AutoMQ Diskless Kafka Questions Answered

AutoMQ is a cloud-native streaming platform that is fully compatible with Apache Kafka. It implements a Diskless architecture that stores persistent stream data in S3-compatible object storage instead of depending on broker-local disks and inter-broker replica copying.

AutoMQ keeps the Kafka protocol and ecosystem compatibility that teams already rely on, while changing the storage architecture so brokers can be stateless, elastic, and easier to operate in cloud environments.

Traditional Kafka follows a Shared Nothing architecture: each broker owns local storage and replicates data to other brokers. AutoMQ uses a Shared Storage architecture, where stateless brokers handle compute and network I/O while object storage provides durable, elastic storage.

This design enables:

• Near-unlimited storage capacity through S3-compatible object storage
• Seconds-level elasticity without large partition data movement
• Zero cross-AZ traffic for Kafka replication paths in supported deployments
• Reduced need for EBS over-provisioning
• 100% Apache Kafka protocol compatibility for existing applications and tools

Yes. AutoMQ implements the standard Kafka protocol, so producers, consumers, Kafka Connect, MirrorMaker, Flink, Spark, and common monitoring tools can work without application code changes. In most migrations, teams only need to point clients to the AutoMQ bootstrap endpoint.

AutoMQ offers two main commercial deployment models:

• AutoMQ BYOC: A managed Kafka experience where AutoMQ helps provision, scale, and patch clusters while the control plane and data plane run inside your own cloud account.
• AutoMQ Software: A self-managed option for platform teams that operate their own Kubernetes, private cloud, or data center infrastructure.

Both options use the same Diskless Kafka foundation and Kafka-compatible protocol layer.

Yes. AutoMQ supports AWS, Google Cloud, Azure, Oracle Cloud, and private environments that provide S3-compatible object storage. For private or hybrid environments, teams commonly use object storage systems such as MinIO or Ceph.

AutoMQ separates compute from storage. Brokers are stateless, while durable stream data is written to S3-compatible object storage. To keep produce latency low, AutoMQ uses a WAL layer for the immediate durable write path, then flushes data to object storage asynchronously.

Production deployments can use low-latency WAL options such as EBS WAL, Regional EBS WAL, or NFS WAL. AutoMQ Open Source defaults to S3 WAL, which trades some latency for a simple object-storage-based setup.

No. Diskless means brokers do not keep persistent state on local disks. Durable data is written to the WAL and then to object storage. If a broker fails, another broker can take over the partition mapping because the persistent data is not tied to the failed broker's local disk.

AutoMQ does not write incoming messages directly into the final object storage layout on the hot path. Incoming messages first go through the WAL, then the system asynchronously writes stream objects to S3-compatible object storage.

For latency-sensitive workloads, AutoMQ commercial editions can use high-performance WAL options such as Regional EBS WAL or NFS WAL. AutoMQ Open Source with S3 WAL is simpler to deploy and is better suited to latency-tolerant workloads such as logging and monitoring.

AutoMQ brokers are stateless, so capacity can be adjusted based on workload demand without moving full partitions of persistent data between brokers. Partition reassignment is primarily a metadata operation because durable data already lives in shared object storage.

This helps teams:

• Scale out or in within seconds in supported environments
• Reduce reserved broker and disk capacity for future spikes
• Use Self-Balancing to distribute partitions and traffic
• Avoid long data-copy operations during normal partition reassignment

Yes. AutoMQ stores persistent stream data in S3-compatible object storage, which is designed for high durability and large-scale retention. This makes AutoMQ well suited for keeping Kafka data for days, weeks, months, or longer based on business and compliance requirements.

AutoMQ pricing is designed around actual usage rather than pre-provisioned Kafka disk capacity. Customers pay cloud provider infrastructure costs directly to their cloud provider and pay AutoMQ service fees based on the selected plan and workload usage.

The main cost savings come from object storage, elastic stateless brokers, no partition fees, and reduced cross-AZ transfer costs in supported deployments.

AutoMQ is built on object storage with a Diskless architecture, which can reduce compute, storage, and network costs compared with traditional Kafka deployments.

• Storage: Object storage offers lower unit cost and pay-as-you-go flexibility.
• Compute: Stateless brokers can scale with demand instead of being reserved for peak load.
• Network: In cloud environments such as AWS, AutoMQ can avoid traditional cross-AZ replication traffic.

The pricing calculator uses AWS US East (N. Virginia) as the reference region. EC2, S3, network, and MSK pricing are based on AWS public rates; actual costs may vary based on region, discounts, workload shape, and cloud account terms.

Calculator assumptions include:

• Apache Kafka is configured with three replicas for data durability
• Throughput metrics are based on server-side compressed data
• Monthly usage is calculated as 730 hours

AutoMQ recommends subscribing through AWS, Google Cloud, or Azure Marketplace for streamlined billing. If marketplace payment is not available, customers can contact AutoMQ to arrange contract-based billing.

