Teams choose ScyllaDB when MongoDB fails to deliver consistent performance at scale. MongoDB suffers from complex horizontal scaling, inefficient memory management and an awkward primary-secondary architecture. ScyllaDB is architected for consistent speed and simplicity for all workloads, no matter the scale.
ScyllaDB’s achieves single-digit millisecond P99 latency, ensuring that your applications are responsive and perform even under heavy loads.
ScyllaDB’s architecture is designed to handle large-scale deployments with ease, providing linear performance compared to MongoDB as the workload increases.
ScyllaDB allows you to add or remove nodes as needed to accommodate changing workloads with partitioned data automatically.
ScyllaDB autotunes itself for your workload and fully maximizes your hardware’s power. This results in lower cost of ownership compared to MongoDB.
ScyllaDB is highly fault-tolerant with no single point of failure. It’s simple for users to set up and manage an always-on topology.
All ScyllaDB nodes are equal. You don’t need to provision specialized replica sets just to expand your write capacity. Get the most value out of your infrastructure.
The results were overwhelming. With similar infrastructure costs, we achieved much better latency and capacity; the decision was clear and validated. 
benchANT’s Dr. Daniel Seybold compares ScyllaDB vs MongoDB with respect to features, architectures, performance, and scalability.
Tractian needed to upgrade their real-time machine learning environment to support an aggressive increase in data throughput. Benchmarking at 160,000 OPS, they found that ScyllaDB could support a 10X increase in throughput with 17X lower latency than MongoDB – with similar infrastructure costs.
Augury originally built their predictive services on top of MongoDB, but as the company grew, the dataset reached the limits of MongoDB. They migrated their analytics use cases to ScyllaDB, and also discovered that ScyllaDB could support an OLTP “machine trends dashboard” use case with millisecond response times.
For Numberly’s real-time ID matching use case, MongoDB’s primary/secondary architecture could not sustain the required write throughput and meet latency SLAs. By replacing a 15-node MongoDB cluster with a 3-node ScyllaDB one, they meet SLAs with less hassle & cost
In 2015, Discord messages handled 100M messages, all stored on a single MongoDB replica set. As they expanded to billions of messages, they tried using Cassandra, but they ultimately moved to ScyllaDB as they reached the trillion message threshold.
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