The 2025 web database list: A retrospective

In technology, predictions age quickly. This article originally listed fifteen databases to watch for 2025. Looking back, it is a useful snapshot of the landscape at the time, but a simple list of features is not enough to make a critical technical decision. The core question is not what a database *can* do, but what it *costs* you in terms of performance, complexity, scalability and operational overhead.

This updated analysis revisits the original list. We will examine the specific problems each database solves and, more importantly, the trade-offs inherent in choosing it. The goal is to give you a framework for deciding which database fits the unique constraints of your project, your data and your team.

The trade-off is significant cost and complexity. The licensing fees are substantial, and it requires specialised database administrators. For most new web applications, its resource footprint and operational overhead make it a non-starter.

### 2. MySQL MySQL is a default choice for relational data, powering a large portion of the web. Its strengths are its reliability, ease of use and the massive community supporting it. It is a solid, predictable foundation for content management systems, e-commerce platforms and many general-purpose web applications.

The trade-off is that while it is a workhorse, it can require more manual effort for sharding and scaling compared to newer, distributed SQL systems. Its feature set is also less extensive than PostgreSQL's out of the box.

### 3. MS SQL Server If your organisation runs on the Microsoft stack, SQL Server is the logical choice. Its integration with .NET, Azure services and Visual Studio is excellent. The tooling is mature and provides a productive development experience.

The trade-off is vendor lock-in and cost. While it now runs on Linux, it is most at home on Windows and in Azure. Licensing outside of the cloud can be expensive, and it is less portable than open-source alternatives.

### 4. MongoDB MongoDB's document model is a strong fit for applications with unstructured or rapidly changing data schemas. It maps naturally to objects in application code, which can accelerate development. We have seen its utility in projects like the Jhumru food ordering platform.

The trade-off is in its consistency model. While it now supports multi-document ACID transactions, its primary design favours flexibility over the rigid transactional integrity of a traditional RDBMS. This requires discipline in your application design to maintain data quality.

### 5. PostgreSQL PostgreSQL has become a favourite for developers who need more than a basic relational database. Its support for advanced data types, powerful indexing (including JSONB) and extensibility make it highly versatile. It is a strong choice for complex applications, geospatial services and analytical workloads.

The trade-off is a slightly steeper learning curve than MySQL. Its performance is excellent, but tuning it can be more complex for teams without prior experience.

### 6. IBM DB2 DB2 comes from a legacy of mainframe computing, where absolute reliability is paramount. You would use it in financial services or other large enterprises with existing IBM infrastructure.

The trade-off is that it is a niche technology in the modern web development landscape. Finding developers with current DB2 skills for a new web project is difficult and expensive compared to finding PostgreSQL or MySQL expertise.

### 7. Cassandra Cassandra is built for massive, geographically distributed datasets with high write throughput. Its masterless architecture means there is no single point of failure, giving you high availability. It is a fit for IoT data collection, activity logs and messaging systems.

The trade-off is eventual consistency. Data written to one node takes time to propagate. If your application requires immediate, strong consistency for all reads, Cassandra is the wrong tool.

### 8. Redis Redis is exceptionally fast because it stores data in memory. Its primary use is for caching, real-time leaderboards, and managing user sessions. It is a component you add to an architecture for speed, not a standalone database of record.

The trade-off is its reliance on RAM. Your entire dataset must fit in memory, which can be costly. While it offers persistence options, it is not designed for the same level of durability as a disk-based database like PostgreSQL.

### 9. Elasticsearch Elasticsearch is a search engine, not a general-purpose database. It excels at full-text search, log analysis and monitoring. If you need to search large volumes of text or aggregate machine-generated data, it is a powerful tool.

The trade-off is complexity. Using it for simple CRUD operations is overkill. It is a specialised system that solves a specific problem, and should be used alongside a primary database.

### 10. MariaDB MariaDB was created by the original developers of MySQL as a community-driven fork. It serves as a drop-in replacement for MySQL, offering protocol compatibility while introducing some different features and performance optimisations.

The trade-off is a smaller commercial ecosystem. While technically robust, the market for third-party tools, hosting and enterprise support is smaller than MySQL's, which can be a factor for some organisations.

### 11. SQLite SQLite is a self-contained, serverless database engine. It is embedded directly within an application. This makes it a perfect choice for mobile apps, desktop software or simple websites with low concurrency needs. We use it in some mobile app projects.

The trade-off is that it does not handle high write concurrency. It is designed for a single writer at a time, making it unsuitable for most multi-user web applications.

### 12. OrientDB OrientDB is a multi-model database, combining graph, document and key-value models in one engine. You might choose it if your data has complex relationships but also benefits from document-style flexibility, and you want to avoid managing two separate systems.

The trade-off is that it can be a master of none. A dedicated graph database like Neo4j will often outperform it on graph workloads, and a dedicated document database like MongoDB has a more mature ecosystem.

### 13. DynamoDB Amazon's DynamoDB is a fully managed NoSQL database that offers predictable latency at nearly any scale. As a serverless offering, it removes operational overhead. It is ideal for applications built on AWS that need to scale rapidly.

The trade-off is vendor lock-in and a restrictive query model. You must design your data access patterns upfront. If you get it wrong, queries can be inefficient and costs can escalate unexpectedly.

### 14. Firebird Firebird is a lightweight, open-source relational database with a long history. It offers good concurrency and a small footprint, making it viable for embedded systems or applications where resources are constrained.

The trade-off is its small community and market share. Finding documentation, modern tooling and experienced developers is significantly harder than for mainstream databases like PostgreSQL.

### 15. Neo4j Neo4j is the leading graph database, designed specifically to store and traverse relationships. It is the right choice when the connections between your data points are as important as the data itself, such as in social networks, fraud detection or recommendation engines.

The trade-off is its specialisation. If your data model is not a graph, using Neo4j is an anti-pattern. It is not a general-purpose database and should be used for the problem it was built to solve.

At Excelsior Technologies, we build custom web and mobile applications. Selecting the correct data store is a critical part of our design process. If you are assessing these trade-offs for a new project, we can help you navigate them.