12 Best Confluent Alternative Platforms in 2025

Confluent has long been a dominant force in the Kafka ecosystem, offering a robust platform for stream processing and data integration.Confluent has long been a dominant force in the Kafka ecosystem, offering a robust platform for stream processing and data integration. However, the data landscape is rapidly evolving. Today's data engineering teams face increasing pressure to deliver faster, more reliable data pipelines while managing ever-tightening budgets. This has led many to seek out a Confluent alternative that better aligns with modern requirements for operational simplicity, cost-efficiency, and specialized use cases like real-time analytics and Change Data Capture (CDC).

The reasons for exploring alternatives are diverse. Some teams find Confluent's pricing model to be complex and unpredictable, leading to significant cost overruns as data volumes scale. Others grapple with the operational overhead of managing its extensive feature set, much of which may go unused. For organizations prioritizing lean infrastructure or specific functionalities like serverless operations or Kafka-compatible storage engines, a more specialized solution often provides a better fit. The search for a suitable Confluent alternative is about finding the right balance of performance, features, and total cost of ownership for your specific data architecture.

This guide moves beyond surface-level comparisons to offer a deep dive into the 12 best alternatives to Confluent. We will explore managed Kafka services like Amazon MSK and Aiven, innovative Kafka-compatible platforms such as Redpanda and WarpStream, and modern CDC-first solutions like Streamkap. For each platform, you'll find a detailed analysis of its unique strengths, limitations, pricing signals, and ideal use cases, complete with direct links and screenshots. Our goal is to provide a comprehensive resource that helps you make an informed decision for your data stack, whether you're migrating an existing Kafka workload or building a new real-time pipeline from scratch.

1. Streamkap

Streamkap presents a compelling and modern Confluent alternative for organizations aiming to build real-time data pipelines without the operational overhead. It's a fully managed platform that leverages change data capture (CDC) to stream data changes from sources like Postgres, MySQL, and MongoDB into analytical destinations such as Snowflake, Databricks, and BigQuery with sub-second latency. This approach effectively replaces traditional batch ETL processes, enabling genuine real-time analytics and operations.

The core value proposition of Streamkap is its "zero-ops" experience. It abstracts the immense complexity of managing and scaling Apache Kafka and Apache Flink clusters. This allows data teams to focus entirely on building data products and deriving value from their data, rather than on infrastructure maintenance, patching, and firefighting. The platform is engineered for high performance and cost-efficiency, with the company claiming it can deliver up to 15x faster throughput than competitors like Fivetran at a significantly lower cost.

Key Features & Use Cases

Streamkap is particularly well-suited for data engineers and CTOs who prioritize speed, reliability, and cost control. Its architecture is designed to handle demanding production workloads from day one.

• Real-Time Analytics & Dashboards: Power live dashboards for logistics, finance, or e-commerce by streaming operational database changes directly into a data warehouse. This eliminates data freshness gaps common with batch-based pipelines.
• Automated Schema Evolution: One of its most powerful features is the automatic handling of schema drift. When a source database schema changes (e.g., a new column is added), Streamkap detects it and propagates the change to the destination without manual intervention, preventing pipeline failures.
• In-Flight Data Transformations: Users can apply transformations using Python or SQL directly within the pipeline. This is ideal for data masking, PII redaction, JSON unnesting, or creating complex aggregations before the data lands in the warehouse.
• Database Replication & Modernization: Facilitate zero-downtime database migrations or maintain synchronized read replicas for offloading analytical queries from a primary production database.

Real-World Impact: Customer case studies highlight tangible benefits. For instance, one user reported a 4x performance increase and a 3x lower total cost of ownership, while another achieved a 54% reduction in data-related costs after migrating to Streamkap.

Pros & Cons

Getting Started

Streamkap offers a free trial, allowing teams to connect a source and destination to test its capabilities and performance with their own data. The user experience is designed for rapid onboarding, with dozens of pre-built connectors that can be configured in minutes through a straightforward UI. Direct support channels, including dedicated Slack access, ensure that any technical issues are resolved quickly.

Website: https://streamkap.com

2. Redpanda Cloud

Redpanda Cloud offers a fully managed, Kafka API-compatible streaming data platform that distinguishes itself by eliminating the JVM and ZooKeeper. This design choice results in significantly faster startup times and a more streamlined operational experience. For teams seeking a direct replacement for Kafka without the traditional complexities, Redpanda presents a compelling option, making it a noteworthy confluent alternative.

