ScyllaDB vs Cassandra: High-Throughput NoSQL Benchmarked
ScyllaDB vs Cassandra 2026 — CQL compatibility, p99 latency benchmarks, throughput per node, operational cost, and which high-throughput NoSQL fits your workload.
Quick Answer
ScyllaDB 6.x wins on latency and throughput — it delivers 10x lower p99 latency and 3-5x higher throughput than Apache Cassandra on identical hardware using the same CQL API. Cassandra wins for organizations with deep Cassandra operational expertise and existing DataStax support contracts.
ScyllaDB vs Apache Cassandra: Overview
IoT telemetry, time-series at scale, ad-tech, gaming leaderboards, real-time analytics
Open-source AGPL free; ScyllaDB Cloud free trial $300 credits
ScyllaDB Cloud from $0.50/hr (i4i.xlarge); Enterprise license available
Organizations with Cassandra expertise, multi-datacenter replication, DataStax support
Open-source Apache 2.0 free; Astra DB (DataStax) free 5GB
Astra DB Serverless from $0.10/million reads, $0.75/million writes; DataStax Enterprise custom
ScyllaDB vs Apache Cassandra: Feature Comparison
| Feature | ScyllaDB | Apache Cassandra |
|---|---|---|
| p99 write latency | 1-3ms (no GC pauses) | 20-50ms (JVM GC spikes) |
| Throughput per node | 1-2M ops/sec | 100-300K ops/sec |
| CQL compatibility | Full CQL 3.x, same drivers | CQL 3.x native |
| License | AGPL v3 (open-source copyleft) | Apache 2.0 (permissive) |
| Hardware cost for 1M ops/sec | 1-2 nodes (~$1,000-2,000/mo cloud) | 5-10 nodes (~$5,000-10,000/mo cloud) |
| Managed cloud options | ScyllaDB Cloud, Instaclustr | Astra DB (DataStax), Instaclustr, Aiven |
Pros & Cons
ScyllaDB
Pros
- ScyllaDB 6.x achieves p99 latency of 1-3ms vs Cassandra's 20-50ms p99 on the same hardware
- Shard-per-core architecture via Seastar framework eliminates JVM GC pauses — C++ with no stop-the-world
- Throughput of 1-2M ops/sec per node vs Cassandra's 100-300K ops/sec on comparable hardware
- CQL-compatible — existing Cassandra drivers (Datastax Java, Python, Go) connect without changes
- ScyllaDB 6.0 adds strongly consistent transactions via Raft — closing Cassandra's "lightweight transactions" gap
Cons
- AGPL license requires open-sourcing application code if you distribute ScyllaDB as a service
- Smaller community than Cassandra: 12K GitHub stars vs Cassandra's 8K (Apache, broader ecosystem)
- ScyllaDB Cloud managed pricing is premium vs self-hosted Cassandra on commodity hardware
- Some Cassandra-specific features (SASI secondary indexes, full Cassandra materialized views) have partial support
Apache Cassandra
Pros
- Apache 2.0 license — no copyleft obligations, used freely in commercial products and cloud services
- Proven at Netflix (1+ trillion rows), Apple (150,000+ nodes), and Uber at planetary scale
- DataStax Astra DB provides managed Cassandra with a generous 5GB free tier and serverless pricing
- Virtual nodes (vnodes) enable adding/removing nodes without manual token rebalancing
- Cassandra 5.0 (2024) adds unified compact storage, better secondary indexes, and vector search via Storage Attached Indexing
Cons
- JVM garbage collection causes p99 latency spikes of 50-200ms under write pressure — unavoidable with Java
- Requires 3-5x more nodes than ScyllaDB to achieve equivalent throughput — higher hardware cost
- Cassandra's "lightweight transactions" (LWT) use Paxos and are 5-10x slower than regular writes
- Operational complexity: Cassandra requires careful compaction strategy tuning, heap sizing, and GC tuning
Our Verdict: ScyllaDB vs Apache Cassandra
Use ScyllaDB 6.x if you need Cassandra semantics at 3-10x lower hardware cost and cannot accept JVM GC-induced p99 latency spikes — IoT, ad-tech, gaming, and time-series workloads benefit most. The CQL API compatibility means migration is a connection string change for most applications. Use Apache Cassandra if your organization has deep Cassandra operational expertise, existing DataStax support contracts, or requires Apache 2.0 licensing for commercial redistribution without copyleft obligations. Cassandra 5.0's vector search feature is also worth evaluating if you need AI retrieval alongside your wide-column data.
ScyllaDB vs Apache Cassandra — FAQs
How difficult is it to migrate from Cassandra to ScyllaDB?
Migration is straightforward for most workloads because ScyllaDB uses the same CQL wire protocol and is compatible with all Cassandra 3.x/4.x client drivers. The migration process typically involves: (1) snapshot your Cassandra keyspaces, (2) restore snapshots to ScyllaDB using the sstableloader tool, (3) switch application connection strings to ScyllaDB nodes. ScyllaDB's documentation provides a live migration path using dual-write that achieves zero-downtime cutover. The main compatibility gaps are SASI secondary indexes (use ScyllaDB's Materialized Views instead) and some Cassandra-specific compaction strategies.
What causes Cassandra's p99 latency spikes and why does ScyllaDB avoid them?
Cassandra runs on the JVM, which performs periodic stop-the-world garbage collection. Under write pressure, G1GC pauses of 50-200ms are common and cannot be tuned away — they are inherent to JVM memory management. ScyllaDB is written in C++ using the Seastar async framework with a shard-per-core architecture: each CPU core owns its data shard and handles I/O with dedicated threads, eliminating context switching and GC pauses entirely. ScyllaDB also uses a custom memory allocator that avoids heap fragmentation. The result is consistent sub-5ms p99 latency even at 90% CPU utilization.
Is ScyllaDB used in production at large scale in 2026?
Yes. ScyllaDB's notable production deployments include Discord (storing 1 trillion+ messages, migrated from Cassandra in 2022), Comcast, Ola Cabs, and Grab. Discord's public post-mortem described a 6x reduction in nodes and significantly lower p99 latency after migration. Fanatics (sports merchandise) uses ScyllaDB for real-time inventory management at peak event loads. The company reports 1,500+ enterprise customers in 2026. For comparison, Cassandra's flagship deployments are at Netflix, Apple, and Uber — organizations with large enough engineering teams to manage Cassandra's operational complexity.
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