How to Choose the Right Cloud‑Native Database: A Maturity Model Guide

Evolution of Cloud Service Models

Traditional cloud offerings are classified as Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS). After the emergence of cloud‑native technologies, new variants such as Container as a Service (CaaS) and Function as a Service (FaaS) provide richer self‑service capabilities, clearer responsibility boundaries, and more flexible orchestration of cloud resources.

The diagram shows how cloud‑native components (blue) are managed by users while the underlying cloud provider (gold) supplies the foundational infrastructure.

Cloud‑Native Database Maturity Model

Bill Wilder defined a cloud‑native application as any software that fully exploits the capabilities of a cloud platform. Under this definition, IaaS and PaaS are merely "cloud‑ready" because they require the application to be adapted, whereas CaaS, SaaS and FaaS represent true cloud‑native solutions.

The maturity model visualises four levels, using CaaS as “Kubernetes‑native”, SaaS as “managed service”, and FaaS as “serverless”. The baseline (Level 0) is the traditional IaaS/PaaS deployment.

Level 0: Cloud‑Ready Data

Any workload that can be packaged into a virtual machine (VM) and run on IaaS is considered cloud‑ready. This often means a monolithic application with an embedded database deployed on one or more VMs. It is a valid migration path but does not fully exploit cloud‑native benefits.

Level 1: Kubernetes‑Based Runtime

Enterprises break monoliths into micro‑services and run them in containers. While Docker and Docker‑Compose are suitable for development, production requires orchestration (e.g., Kubernetes) to provide lifecycle management, high availability and scaling.

Level 2: Managed Data Service (DBaaS)

Running a database inside containers is insufficient for cloud‑native requirements. To reach a true DBaaS level, operators must add automation for scaling, backup, restore, upgrades, monitoring and observability. Open‑source projects such as cass-operator and K8ssandra implement these functions for Cassandra on Kubernetes.

Beyond DBaaS, enterprises often need “Data as a Service”. Projects like Stargate expose a RESTful and GraphQL API, allowing developers to interact with data using familiar HTTP patterns.

Level 3: Serverless Data Model

Even with managed services, cost optimisation remains a challenge. Serverless (FaaS) approaches decouple compute from storage, enabling fine‑grained scaling of individual read/write functions. This model maximises resource utilisation, supports multi‑tenant workloads, and shifts the focus from “Can my database run on Kubernetes?” to “How can I achieve the lowest cost for my specific workload?”.

Conclusion

Applying cloud‑native design principles across the stack is the most effective way to realise cloud‑native databases. The maturity model—spanning CaaS, SaaS and FaaS—offers a practical methodology for leveraging data in the cloud, while open‑source projects such as K8ssandra and Stargate accelerate adoption and enable enterprises to advance their data‑architecture maturity.

Original article: https://containerjournal.com/topics/a-maturity-model-for-cloud-native-databases/