Yes. AutoMQ supports both pay-as-you-go and annual contract billing. Annual contracts can include discounted rates based on workload size and commercial terms.

Yes. AutoMQ offers a free trial with limited quota for new users. You can register through AutoMQ Cloud without a credit card.

AutoMQ does not charge partition fees. AutoMQ also uses object storage for data durability, so it does not require traditional multi-replica partition storage in the same way disk-based Kafka deployments do.

Yes. AutoMQ Linking for Kafka supports zero-downtime migration with offset preservation. Producers and consumers can be moved gradually, and consumers can resume from preserved offsets after the cutover.

Savings vary by workload, region, retention, traffic pattern, and cloud discounts. The largest savings usually come from replacing replicated local disks with object storage, reducing cross-AZ replication traffic, and scaling stateless brokers based on actual demand. Use the pricing calculator to model your workload.

Yes, when deployed with the right WAL option and cluster configuration. AutoMQ supports low-latency real-time workloads, and published customer stories show AutoMQ running production pipelines for companies such as Grab, Geely, Honda, JD.com, LG U+, Poizon, and Tencent Cloud EMR.

AutoMQ Software is designed for teams that run in Kubernetes, private cloud, or data center environments. It can use S3-compatible object storage such as MinIO or Ceph while keeping the Kafka-compatible API surface.

AWS offers Amazon MSK, but MSK still requires teams to plan broker capacity, provision EBS storage, manage partition rebalancing, and account for cross-AZ networking costs. AutoMQ BYOC runs in your AWS account and VPC with a Diskless architecture that uses object storage, stateless brokers, and automatic scaling.

Yes. AutoMQ BYOC is a managed Kafka experience deployed in your cloud account. AutoMQ helps manage scaling, rebalancing, patching, and storage while keeping the data plane in your environment.

AutoMQ reduces major MSK cost drivers: EBS storage, cross-AZ replication traffic, and over-provisioned capacity. Actual savings depend on throughput, retention, partition count, region, and cloud discounts, so teams should model their own workload in the pricing calculator.

Beyond broker instance hours, MSK costs can include:

• Idle EBS capacity reserved for peak retention and future growth
• Cross-AZ networking for producer traffic, broker replication, and consumer reads
• Operational work for partition balancing, scaling, upgrades, and patching
• Over-provisioned capacity because Kafka clusters are often sized for peak demand

AutoMQ Linking for Kafka supports zero-downtime migration from MSK:

1. Deploy AutoMQ alongside the existing MSK cluster.
2. Create a linking task from the AutoMQ Console.
3. Replicate topics and consumer group offsets.
4. Move producers to AutoMQ.
5. Move consumers to AutoMQ with offset preservation.
6. Decommission MSK after validation.

The process does not require application code changes for Kafka-compatible workloads.

MSK Serverless automates some infrastructure operations, but it still has service limits and shared-service tradeoffs. For high-throughput or latency-sensitive production workloads, teams should compare workload limits, cost behavior, scaling behavior, and operational control before choosing it.

AutoMQ is built around a Diskless architecture where brokers are stateless and durable data is stored in object storage. MSK Express is an AWS-managed Kafka option with its own operational model and limits. AutoMQ also provides AutoMQ Linking for Kafka to support migration from existing Kafka or MSK clusters.

MSK Tiered Storage can reduce some storage pressure, but it does not remove every disk-based Kafka operational concern. Teams still need to understand supported Kafka versions, topic constraints, local storage behavior, and how tiered storage affects reads, compaction, retention, and cost.

AutoMQ brokers are stateless, which makes them a better fit for Spot Instances than disk-bound brokers. If a Spot instance is reclaimed, a replacement broker can recover state from shared storage. Teams should still set conservative Spot policies and validate availability targets for production workloads.

Confluent Cloud pricing can include managed service margins, partition-based charges, networking costs, and storage costs depending on cluster type and workload. AutoMQ BYOC runs in the customer's cloud account and uses object storage with stateless brokers, which gives teams more infrastructure transparency and cost control.

Savings depend on throughput, read fanout, partition count, retention, cloud region, and current Confluent plan. The biggest potential savings usually come from avoiding partition fees, reducing cross-AZ networking costs, using object storage, and running in a BYOC model.

Some Kafka services charge based on partition count, so teams pay more as they create more topics and partitions even when throughput is modest. AutoMQ does not charge partition fees, so partition count remains primarily an engineering and workload design choice.

Yes. AutoMQ BYOC runs in your own cloud account. Both the control plane and data plane are deployed in your environment, helping teams keep data, networking, and infrastructure under their account and security boundary.

AutoMQ Linking for Kafka is designed for zero-downtime migration with offset preservation from Kafka-compatible clusters. The right migration approach depends on the source cluster, data volume, downtime tolerance, and operational constraints, so AutoMQ experts can help plan the cutover.

Table Topic is AutoMQ's built-in capability for materializing Kafka topics as query-ready lakehouse tables. It helps teams stream data into Apache Iceberg without building and operating separate ETL pipelines.

