ScyllaDB Reviews & Product Details

The performance is really impressive - we're seeing much lower latency and way better throughput compared to what we had before. The fact that it's compatible with Cassandra made our migration pretty smooth, and honestly, the speed improvements alone made it worth the switch. Review collected by and hosted on G2.com.

It took us a good bit of time to get comfortable with it, especially coming from a simpler database setup. The docs are okay but sometimes feel a bit sparse - we spent way too much time googling things during our initial deployment. Would love to see more practical examples and troubleshooting tips for when things don't go as planned. Review collected by and hosted on G2.com.

I like ScyllaDB's performance, especially because it's impacted by its indexing and the whole database being written in C++, which serves as the building block for a faster NoSQL DB. It's great for storing our ERP system, especially as we introduce new features and are unsure about the schema size. This NoSQL wide column structure allows us to store a wide range of data. Performance is great compared to MongoDB, and it has very low latency. We experienced a decrease in latency while debugging through the log. Review collected by and hosted on G2.com.

I find ScyllaDB's UI to be very minimal. It doesn't have native integration for cloud functions like MongoDB or Firebase. Also, live streaming requires extra setup compared to Firebase. Review collected by and hosted on G2.com.

In Blockchain, we deal with a massive amount of transaction data. ScyllaDB handles high-speed writes and reads without slow down for storing our transactional data fron ethereum(other Blockchains) chain to our local storage just to make it more acessible and faster. Review collected by and hosted on G2.com.

To get the absolute best results, you need to understand how to model your data correctly (partition keys and clustering keys). Review collected by and hosted on G2.com.

I like ScyllaDB for its significant cost savings and compatibility with Cassandra. The operational simplicity and robust ecosystem make it efficient to use. I value the high performance on fewer nodes, which reduces infrastructure costs due to CPU optimization and the ability to work with smaller clusters. The auto-tuning and self-optimization features help cut down on manual DBA overhead. I also appreciate the strong community and OEM support, which reduce downtime risks and facilitate adoption across production environments. Additionally, I found the initial setup to be very easy, especially with managed services, and the process is familiar for Cassandra users but simplified for new users, particularly with automated tuning scripts. Review collected by and hosted on G2.com.

The learning curve around the shard-per-core architecture and tuning is steep for new team members who are not familiar with it. Monitoring integration and developer SDK still lag behind other databases like Cassandra. Although ScyllaDB's native monitoring stack with Prometheus and Grafana works well, it requires manual setup and configuration compared to managed DB services like Azure and AWS, and using popular observability tools like New Relic or DataDog with pre-built alerting templates for common failures would be much better than having to write numerous queries in Prometheus. Review collected by and hosted on G2.com.

I love how the ScyllaDB community is rising right now. I also love how they have a lower cost, especially as a startup or small organization where we are very cost stringent and expect a lot more features from database systems. The initial setup of ScyllaDB was very easy for me. The documentation is great to refer to, and I just created a cluster and deployed it on ScyllaDB Cloud easily. Review collected by and hosted on G2.com.

Right now, it's a simple database implementation I have done, and, we still have to migrate. We have the backup data from Supabase, and, we have to implement it yet on ScyllaDB. So I think I would need time to give a better answer for that. Review collected by and hosted on G2.com.

I like ScyllaDB's performance-oriented architecture and its compatibility with existing Cassandra tooling, which makes it efficient and scalable. Its focus on efficiency and scalability stands out as key strengths for me. I appreciate how it allows for familiar APIs and workflows to be reused without major changes during evaluations. It's great for evaluating system disparities, ensuring low latency and predictable behavior. Its efficient use of underlying hardware and ease of integration into distributed data architectures needing to grow while maintaining consistent performance makes it valuable. Review collected by and hosted on G2.com.

One idea could be, the initial learning curve, especially for people who are new to the distributed database or performance tuning concepts. Some configurations could use some work on that. And establish clearer guidance. Review collected by and hosted on G2.com.

What I like most about ScyllaDB is its performance and efficiency. It handles very high throughput with low latency, which is critical for real time and large scale data workloads. I also appreciate that it stays compatible with the Cassandra ecosystem, so teams can adopt it without major changes. The way it uses hardware resources efficiently makes operations much simpler. Review collected by and hosted on G2.com.

One thing I find challenging with ScyllaDB is the learning curve when it comes to tuning and operating it at scale. While it delivers strong performance, teams still need a good understanding of distributed systems to get the most out of it. Review collected by and hosted on G2.com.

I use ScyllaDB for building high performance backend systems that need large volumes of data with very low latency. I like its performance and latency, as it can process large numbers of read and write operations while keeping the response time fast. I also like its ability to scale easily. Additionally, it's scalable and compatible with Apache Cassandra APIs, which makes integration with tools and systems much easier. Review collected by and hosted on G2.com.

One thing that could be improved in ScyllaDB is the complexity of the initial setup. For beginners, cluster configuration and tuning can take some time and requires prior knowledge. More beginner-friendly, simplified tools and setup, clearer responses for common deployment scenarios, and automated config options could help developers start more easily. I think more friendly guides could make the process much easier for new users. Review collected by and hosted on G2.com.

I like ScyllaDB's impressive performance and efficiency, delivering very low latency even under heavy workloads. Its excellent use of available hardware allows us to handle large datasets with fewer nodes, which helps in reducing operational costs. The seamless compatibility with Apache Cassandra is also a big plus, as it makes adoption easier while we still benefit from significant performance improvements and scalability. I value how it allows us to keep our APIs responsive and services stable during traffic spikes. Review collected by and hosted on G2.com.

One area that could be improved in ScyllaDB is the operational learning curve. While it performs extremely well once properly configured, tuning and managing clusters can require solid distributed systems knowledge. Some ecosystem tools and documentation are also less mature compared to Apache Cassandra, so troubleshooting or advanced setups may take additional effort. Review collected by and hosted on G2.com.

ScyllaDB delivers extremely low latency and high throughput at scale, which helps keep performance predictable even under heavy load. Its shard-per-core architecture and efficient I/O scheduling allow it to fully utilize modern hardware without constant tuning, and that can reduce the number of nodes we need. I also appreciate that it’s largely Cassandra-compatible while being much faster, so you get a familiar data model and ecosystem, along with better tail latencies. Review collected by and hosted on G2.com.

ScyllaDB still comes with a learning curve, particularly when it comes to data modeling and capacity planning. If you get the model or the underlying infrastructure wrong, performance can degrade quickly. It also assumes you have solid hardware and that you make careful choices around storage and memory, so it’s less forgiving than simpler databases. Because of that, it can feel like overkill for smaller or more generic workloads. Review collected by and hosted on G2.com.