But fear not, because we’re about to embark on a journey that will not only shed light on the mystical world of cloud-native and cloud-enabled architectures but also provide you with a roadmap to conquer the multi-tenant analytics beast, once and for all.

Imagine a world where your engineering team can effortlessly build and deploy highly scalable, resilient, and cost-optimized analytics solutions, without sacrificing performance or vendor lock-in. A world where your customers can access real-time, personalized insights tailored to their unique needs, while your organization reaps the benefits of increased agility, developer productivity, and a future-proof architecture.

What Does Cloud-Native Mean?

The term “cloud-native” refers to an approach to building and running applications that takes full advantage of the cloud computing model. Cloud-native applications are designed and architected specifically for the cloud environment, leveraging cloud services and infrastructure to achieve optimal performance, scalability, and resilience.

Key characteristics of cloud-native applications include:

1. Microservices Architecture: Cloud-native applications are built using a microservices architecture, where the application is broken down into smaller, independent services that can be developed, deployed, and scaled independently.
2. Containerization: Cloud-native applications are typically packaged and deployed using containers, such as Docker, which provide a consistent and lightweight runtime environment across different infrastructures.
3. Automation and DevOps: Cloud-native development relies heavily on automation and DevOps practices, such as continuous integration and continuous deployment (CI/CD), to enable rapid and frequent application updates and deployments.
4. Cloud-Native Services: Cloud-native applications leverage cloud-native services, such as managed databases, messaging queues, and serverless computing, to offload undifferentiated heavy lifting and focus on delivering business value.
5. Resilience and Scalability: Cloud-native applications are designed to be resilient and highly scalable, taking advantage of cloud infrastructure capabilities like auto-scaling, load balancing, and self-healing to ensure high availability and efficient resource utilization.
6. Declarative Configuration: Cloud-native applications often use declarative configuration files (e.g., Kubernetes manifests) to define the desired state of the application and its infrastructure, enabling automated deployment and management.

Benefits of adopting a cloud-native approach include:

1. Increased Agility: Cloud-native architectures enable faster development and deployment cycles, allowing organizations to quickly respond to changing market demands and customer needs.
2. Scalability and Efficiency: Cloud-native applications can scale up or down automatically based on demand, optimizing resource utilization and reducing costs.
3. Resilience and Availability: The distributed nature of cloud-native architectures, combined with self-healing and load-balancing capabilities, ensures high availability and fault tolerance.
4. Portability and Vendor Independence: Cloud-native applications can be deployed across multiple cloud providers or on-premises environments, reducing vendor lock-in.
5. Cost Optimization: By leveraging cloud-native services and pay-as-you-go pricing models, organizations can optimize their costs and avoid overprovisioning resources.
6. Improved Developer Productivity: Cloud-native tools and practices, such as containerization and automated pipelines, streamline development workflows and reduce operational overhead.

Cloud-enabled vs cloud-native

Cloud-Enabled:

Cloud-enabled refers to traditional applications or systems that are designed to take advantage of cloud services or infrastructure to some extent, but are not built specifically for the cloud from the ground up. Cloud-enabled applications may use cloud services for certain aspects like data storage, backup, or content delivery, but their core architecture and design may still follow a more traditional, monolithic approach.

Cloud-Native:

As mentioned earlier, cloud-native refers to applications and systems that are designed and architected from the ground up to fully leverage and take advantage of the cloud computing model. Cloud-native applications are built using modern architectural patterns like microservices, containerization, and DevOps practices, and they are designed to be highly scalable, resilient, and available by leveraging cloud-native services and infrastructure.

The key differences between cloud-enabled and cloud-native applications are:

1. Architecture: Cloud-enabled applications are often monolithic or have a more traditional architecture, while cloud-native applications are built using a microservices architecture, with each service independently deployable and scalable.
2. Portability: Cloud-native applications are designed to be portable across different cloud providers or on-premises environments, while cloud-enabled applications may have dependencies or tight coupling with specific cloud services or infrastructure.
3. Scalability and Resilience: Cloud-native applications are designed with scalability and resilience as core principles, leveraging cloud infrastructure capabilities like auto-scaling, load balancing, and self-healing. Cloud-enabled applications may have limited scalability and resilience capabilities.
4. Automation and DevOps: Cloud-native applications heavily rely on automation and DevOps practices like continuous integration and deployment (CI/CD), while cloud-enabled applications may have more manual processes for deployment and management.
5. Utilization of Cloud Services: Cloud-native applications are built to fully leverage cloud-native services like managed databases, messaging queues, and serverless computing. Cloud-enabled applications may use some cloud services, but their core functionality is often not tightly integrated with cloud-native services.

As an embedded analytics solution that simplifies offering self-service analytics for SaaS companies, Qrvey was born cloud-native and takes advantage of cloud enabled machine learning and AI services that enable our customers to create embedded analytics applications in a self-service fashion.

We serve as an orchestration layer over these microservices that simplifies access and empowers a range of users (whether you are a data analyst, a data scientist or just a data enthusiast) to take advantage of them without having a dedicate engineering team with specific skills that would normally be required to create analytics applications.

On AWS specifically, users can access Amazon Comprehend for Natural Language Processing (NLP) and text analysis to Amazon Rekognition for image and video analysis and Amazon Textract for text extraction from scanned documents to Amazon Sagemaker for machine learning model building and many more.

Our platform allows our customers to spend less time in development and more time delivering real-value to their customers.

With Qrvey, you can build less and deliver more.