It offers flexible deployment models including Serverless, Dedicated, and Bring Your Own Cloud (BYOC), catering to a wide range of organizational needs and security postures. Because it is a drop-in Kafka replacement, existing applications, connectors, and tools built for the Kafka ecosystem can connect to Redpanda with minimal to no code changes. For a refresher on core Kafka concepts, you can explore this guide on what Kafka is.

Key Features and Considerations

• Deployment Flexibility: Choose between a hands-off serverless experience, dedicated hardware for performance isolation, or a BYOC model for data sovereignty within your own AWS or GCP environments.
• Performance: The platform is built in C++ for high throughput and low-latency performance, aiming to provide a better price-to-performance ratio than traditional Kafka setups.
• Transparent Pricing: Redpanda Cloud provides clear, usage-based pricing with published tiers for throughput and partitions, which helps in predictable cost management. Billing is also available through AWS and GCP marketplaces.

Website: https://www.redpanda.com/redpanda-cloud

3. Amazon Managed Streaming for Apache Kafka (Amazon MSK)

Amazon MSK is AWS's fully managed service for Apache Kafka, designed for teams deeply integrated into the AWS ecosystem. It provides a native, cloud-based solution for running Kafka clusters without the burden of managing the underlying infrastructure. By handling tasks like server provisioning, patching, and high-availability configuration, MSK allows developers to focus on building streaming applications, positioning it as a strong confluent alternative for organizations committed to AWS.

The service offers two primary modes: Provisioned Clusters for fine-grained control over broker instances and storage, and Serverless for automatic scaling based on workload demands. With deep integrations into AWS services like IAM for security, CloudWatch for monitoring, and VPC for private networking, MSK provides a secure and cohesive experience within a company's existing cloud environment. Its MSK Connect feature also simplifies running Kafka Connect connectors without needing a separate cluster.

Key Features and Considerations

• Deep AWS Integration: Natively connects with AWS security, networking, and monitoring services, creating a seamless operational experience for existing AWS users.
• Flexible Operational Modes: Users can choose between Provisioned mode for predictable workloads and maximum control or Serverless for variable workloads that require automatic scaling.
• Managed Connectors: MSK Connect allows you to run fully managed Kafka Connect connectors, simplifying data integration tasks from various sources and sinks without managing a separate connect cluster.
• Complex Cost Structure: Pricing can be difficult to predict, as it involves charges for broker instance hours, storage, data transfer between availability zones, and partition hours (in Serverless mode).

Website: https://aws.amazon.com/msk

4. Aiven for Apache Kafka

Aiven for Apache Kafka provides a fully managed, open-source streaming platform that excels in multi-cloud flexibility. It allows teams to deploy and manage Kafka clusters across major cloud providers like AWS, GCP, and Azure, often with a single click. This makes it an ideal confluent alternative for organizations committed to a multi-cloud strategy or seeking to avoid vendor lock-in while leveraging a robust, off-the-shelf Kafka experience.

The platform simplifies operations by bundling key ecosystem tools like Kafka Connect and Karapace (for schema registry) directly into its offering. Aiven’s inclusive pricing model, which often incorporates networking costs, provides greater cost predictability compared to other providers where networking can be a significant and variable expense. To better understand the benefits of such platforms, you can find more information about the managed Kafka landscape here.

Key Features and Considerations

• Multi-Cloud and Multi-Region: Deploy your Kafka clusters on the cloud provider and region of your choice, enabling data sovereignty and disaster recovery strategies with ease.
• Integrated Tooling: Aiven includes managed Kafka Connect and a schema registry, reducing the operational burden of managing these critical components yourself.
• Transparent Pricing: Offers clear, all-inclusive list pricing on many plans, which simplifies budgeting by bundling costs like data transfer. They also provide free startup credits and migration services.
• High Availability: Aiven guarantees a 99.99% uptime SLA, making it a reliable choice for mission-critical applications that cannot tolerate downtime.

Website: https://aiven.io/kafka

5. Google Cloud Managed Service for Apache Kafka

Google Cloud Managed Service for Apache Kafka is a fully managed, open-source compatible Kafka service deeply integrated into the Google Cloud Platform (GCP) ecosystem. It offers a native experience for organizations already invested in GCP, providing seamless connectivity to services like BigQuery and Google Cloud Storage. For teams looking for a managed Kafka solution that leverages their existing GCP commitments and simplifies infrastructure management, it serves as a strong confluent alternative.

The service runs open-source Apache Kafka and includes support for Kafka Connect, allowing users to deploy connectors for various data sources and sinks. Security is addressed through Private Service Connect (PSC) endpoints, enabling private and secure connections to Kafka clusters from within a Virtual Private Cloud (VPC). This approach ensures that data traffic does not traverse the public internet, a key requirement for many enterprises.