Table Topic supports Apache Iceberg today. Additional lakehouse formats may be considered based on customer demand and product roadmap priorities.

Table Topic is a managed alternative to writing Flink jobs solely for Kafka-to-lake ingestion. Flink remains the right choice for complex stateful processing, joins, custom transformations, or event-time computation. Table Topic is best for simpler, reliable stream-to-table materialization.

No. Existing Kafka producers and consumers continue using Kafka topics through the Kafka protocol. Table Topic runs alongside the streaming workload and materializes data into table storage for analytics.

Data freshness depends on the configured commit interval and downstream table maintenance strategy. Shorter commit intervals improve freshness but can create more small files, so teams should balance freshness against compaction and query cost.

Yes. AutoMQ Managed Connector is built on Apache Kafka Connect. Standard Kafka Connect plugins that run on platforms such as Confluent Platform or MSK Connect can usually run without code changes.

You can upload custom connector plugins. As long as the plugin follows the standard Kafka Connect model and its runtime dependencies are available, AutoMQ can run it.

Managed Connectors run on Kubernetes and scale based on workload signals such as CPU, throughput, and connector task demand. This removes much of the manual worker sizing and deployment work from the user.

Connectors and plugins are free to use. Customers pay for the compute resources consumed by connector workers, based on the selected deployment and billing model.

Yes. Logs and metrics are built in. Metrics can also be exported to systems such as Prometheus, Datadog, or your existing observability stack for dashboards and alerting.

Kafka Agent is an AI-powered operations assistant for Apache Kafka. It combines Kafka operational knowledge with LLM reasoning to help diagnose cluster issues, manage topics, analyze consumer lag, and identify problems through natural language conversation.

Traditional monitoring tools show metrics and fire threshold-based alerts. Kafka Agent goes further by correlating metrics across brokers, consumers, and topics, identifying likely root causes, and suggesting concrete actions.

Kafka Agent deploys in your private network. Kafka messages, metadata, and credentials stay in your infrastructure. LLM providers are configurable, so teams can use cloud LLM APIs with their own keys or local OpenAI-compatible models for private deployments.

Kafka Agent includes built-in tools organized by risk level. Read-only operations such as cluster overview, consumer lag analysis, and topic listing can run automatically. Mutating operations such as creating topics or changing configs require confirmation. High-risk operations such as deleting topics or resetting offsets require explicit confirmation and impact review.

Yes. A single Kafka Agent instance can manage multiple Kafka clusters with independent connection settings, credentials, and access policies.

Kafka Agent supports cloud LLM providers and open-source models that expose an OpenAI-compatible API. Teams can configure provider priority and fallback based on privacy, availability, and cost requirements.

AutoMQ publishes customer stories across mobility, gaming, e-commerce, telecom, cloud services, and observability. Examples include:

• Grab: Reduced partition reassignment from hours to seconds and improved throughput efficiency
• LG U+: Processes 2.2 billion daily messages with stateless Kafka architecture on AWS
• Tencent Cloud EMR: Integrated AutoMQ as a first-party service with Iceberg table support
• Poizon: Handles 40 GiB/s observability peaks with elastic scaling
• Geely: Supports a connected vehicle platform for more than 10 million vehicles
• Honda: Migrated with AutoMQ Linking for Kafka and reduced Kafka TCO
• JD.com: Runs at 40 GiB/s scale with a reduced storage footprint

AutoMQ offers Dev, Pro, and Enterprise tiers with different limits, availability targets, and support levels.

• Dev: 20 MiB/sec max ingress throughput, 2,000 max partitions, 7 days max message retention, 99.50% SLA, community support
• Pro: 1,024 MiB/sec max ingress throughput, 20,000 max partitions, 90 days max message retention, 99.95% SLA, business-hours support with 3-hour response time
• Enterprise: Unlimited ingress throughput, unlimited partitions, unlimited retention, 99.99% SLA, 24/7 premium support with 1-hour response time

Enterprise can also include a dedicated support engineer and Multi-Region Cluster for disaster recovery.

Referral Partners earn commission for qualified deals they refer that close successfully. The partner introduces and registers the opportunity, while AutoMQ handles the sales process, technical evaluation, and customer onboarding.

Reseller Partners receive discounted AutoMQ pricing for customer deals and can build a recurring revenue stream while managing the customer relationship directly.

Yes. Partners can operate under both models. Some opportunities can be referred to AutoMQ, while others can be managed directly by the partner as a reseller.

AutoMQ is Kafka-compatible, so existing Kafka expertise transfers directly. AutoMQ provides partner enablement materials, migration guidance, competitive positioning, and joint go-to-market support.

You can:

• Start a free trial through AutoMQ Cloud
• Try the interactive demo
• Contact AutoMQ experts for migration planning or private offers
• Read the documentation

Start with the AutoMQ documentation, the AutoMQ blog, the customer stories, and The Diskless Engine.