Best Kafka tools for Spring Kafka and Spring Cloud Stream

Tools compared

Rank	Tool	Total (out of 110)	Out of the data path	Spring application visibility	Production access on request	Audit trail per person	Directory and Kafka sign-in	Many teams, shared clusters	Cost a year, one cluster (modelled)
1	Kpow	99	One container, no external database, not a proxy	Streams topologies and metrics via an open-source agent; all groups, simple included; DLT headers	GROUP_EDIT permission, temporary policies, staged changes	Every offset reset and data query, by user	SAML, OpenID, LDAP	Tenants per team	$7,380
2	Kafbat UI	68	One container	Consumer groups and records; no Streams view	Read-only clusters, no approvals	Opt-in log, no view in the product	OAuth2, OIDC, LDAP	Roles per resource	$8,640
3	AKHQ	60	One container	Consumer groups and records; no Streams view	Group roles, no approvals	Opt-in topic, no reads	LDAP, OIDC	Regex group roles	$8,640
4	Spring Boot Actuator and Micrometer	40	Inside each app, plus a metrics store	Metrics and a topology description per app, nothing drawn	Per-app endpoint security; bindings can be paused	None by person	Per-app Spring Security	None	$14,400
5	Conduktor	59	PostgreSQL; Gateway proxy for data controls	Consumer groups and records; no Streams view described	Masking exemptions, owner approvals	70+ event types, by user	LDAP, OIDC	Groups across clusters; Virtual Clusters need Gateway	$32,880

The tools, ranked for Spring Kafka and Spring Cloud Stream

What teams using Spring Kafka and Spring Cloud Stream need

This page is about Kafka tools for teams whose applications are built with Spring for Apache Kafka, the spring-kafka library behind KafkaTemplate and @KafkaListener, or with Spring Cloud Stream and its Kafka and Kafka Streams binders. Neither is a Kafka platform: both are frameworks inside the application, so the question here is how a tool sees, governs and records work on Spring applications, whatever Kafka they connect to. How the library itself is set up is covered in Kafka with Spring Boot. Spring applications come in two kinds, and a tool treats them differently. A listener service, @KafkaListener or a Spring Cloud Stream function on the Kafka binder, is an ordinary consumer and producer. A Kafka Streams application, built with Spring’s StreamsBuilderFactoryBean or the Kafka Streams binder, also keeps state stores, internal topics and a topology of processors.

A Kafka Streams application’s application.id is used in several places at once: as the consumer group it reads through, the prefix of its internal topic names and the name of its local state directory, as the Kafka Streams configuration reference lists. The same reference advises changing it when an application is updated unless the existing internal topics and state stores should be reused, so a renamed Spring function or a new version suffix starts a new consumer group and new internal topics while the old ones stay on the cluster. A tool that shows the group, the internal topics and the topology side by side makes that visible; one that shows only consumer groups leaves the old group looking like a stalled consumer.

A Kafka Streams application’s topology and its thread and state store metrics live inside the application, so a tool that shows them needs some code running there. In Kpow’s case that is the Streams agent, which is Factor House code that runs inside the customer’s own application and is published under the Apache 2.0 licence, so a security team can read exactly what it does before it goes into a Spring service.

Not every Spring consumer is a consumer group member. A @KafkaListener that names its partitions with topicPartitions, as the listener annotation documentation shows, assigns them itself and commits offsets without joining a group, and Kafka’s admin API reports such groups as simple consumer groups (ConsumerGroupListing). A lag view that lists only classic groups misses them.

A Spring dead-letter topic carries its diagnosis in the headers. When Spring for Apache Kafka’s DeadLetterPublishingRecoverer publishes a failed record, it adds the exception class, the cause, the exception message and the full stack trace as record headers, as the error handling documentation lists. Triage is reading those headers beside the record, so a tool needs to show headers on the dead-letter topic. The options for that work are compared in the best tools to manage a dead letter queue in Kafka.

A Kafka Streams offset reset is more than an offset reset. Kafka’s application reset tool requires every instance of the application to be stopped, “otherwise, the application may enter an invalid state, crash, or produce incorrect results”, resets the input topics’ offsets, deletes the internal topics, and leaves the local state directory on each machine for the team to delete. Moving a Streams application’s group offsets in a UI while its changelogs and state stores still hold the old results is therefore a partial reset, and the permission to do it belongs with people who know the difference.

Out of the data path. Every Spring application is a Kafka client with its own bootstrap address, set in spring.kafka.bootstrap-servers or the binder’s brokers property. A tool whose controls work only when client traffic passes through a proxy needs every listener and Streams application pointed at the proxy instead of the brokers, and they then depend on the proxy staying up. A management tool needs nothing more than its own client connection to Kafka. Kpow is one container with no external database, installed in your own environment and out of the data path, and its Streams agent writes to a Kafka topic rather than to Kpow. Conduktor Console also connects directly, with a PostgreSQL database of its own; Conduktor’s data-level controls, such as encryption, masking of the data itself and virtual clusters, run in Conduktor Gateway, a Kafka proxy that client applications connect through.