Key Features and Considerations

• Native GCP Integration: Offers out-of-the-box integration with core GCP services, simplifying data pipelines for analytics, machine learning, and archival use cases within the same cloud environment.
• Cost Management: Provides a published pricing model based on Data Compute Units (DCUs) and storage. It supports spend-based committed use discounts (CUDs), allowing organizations to reduce costs in exchange for long-term commitments.
• Private Networking: Uses Private Service Connect for secure, private connectivity, although it’s important to note that PSC endpoints incur their own per-hour and per-GiB data processing charges.
• Tiered Storage: Automatically moves older data to lower-cost storage tiers, helping to optimize storage costs for long-term data retention without manual intervention.

Website: https://cloud.google.com/products/managed-service-for-apache-kafka

6. Microsoft Azure Event Hubs

Microsoft Azure Event Hubs is a fully managed, real-time data ingestion service that provides a compelling confluent alternative for organizations deeply integrated into the Azure ecosystem. It offers native support for the Apache Kafka protocol, allowing existing Kafka clients and applications to connect and stream data without requiring code changes. This feature makes it an attractive option for teams looking to leverage a managed service without rewriting their existing producers and consumers.

Event Hubs is designed for high-throughput data streaming and simplifies operations by abstracting away the complexities of managing Kafka brokers. It includes robust, Azure-native features like automatic data capture to Blob Storage or Data Lake Storage, as well as geo-disaster recovery capabilities. These integrations provide a seamless experience for building resilient, cloud-native data pipelines within the Azure environment.

Key Features and Considerations

• Kafka Protocol Support: Allows for a lift-and-shift migration for existing Kafka applications, enabling them to connect to an Event Hubs endpoint with only a configuration change.
• Azure-Native Integration: Tightly integrated with the Azure stack, offering built-in features like Capture for long-term data archival and geo-disaster recovery for high availability.
• Tiered Offerings: Provides multiple service tiers (Standard, Premium, Dedicated) to accommodate different performance, isolation, and scalability requirements, from small projects to enterprise-grade workloads.

Website: https://azure.microsoft.com/en-us/pricing/details/event-hubs/

7. StreamNative Cloud

StreamNative Cloud provides a fully managed Apache Pulsar service, positioning itself as a powerful confluent alternative for teams that require more than just traditional event streaming. It uniquely combines stream and message queue semantics in a single platform. Through its Ursa engine, it also offers Kafka-compatible access, allowing existing Kafka applications to migrate with minimal changes.

This platform is particularly well-suited for complex enterprise scenarios involving multi-tenancy, strong isolation, and global geo-replication needs. With native support for lakehouse storage integrations like Apache Iceberg and Delta Lake, StreamNative is built for modern data stacks. It provides both fully-hosted and Bring Your Own Cloud (BYOC) deployment models, offering flexibility for data governance and security requirements.

Key Features and Considerations

• Unified Messaging: Natively supports both streaming and traditional message queuing patterns, which can simplify architecture by eliminating the need for separate systems.
• Kafka Compatibility: The Ursa protocol handler allows Kafka clients to connect to a Pulsar cluster without code modification, easing the transition from a Kafka-based ecosystem.
• Multi-tenancy and Geo-replication: Designed from the ground up for multi-tenant environments with robust isolation. It also features built-in, out-of-the-box geo-replication for building resilient, global applications.
• Lakehouse Integration: Offers seamless connectors for tiering data to storage formats like Iceberg and Delta Lake, facilitating analytics and long-term retention.

Website: https://streamnative.io

8. Instaclustr by NetApp

Instaclustr by NetApp provides a fully managed Apache Kafka service aimed at enterprises requiring SRE-led operations and robust support. It offers the flexibility to run managed Kafka and Kafka Connect across major cloud providers like AWS, Azure, and GCP, or even in on-premises data centers. This multi-cloud capability and focus on enterprise-grade reliability make it a strong confluent alternative for organizations that need a vendor-operated platform with specific cloud placement and support guarantees.

The platform allows customers to deploy Kafka clusters either within Instaclustr's own cloud account or directly into their own, providing greater control over data residency and network security. With clearly defined SLA tiers for both production and non-production environments, Instaclustr caters to businesses that prioritize operational stability and predictable performance backed by expert support.

