Performance Without Compromise
Atomic Fusion delivers dramatically higher throughput and lower end-to-end latency through a fundamentally different streaming architecture.
Stop Designing Around Streaming Limitations
Build applications around what your data needs—not what your streaming infrastructure can handle.
Throughput
Higher single-partition throughput changes more than benchmark results.
Freedom
It gives developers greater freedom to preserve ordering, avoid unnecessary partitioning, and simplify application architectures.
Low Latency
Lower end-to-end latency means faster reactions, shorter feedback loops, and more responsive real-time systems.
Precision Engineering
Fusion removes traditional storage bottlenecks. Each partition draws on the capacity of a distributed storage fleet rather than relying on a small set of broker-local disks. Brokers scale independently, without relocating partition logs. The result is greater throughput, lower latency, and more flexibility in how applications are designed.
One partition. 20× the throughput.
In traditional Kafka architectures, each partition's throughput is constrained by the brokers and disks responsible for storing and replicating its data.
Applications often compensate by increasing partition counts—even when doing so complicates ordering, processing, or data management.
Fusion changes that equation; instead of confining partition storage to a handful of machines, it distributes storage workloads across its entire storage fleet.
In benchmark comparisons, Fusion achieves 20× higher single-partition throughput than a leading managed Kafka service.
This gives developers greater freedom to choose partition counts based on application requirements rather than infrastructure limitations.
10× lower latency. Faster reactions.
In event-driven applications, what matters isn't just how quickly data can be written—it's how quickly that data becomes available to consumers.
Every millisecond between producing an event and consuming it adds delay to the systems that depend on that information.
Fusion is engineered to minimize this end-to-end delay through an optimized streaming data path, distributed storage, and efficient broker-side caching.
In benchmark comparisons, Fusion achieves 10× lower producer-to-consumer latency than a leading managed Kafka service.
For latency-sensitive workloads such as fraud detection, real-time analytics, and operational monitoring, faster delivery means applications can respond to new information sooner.
And Fusion doesn't sacrifice durability for speed. Every acknowledged write has been durably persisted in at least two of three availability zones.
Performance that scales with demand.
Streaming workloads are rarely uniform. Some partitions receive disproportionately high traffic, consumer demand changes, and workloads grow unpredictably.
Fusion separates compute from storage, allowing both to scale independently.
Storage workloads are distributed across the storage fleet, while brokers can be added to increase processing capacity without relocating durable partition data.
This architecture reduces the coupling between partition performance, broker placement, and storage capacity found in conventional Kafka deployments.
Review ArchitecturePerformance you can verify.
Infrastructure performance should be measured, not taken on faith.
Our benchmarks compare Fusion against leading managed Kafka service providers and self-hosted Apache Kafka, evaluating sustained single-partition throughput and producer-to-consumer latency under documented workload conditions.