ScyllaDB Cloud
ScyllaDB, IncExternal reviews
426 reviews
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Fast, Efficient, and User-Centric Database Solution
What do you like best about the product?
I like how open and involved the employees at ScyllaDB are. It just seems like they really care about the user experience and really care about their user base.
What do you dislike about the product?
No obvious issues at this time
What problems is the product solving and how is that benefiting you?
I use ScyllaDB for storing data in an easy-to-retrieve and performant manner. It isnt used by our team yet but its something we've started looking into
Efficient Terminal Commands, Needs Better Usability for Beginners
What do you like best about the product?
I like that ScyllaDB is more flexible for me compared to MongoDB, and the terminal structure and commands are better to use. I also find the teaching at the summit very good because it helps me see my mistakes and learn from them. Using the cqlsh terminal to interact with the cluster efficiently through CQL queries is great, and ScyllaDB's use of shard per core architecture maximizes performance and reduces latency in a distributed database.
What do you dislike about the product?
I think improving the setup guide and helping new users understand the distributed database concept. A clear dashboard-like insight to help developers in observability tools and expanding integration with cloud native ecosystems can make it more powerful for developers.
What problems is the product solving and how is that benefiting you?
I find using the cqlsh terminal efficient for interacting with clusters, and ScyllaDB maximizes performance with its shard per core architecture to reduce latency.
SkyllaDB’s Short TTL Feature Is Great for Short-Term Vector Indexing
What do you like best about the product?
SkyllaDB’s short TTL feature is helpful when you need a short-term memory vector search.
What do you dislike about the product?
I didn't go through fully to comment on this
What problems is the product solving and how is that benefiting you?
Documentation help from all the available intellectual property of the organization.
Efficient DB with High Performance, Minor Learning Curve
What do you like best about the product?
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.
What do you dislike about the product?
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.
What problems is the product solving and how is that benefiting you?
ScyllaDB addresses high throughput and low latency challenges, maintaining predictable performance at scale. Its performance-oriented architecture and compatibility with existing Cassandra tooling enhance efficiency and scalability, helping us evaluate system disparities and fit into scalable distributed data architectures.
Scalable High Performance with Low Latency
What do you like best about the product?
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.
What do you dislike about the product?
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.
What problems is the product solving and how is that benefiting you?
I use ScyllaDB for building high-performance backend systems that handle large data volumes with low latency, efficiently processing many read and write requests even under high traffic, keeping the system fast and reliable as data and users grow.
Highly Scalable and Ready to Grow
What do you like best about the product?
Scalability, quick integration, easy to use guide
What do you dislike about the product?
significant operational complexity, a steep learning curve, high infrastructure cost requirements for optimal performance, and limited suitability for transactional or highly relational data
What problems is the product solving and how is that benefiting you?
Currently I don't use ScyllaDb professionally but looking forward to it.
Lightning-Fast and Reliable, Needs Better Indexing
What do you like best about the product?
I find ScyllaDB to be very fast and robust, which is crucial for my analytics and transactions tasks. I really appreciate the speed it offers, and the faster write capabilities make it stand out for me.
What do you dislike about the product?
Indexing
What problems is the product solving and how is that benefiting you?
ScyllaDB provides me speed and robustness for analytics and transactions.
Scalable and Efficient with Compatibility Plus
What do you like best about the product?
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.
What do you dislike about the product?
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.
What problems is the product solving and how is that benefiting you?
I use ScyllaDB as a scalable database for high throughput and low latency needs. It handles large data volumes without bottlenecks, maintains low latency under load, and efficiently manages datasets. Its Cassandra compatibility eases integration while providing predictable performance and better hardware utilization.
ScyllaDB: Predictable Low Latency and High Throughput at Scale
What do you like best about the product?
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.
What do you dislike about the product?
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.
What problems is the product solving and how is that benefiting you?
ScyllaDB addresses our need for consistently low-latency reads and writes on large, write-heavy workloads where traditional relational databases or slower NoSQL options tend to struggle. It allows us to support real-time, high-throughput use cases—such as event ingestion and time-series or key-value access—on fewer nodes. As a result, we can reduce infrastructure costs while still meeting strict SLAs.
Predictable, Ultra-Low Latency Performance at Scale with ScyllaDB
What do you like best about the product?
ScyllaDB delivers extremely low latency and high throughput at scale, so it keeps performance predictable even under heavy load. Its shard-per-core architecture and efficient I/O scheduling let it fully utilize modern hardware without constant tuning, which reduces the number of nodes we need. I also like that it is largely Cassandra-compatible while being much faster, so you get a familiar data model and ecosystem with better tail latencies.
What do you dislike about the product?
ScyllaDB still has a learning curve, especially around data modeling and capacity planning; if you get the model or infrastructure wrong, performance can degrade quickly. It also expects solid hardware and careful storage and memory choices, so it is less forgiving than simpler databases and can feel like overkill for small or generic workloads.
What problems is the product solving and how is that benefiting you?
ScyllaDB solves the need for consistently low-latency reads and writes on large, write-heavy workloads where traditional relational databases or slower NoSQL options would struggle. This lets us handle real-time, high-throughput use cases (like event ingestion and time-series or key-value access) on fewer nodes, lowering infrastructure costs while meeting strict SLAs.
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