Key Features and Considerations

• Multi-Cloud and Hybrid Deployment: Deploy managed Kafka consistently across AWS, Azure, GCP, or your own on-premises infrastructure, enabling flexible hybrid cloud strategies.
• SRE-Managed Operations: The service is backed by an experienced Site Reliability Engineering (SRE) team, offering proactive monitoring, maintenance, and expert support to ensure cluster health and performance.
• Transparent Cost Estimation: Instaclustr provides a detailed pricing calculator on its website, allowing teams to estimate costs based on cloud provider, instance types, and data volume before committing.

Website: https://www.instaclustr.com/pricing/

9. Upstash Serverless Kafka

Upstash offers a serverless, Kafka-compatible messaging platform specifically designed for serverless and edge computing environments. Its key differentiator is a per-message pricing model and a REST API, which lowers the barrier to entry for developers working in environments like Cloudflare Workers where traditional TCP connections are difficult. This focus on ease of use and cost-effectiveness for smaller or spiky workloads makes it a unique confluent alternative.

The platform is engineered for simplicity, allowing teams to provision a Kafka cluster in seconds without managing servers or complex configurations. Its ability to scale down to zero means users only pay for what they actually use, which is ideal for development, testing, or applications with unpredictable traffic patterns. Upstash also provides serverless Kafka Connectors, simplifying integrations with other data sources and sinks.

Key Features and Considerations

• Serverless and Edge Optimized: Built with a REST API alongside the standard Kafka protocol, making it easily accessible from HTTP-native environments and serverless functions.
• Pay-per-Request Pricing: The pricing model is transparent and based on the number of messages processed, which is highly cost-effective for low-throughput or intermittent workloads. It includes a generous free tier.
• Instant Provisioning: Clusters are created almost instantly, removing the operational overhead typically associated with setting up and managing a Kafka deployment.
• Scales to Zero: Ideal for applications that may have periods of inactivity, as costs are directly tied to usage, eliminating expenses for idle resources.

Website: https://upstash.com/kafka

10. WarpStream

WarpStream is a Kafka-compatible streaming data platform built on a "Bring Your Own Cloud" (BYOC) architecture. Its data plane runs directly on object storage within your own cloud account (like Amazon S3), which significantly reduces data egress costs and gives you complete data sovereignty. This model is engineered to separate compute and storage, allowing for more predictable cost management and making it a compelling confluent alternative for cost-conscious organizations.

The platform is designed for operational simplicity and cost efficiency, eliminating the need to manage complex infrastructure like brokers or ZooKeeper. By leveraging commodity object storage, WarpStream aims to provide enterprise-grade streaming at a fraction of the cost of traditional managed Kafka services. Its pricing model is notably straightforward, avoiding charges per agent, core, or partition, which simplifies budget forecasting for data-intensive workloads.

Key Features and Considerations

• BYOC Architecture: Keeps all your streaming data within your own cloud account's object storage (e.g., S3), giving you full control and drastically cutting egress fees.
• Predictable Pricing: Offers a simple, published pricing model without per-partition or per-agent fees, making costs easier to manage as you scale.
• High Availability: Provides multiple service tiers with published SLAs up to 99.999% and supports multi-region replication for robust disaster recovery strategies.
• Operational Simplicity: As a serverless platform, it handles the underlying infrastructure, allowing teams to focus on building applications rather than managing Kafka clusters.

Website: https://www.warpstream.com

11. Apache Kafka (self-managed)

For organizations that prioritize ultimate control and want to avoid vendor lock-in, self-managing open-source Apache Kafka is the foundational choice. This approach involves deploying and operating your own Kafka clusters, whether on-premises or within your cloud VPC. It provides direct access to the latest features like KRaft, Kafka Streams, and Kafka Connect, offering maximum flexibility in architecture and configuration. This makes it a powerful confluent alternative for teams with strong operational expertise.

Managing Kafka yourself means you are fully responsible for everything from initial setup and networking to scaling, upgrades, security, and 24/7 monitoring. While there are no licensing fees for the software itself, the total cost of ownership includes significant engineering and SRE time. This option is best suited for organizations that have the in-house skills to handle the operational complexities and require granular control over their data infrastructure. For those new to the ecosystem, you can learn more about Apache Kafka connectors.

Key Features and Considerations

• Complete Control: You have full authority over cluster design, hardware selection, versioning, and security policies, with no restrictions imposed by a third-party vendor.
• No License Fees: The software is free to download and use under the Apache 2.0 license, eliminating direct software costs and preventing vendor lock-in.
• Operational Overhead: The responsibility for cluster reliability, performance tuning, disaster recovery, and on-call support falls entirely on your internal teams.

