AutoMQ vs. Redpanda: Exploring Kafka Alternatives

Overview

As organizations seek more efficient and cost-effective alternatives to Apache Kafka, two prominent solutions have emerged: AutoMQ and Redpanda. Both platforms aim to address Kafka's challenges while offering improved performance, cost savings, and operational simplicity. This report provides a detailed comparison of these alternatives, analyzing their architectures, performance characteristics, compatibility, and cost-effectiveness.

Architectural Foundations

AutoMQ: Cloud-Native Storage Separation

AutoMQ represents a fundamental reimagining of Kafka's architecture for cloud environments. Inspired by Snowflake, AutoMQ decouples compute from storage, leveraging shared cloud storage systems like Amazon S3 alongside stateless brokers[1][14].

This architectural approach allows AutoMQ to:

Offload data persistence entirely to cloud storage services
Maintain stateless brokers that can be quickly scaled
Eliminate expensive broker replication
Minimize cross-zone networking costs[3]

AutoMQ's architecture incorporates both Write-Ahead Logging (WAL) storage and object storage, storing all data in object storage in near real-time[13]. This shared storage architecture makes the entire broker node completely stateless, enabling operations like second-level partition reassignment and automatic scaling[13].

Redpanda: Modern C++ Implementation

Redpanda takes a different approach, completely rewriting Kafka in C++ while maintaining protocol compatibility[10][11]. This implementation eliminates dependencies on JVM and ZooKeeper, resulting in:

Thread-per-core parallelism with CPU core pinning
Elimination of lock contention and context switching
Removal of JVM garbage collection overhead
Replacement of ZooKeeper with Raft consensus
Single-binary deployment[8][17]

Redpanda retains a more traditional architecture compared to AutoMQ, still relying primarily on local storage for performance, though its Enterprise edition offers tiered storage capabilities[17][18].

Performance Characteristics

Latency and Throughput

Both systems claim significant performance improvements over Apache Kafka, but with different benchmarks:

Metric	AutoMQ	Redpanda	Apache Kafka
Publisher Latency (P99)	Single-digit ms[13]	Claims 10x faster than Kafka[17]	>620ms (under high load)[13]
Throughput Capacity	High with fewer machines[14]	Claims to handle 1GB/s with 3 nodes vs Kafka's 9 nodes[17]	Requires more nodes for equivalent throughput[17]
Resource Efficiency	Optimizes cloud resources	Maximizes hardware usage	Requires significant overprovisioning[1][17]

Redpanda's performance advantages stem primarily from its C++ implementation and thread-per-core architecture, while AutoMQ achieves its performance through cloud-optimized storage separation and reduced network overhead[8][13].

Scalability and Elasticity

The platforms differ significantly in their approach to scaling:

Feature	AutoMQ	Redpanda	Apache Kafka
Scale in/out	In seconds[13]	In hours (Community), In seconds (Enterprise)[13][15]	In hours/days[13]
Partition Reassignment	In seconds[13]	In hours (Community), In seconds (Enterprise)[13][15]	In hours/days[13]
Continuous Self-Balancing	Yes[13]	Yes[17]	No (requires Cruise Control)[13]
Spot Instance Support	Yes[13]	Limited (claimed)[13][15]	No[13]

AutoMQ's stateless broker architecture enables significantly faster scaling operations, while Redpanda's Enterprise edition provides more advanced scaling capabilities compared to its Community version[13][15].

Kafka Compatibility

Both solutions maintain compatibility with Kafka's API, but with important distinctions:

AutoMQ: "Native Kafka" Implementation

AutoMQ maintains what it calls "Native Kafka" compatibility, extensively reusing Kafka's original code while modifying only the underlying storage layer to be S3-based[6][14]. This approach ensures:

100% compatibility with Apache Kafka[14]
No need for business systems to upgrade during migration
Ability to leverage existing Kafka tooling and ecosystems[6]

Redpanda: "Kafka Protocol" Compatibility

Redpanda implements the Kafka protocol from scratch in C++, providing what it terms "Kafka Protocol" compatibility[15]. This means:

Clients can connect to Redpanda using standard Kafka clients
Existing Kafka applications can typically use Redpanda without modification
Redpanda speaks "only Kafka" in terms of protocols[7][15]

Both platforms strive to make migration from Kafka as seamless as possible, though AutoMQ's reuse of Kafka code potentially offers higher compatibility in edge cases.

Cost-Effectiveness

Infrastructure Costs

Both platforms claim significant cost savings compared to Apache Kafka, but through different mechanisms:

Cost Factor	AutoMQ	Redpanda	Apache Kafka
Primary Storage Costs	Lower (uses cheaper S3 storage)[1][3]	Lower than Kafka (more efficient resource usage)[17]	Higher (requires local storage and replication)[1]
Compute Requirements	Lower (stateless brokers)[13]	Lower than Kafka (more efficient C++ implementation)[17]	Higher (requires more nodes)[17]
Cross-Zone Networking	No (with S3 replication)[13]	Yes[13]	Yes[13]
Storage Efficiency	High (separation of storage and compute)[1]	Medium (improved with tiered storage in Enterprise)[17]	Low (requires replication and over-provisioning)[1]

AutoMQ claims up to a 10x cost advantage primarily through cloud storage efficiency and elimination of cross-zone networking costs[1][5]. Redpanda claims up to 6x cost efficiency through more efficient resource utilization and simplified infrastructure[17][18].

