EDB Postgres AI for CloudNativePG Reviews & Product Details

One of the main things I like about EDB Postgres AI for CloudNativePG is its seamless integration with Kubernetes-native processes. Dealing with PostgreSQL clusters, backups, failovers, and scaling has become way easier since these processes can be carried out with the help of Kubernetes objects. This tool has proved to be really helpful for me since it allows me to run database operations reliably without managing everything myself. Review collected by and hosted on G2.com.

However, the main limitation with CloudNativePG is that it might be hard to use by teams which have not operated PostgreSQL on Kubernetes before, due to a need to get familiar with configurations, operators, backup, failover and recovery process, among other aspects. The integration with various existing tools might also be necessary, which means more work to do. Review collected by and hosted on G2.com.

EDB Postgres AI for CloudNativePG provides a strong combination of PostgreSQL capabilities, AI/ML support, and cloud-native database management. I particularly like its scalability, flexibility, and ease of managing PostgreSQL workloads in Kubernetes environments. It helps simplify database operations while providing a reliable platform for modern cloud-native applications. Review collected by and hosted on G2.com.

The main limitation is that some advanced features may require additional learning and configuration, especially for teams that are new to cloud-native PostgreSQL environments. Documentation and setup could be more straightforward for certain use cases. Overall, these are relatively minor concerns compared with the benefits and capabilities of the platform. Review collected by and hosted on G2.com.

What I like most is how seamlessly it brings truly Kubernetes-native practices into database operations. Being able to manage high availability, continuous backups, and rolling updates declaratively through Custom Resource Definitions (CRDs) makes running Postgres feel like any other containerized workload in our GitOps pipeline. Review collected by and hosted on G2.com.

The configuration overhead and learning curve can be steep when setting up advanced enterprise features. During initial deployments, navigating the interplay between CloudNativePG CRDs, EDB’s enterprise-specific licensing and extensions, and Kubernetes storage class nuances can feel unnecessarily complex and hard to untangle. Review collected by and hosted on G2.com.

EDB has been productive overall for my use. I like most of the features, including the automated failover engine. It also has the ability to manage Pods and PVCs directly without using custom APIs, and it simply feels better than traditional Kubernetes. Review collected by and hosted on G2.com.

For me, the only things that have been difficult are the initial time investment. While the initial setup is seamless and easy, the other operations are more complex and require some learning before even getting started with them. Also, the infra cost for low latency is a bit higher. Review collected by and hosted on G2.com.

The simplicity of the UI is worth noting. Also, because the platform supports a wide range of integrations with other apps and APIs, it really helps with automation. Review collected by and hosted on G2.com.

I assume the pricing might be better for individual users. AI part for some cases fails to match my expectations. Lastly, support process is needed to be optimized. Review collected by and hosted on G2.com.

What I like best is the TDE and near-zero-downtime setup. Encryption for data at rest comes built in, so I didn't have to add it separately. And the failover/maintenance work happens without the downtime I used to deal with before. Review collected by and hosted on G2.com.

What I dislike is the extra complexity that comes with the enterprise layer. CloudNativePG by itself is simple, but once you add EDB's enterprise features on top, there's more to configure and manage, and it takes more time to get used to compared to a plain open-source setup. Review collected by and hosted on G2.com.

I like how CloudNativePG handles PostgreSQL operations in a Kubernetes environment without requiring us to manage every operational task manually. In my experience, the biggest benefit has been having PostgreSQL configuration and lifecycle management handled through Kubernetes resources instead of maintaining separate manual procedures. This makes deployments and configuration changes more consistent across environments. I also found the automation around backups, failover, and high availability useful because it reduces the amount of routine database administration required from the engineering team. From a day-to-day operations perspective, the Kubernetes-native approach makes it easier to manage PostgreSQL alongside the rest of our containerized workloads. Review collected by and hosted on G2.com.

The main area I found challenging was the initial setup and configuration, especially when working with Kubernetes concepts and PostgreSQL-specific settings together. Some troubleshooting scenarios can also require a deeper understanding of both CloudNativePG and the underlying Kubernetes resources. I think clearer documentation around common configuration issues, troubleshooting examples, and monitoring would make the learning curve easier for new users. Once the environment is configured correctly, however, the day-to-day management experience is much smoother. Review collected by and hosted on G2.com.

I like that EDB Postgres AI for CloudNativePG takes care of a lot of the operational work, especially around high availability and backups, so I can spend less time managing the database. Everything works pretty well with it, and the initial setup was so easy, which makes it even more enjoyable to use. Review collected by and hosted on G2.com.

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Oracle compatibility and declarative database management fit cleanly into modern GitOps/CI-CD workflows. For me, it’s the combination of deep Postgres expertise baked into a truly Kubernetes-native operator, rather than a Postgres tool that feels awkwardly adapted for containers. Review collected by and hosted on G2.com.

Logical replication during major upgrades still has real gaps. Although the upgrade path has improved, there are notable limitations, so schema changes, sequences, and LOBs often need to be handled manually as part of the upgrade process.

EDB Postgres Extended is also notably restricted for teams running on ARM-based infrastructure, which reduces the available options. These architecture constraints limit flexibility: all nodes in a Kubernetes cluster must use the same CPU architecture, so hybrid CPU-architecture Kubernetes clusters are not supported. Review collected by and hosted on G2.com.

I like the feature where we can build Gen AI application directly on EDB Postgres AI for CloudNativePG. The scaling of AI models within the database is another aspect I appreciate. The Unified workloads feature, allowing for both traditional transactional queries and AI tasks on the same platform, stands out to me. Additionally, I like the hybrid data manager feature. With these capabilities, it's useful for handling growing chatbot data and supports vector search, which is widely used for AI applications. Review collected by and hosted on G2.com.

It's very difficult to create initial setup and configuration database for AI workloads due to improper documentation, so I'd like to improve the document's content. There are some advanced features in EDB, but due to lack of documentation, we are not aware of how to use them. The PostgreSQL uses one primary writer node with multiple read replicas which becomes a bottleneck for very high write workloads. It should follow similar to Google Spanner so that we scale horizontally. It's very hard to set up initially without any expert in PostgreSQL. The challenge we faced is how to configure replications and containers, and it's very difficult to configure security as well. Review collected by and hosted on G2.com.