Website: https://kafka.apache.org

12. CloudAMQP (Managed RabbitMQ)

For teams whose use cases are better aligned with traditional message queuing than with Kafka's log-based architecture, CloudAMQP offers a fully managed RabbitMQ service. It excels in scenarios requiring complex routing, task queues, and protocols like AMQP, MQTT, and STOMP. While not a direct Kafka replacement, it serves as a strong confluent alternative for organizations that need a battle-tested message broker with different semantic guarantees.

CloudAMQP simplifies the setup and maintenance of RabbitMQ clusters, providing an intuitive management UI, monitoring tools, and automated scaling. Its support for multiple protocols makes it highly versatile for IoT applications, microservices communication, and web notifications. The platform's fast onboarding and wide range of plan sizes allow teams to get started quickly and scale as their needs evolve, with dedicated and multi-tenant options available.

Key Features and Considerations

• Protocol Support: Native support for AMQP, MQTT, STOMP, and other protocols, making it a flexible choice for diverse application needs beyond typical data streaming.
• Ease of Use: Provides a managed experience with a user-friendly UI, comprehensive monitoring, and straightforward cluster management, significantly reducing operational overhead.
• Traditional Messaging Patterns: Ideal for implementing patterns like request/reply, point-to-point, and fan-out, which can be more complex to model in Kafka. It's well-suited for classic task distribution and inter-service communication.

Website: https://www.cloudamqp.com

Top 12 Confluent Alternatives — Side-by-Side Comparison

Choosing the Right Streaming Platform for Your Needs

Navigating the landscape of real-time data streaming can be complex, but as we've explored, a powerful ecosystem of Confluent alternatives exists to meet diverse technical and business requirements. The "best" choice is not a one-size-fits-all answer; it's the platform that aligns most closely with your specific goals, operational capacity, and strategic vision for data. The journey away from a single-vendor lock-in opens up opportunities for significant cost savings, improved performance, and a more agile data architecture.

Key Takeaways and Decision Factors

Your evaluation process should pivot on a few critical questions. Are you looking for a drop-in Kafka replacement, or are you seeking to solve a higher-level business problem like real-time data integration? The answer will guide you down very different paths.

• Managed Kafka Services: For teams deeply integrated within a specific cloud provider's ecosystem, platforms like Amazon MSK, Azure Event Hubs, and Google Cloud MSK offer a compelling path. They provide the familiarity of Kafka with the benefits of managed infrastructure, reducing the operational burden of cluster management. When choosing between these, a comprehensive AWS vs Azure vs GCP comparison can be invaluable, as it sheds light on the broader ecosystem, pricing models, and service integrations that will impact your long-term success.

• Modern Kafka-Compatible Platforms: If your primary goal is to achieve Kafka's power without its operational complexity, especially the JVM's overhead, then Redpanda and WarpStream present a modern approach. Redpanda's C++ based architecture offers a simpler, more performant alternative, while WarpStream's object storage-based model introduces a novel serverless paradigm that decouples compute and storage for cost efficiency.

• The Self-Managed Route: Opting for self-managed Apache Kafka remains a valid choice for organizations with deep engineering expertise and a need for absolute control over their infrastructure. This path offers maximum flexibility but comes with the highest total cost of ownership when accounting for the engineering hours required for maintenance, scaling, and security.

Shifting Focus from Infrastructure to Outcomes

A critical insight from our analysis is the emerging shift from infrastructure-centric to outcome-centric data streaming. While managing Kafka brokers, topics, and partitions is a necessary task for some, many data teams are realizing their core mission is not to operate streaming infrastructure. Their goal is to deliver fresh, reliable data to analytics platforms, data warehouses, and operational systems as quickly and efficiently as possible.

This is where a CDC-first Confluent alternative like Streamkap fundamentally changes the game. By abstracting away the underlying complexities of Kafka and Flink, Streamkap allows you to focus purely on the data pipeline itself. You define your sources and destinations, and the platform handles the rest-from schema evolution and fault tolerance to performance optimization and scaling. This zero-ops model eliminates the need for a dedicated team of streaming infrastructure experts.

Ultimately, the right Confluent alternative is one that empowers your team, not burdens it. As you weigh your options, consider the total cost of ownership, which includes not just licensing and infrastructure costs but also the invaluable time of your engineering team. The ideal solution will reduce friction, accelerate your time to value, and allow you to unlock the full potential of your data with confidence and speed.

Ready to move beyond managing complex streaming infrastructure? Streamkap offers a powerful, serverless Confluent alternative built for modern data teams. See how our CDC-first platform can deliver real-time data pipelines from your databases to any destination with 90% lower costs and zero operational overhead by visiting Streamkap to start your free trial.