Operational Costs

Beyond direct infrastructure costs, operational overhead represents a significant expense:

Operational Factor	AutoMQ	Redpanda	Apache Kafka
Deployment Complexity	Low (single service)[13]	Low (single binary)[17]	High (Kafka + ZooKeeper + tools)[17]
Monitoring Requirements	Reduced	Reduced	Extensive (JVM, ZooKeeper, etc.)[17]
Scaling Complexity	Low (automated)[13]	Medium (Community), Low (Enterprise)[17]	High (manual balancing)[17]
FTE Requirements	Lower	Lower	Higher (estimated 3-4x more)[17]

According to Redpanda's analysis, administrative cost savings can be substantial, with a claimed reduction from $448,000 to $48,000 in FTE costs for a large workload[17].

Key Feature Comparison

The following table summarizes the key differences between AutoMQ, Redpanda, and Apache Kafka:

Feature	AutoMQ	Redpanda	Apache Kafka
Core Architecture	Cloud storage separation	Modern C++ implementation	JVM-based with local storage
Kafka Compatibility	Native Kafka (modified storage layer)[13]	Kafka Protocol (from scratch)[15]	Original implementation
Broker State	Stateless[13]	Stateful[13]	Stateful
Primary Storage	Cloud storage (S3, EBS)[13]	Local disks (with tiered storage option)[8]	Local disks
Component Count	Broker[13]	Broker[13]	Broker + ZooKeeper
Durability	Guaranteed by cloud storage[13]	Guaranteed by Raft[13]	Guaranteed by ISR
Self-Balancing	Yes[13]	Yes (Enterprise)[17]	No (requires Cruise Control)
Source Availability	Open Source[13]	Open Source (Community)[13][17]	Open Source

Best Practices and Use Cases

Ideal Use Cases

Ideal Scenarios for AutoMQ

AutoMQ is particularly well-suited for:

Cloud-native deployments seeking to minimize infrastructure costs
Workloads requiring frequent scaling or elastic capacity
Organizations looking to reduce cross-zone networking costs
Use cases where data persistence in S3 provides additional benefits
Organizations with existing Kafka infrastructure seeking cloud optimization[1][3][6]

Best practices for AutoMQ deployment include:

Leveraging cloud-native features like autoscaling groups
Using regional cloud storage options for optimal performance
Monitoring S3 costs and access patterns
Configuring appropriate WAL storage based on performance needs[5][13]

Ideal Scenarios for Redpanda

Redpanda is particularly well-suited for:

Performance-critical applications requiring minimal latency
Organizations seeking operational simplicity
Self-hosted environments where hardware efficiency is paramount
Use cases requiring strong consistency guarantees
Environments where JVM overhead causes issues[7][11][17]

Best practices for Redpanda deployment include:

Using Redpanda's Autotuner for hardware optimization
Implementing regular maintenance mode for upgrades
Leveraging tiered storage in Enterprise edition for cost optimization
Taking advantage of Redpanda's data safety features[17][18]

Implementation Considerations

Monitoring and Management

Both platforms require less monitoring than Apache Kafka but still benefit from proper observability:

AutoMQ: Monitor S3 access patterns and costs; ensure proper WAL configuration
Redpanda: Leverage built-in metrics; monitor hardware utilization

Security

Both platforms support security features including:

Authentication and authorization
Encryption in transit
Audit logging

Migration Strategy

When migrating from Kafka:

Test compatibility with existing producers and consumers
Plan data migration strategy (parallel processing or cutover)
Validate performance under expected load
Develop rollback procedures

Conclusion

AutoMQ and Redpanda represent two distinct approaches to improving upon Apache Kafka. AutoMQ embraces cloud-native principles through storage separation, achieving cost efficiency and elasticity. Redpanda focuses on performance and operational simplicity through modern C++ implementation and architectural improvements.

For cloud-native deployments prioritizing elasticity and cost optimization, AutoMQ offers compelling advantages. For performance-critical applications requiring minimal latency and operational simplicity, Redpanda presents an attractive option.

Organizations should evaluate these alternatives based on their specific requirements, weighing factors such as performance needs, existing cloud strategy, operational capabilities, and budget constraints. Both solutions significantly improve upon Apache Kafka's limitations while maintaining compatibility, making migration feasible for most organizations.

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References:

AutoMQ Wiki Key Pages

What is automq

Getting started

Architecture

Deployment

Migration

Observability

Integrations

Data analysis
- RisingWave
- Databend
- Apache Doris
- Flink
- StarRocks
Object storage
- MinIO
- Ceph
- CubeFS
Kafka ui
- Kafdrop
- Redpanda Console
Observability
- Flashcat
- Guance Cloud
Data integration
- CloudCanal