Responsive is a stream processing solution that enhances Kafka Streams applications by separating state from compute, automating operations, and providing robust observability. It addresses issues of rebalance instability and resource management, enabling seamless scaling and reducing state storage costs by up to 7x.
Funding
$4.9M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Kafka Streams applications face challenges related to rebalancing instability, resource management, and the high cost of state storage, hindering seamless scaling and efficient operations. Traditional architectures tightly couple state storage with compute, leading to operational complexities and increased infrastructure expenses.
Solution
Responsive is a stream processing solution designed to enhance Kafka Streams by decoupling state from compute, automating operational tasks, and providing comprehensive observability. By separating these critical components, Responsive eliminates rebalance instability and enables effortless scaling to handle terabytes of state. The platform's control plane automatically optimizes Kafka Streams infrastructure based on real-time metrics, while its RS3 storage solution reduces state storage costs by up to 7x. Responsive maintains full compatibility with existing Kafka Streams code, allowing for a smooth transition and immediate benefits.
Target Audience
The primary target audience includes enterprises and developers utilizing Kafka Streams for event-driven applications who require a more scalable, reliable, and cost-effective stream processing solution.
Features
- Disaggregated state architecture, separating storage and compute for improved scalability and resilience
- Autoscaling policies that declaratively specify infrastructure requirements based on real-time metrics
- RS3 storage, a cloud-native streaming storage service that reduces state storage costs
- Asynchronous processing for parallelizing high-latency operations and maximizing throughput
- Time-to-Live (TTL) functionality for limiting storage space and managing data retention
- Metrics API for integrating with existing Application Performance Monitoring (APM) systems
- Java SDK for seamless integration with existing Kafka Streams applications
- Support for all major Apache Kafka broker implementations, whether cloud-based or on-premise