Spring application visibility. For a listener service this means the consumer group and its lag, simple groups included, and the dead-letter topic with its headers. For a Kafka Streams application it also means the topology, the thread, task and state store metrics, and the internal topics, tied together by the application.id. Spring itself exposes the raw material per application: the Kafka binder’s lag metric through Micrometer, and the Kafka Streams binder’s /actuator/kafkastreamstopology endpoint, which is disabled by default and returns a topology description for external tools to draw.

Production access on request. Spring’s own production controls are endpoints inside each application. The bindings endpoint can stop, start, pause and resume a binding, and Spring Boot’s endpoint documentation says that when Spring Security is on the classpath with no other security filter chain, every actuator but health is secured, and that a custom filter chain takes over those rules. Who may pause a consumer in production is therefore decided separately in every application. What a tool adds is per person and per action: who may read records, who may reset a group’s offsets, access to production that expires after the task, and a second person’s approval before a change.

Audit trail per person. A Spring application authenticates to Kafka as its own service principal, and a shared tool reaches Kafka as one identity too, so the brokers’ records name the application or the tool, not the engineer who reset a listener’s offsets or read a dead-letter record. The only record of which person did it has to come from the tool they used. The options are compared in the best tools for Kafka audit logging.

Directory and Kafka sign-in. The tool connects to Kafka with the same SASL or TLS settings the Spring applications use, and people sign in through the company directory over SAML, OpenID Connect or LDAP, so an offset reset is tied to a named person rather than a shared credential. Where an agent runs inside the application, it should take the application’s own Kafka security settings rather than a second credential.

Many teams, shared clusters. Spring services from many teams usually share one cluster, and each team needs its own view of its own consumer groups, topics and Streams applications, and permission to touch only those.

Kpow does not win on every point. Spring Boot Actuator and Micrometer have nothing to deploy beyond the applications and a metrics store, and they hold every metric Kpow shows. Kpow’s topology view needs the agent added to each Kafka Streams application, and Kpow governs people working through Kpow, so the applications’ own actuator endpoints stay as open as each application leaves them.

How a team runs Kpow with Spring Kafka and Spring Cloud Stream

Installing it beside the cluster. Kpow runs as one Docker container, a Java JAR or from the Helm charts, in the same network as the cluster the Spring applications use. It needs no external database, because its snapshots, metrics and audit log live in topics on the Kafka cluster. Nothing changes in the Spring applications for the listener views below; only the Kafka Streams view needs the agent.

Watching listener services. Every @KafkaListener and Spring Cloud Stream binder consumer appears under consumer groups with its members, offsets and lag. Simple groups, such as a listener that assigns its own partitions, are observed by default, controlled by SNAPSHOT_SIMPLE_GROUPS in the environment variables. Group actions include resetting offsets to a value or a timestamp, setting them to the earliest offset and skipping one offset on a partition.

Reading dead-letter records. In data inspect, selecting a headers deserializer includes each record’s headers in the results, so the exception class, message and stack trace Spring adds to a dead-letter record are read beside the record, and kJQ filters narrow the topic to one exception type.

Adding the Streams agent to a Spring Kafka application. The application takes io.factorhouse:kpow-streams-agent as a dependency. The Spring Kafka example, built on Confluent’s Spring orders service, adds a component that listens on the StreamsBuilderFactoryBean and, when the streams are added, creates a StreamsRegistry from the factory’s streams configuration and registers the KafkaStreams instance and its Topology. The application then logs a line for each snapshot it sends, and the application appears in Kpow’s Streams view within a minute or two.

Adding it to a Spring Cloud Stream application. The Spring Cloud Stream example, the Kafka Streams binder’s word count sample, looks up the factory bean named &stream-builder- plus the function name from spring.cloud.stream.function.definition, and registers its streams and topology the same way. In that repository the registry’s producer properties are built by hand with the bootstrap address, because the factory’s own configuration held the address in a form that failed at startup; its README also points to the consumer groups view to reset the word count offsets.

Securing the agent. The StreamsRegistry producer takes the same SSL and SASL settings as any Kafka producer, from security.protocol and sasl.jaas.config to keystores and OAuth token endpoints, and on a cluster with ACLs its principal needs Write on the __oprtr_snapshot_state topic. By default the agent keys its snapshots by the cluster ID, read once through an AdminClient; where a Kafka service does not provide a cluster ID, or the team does not want the agent creating an AdminClient, a manual key strategy uses the environment name set in Kpow instead. Metric filters, which work like Micrometer’s meter filters, decide which metrics the agent sends, for example RocksDB metrics only (Kafka Streams documentation).

Reading topologies and metrics. Each registered application shows its topology and its Stream-Thread, state store and RocksDB metrics in Kpow’s Streams UI, alongside the consumer group named by its application.id. With the Prometheus integration on, /streams/v1 serves the Kafka Streams metrics from every agent and /streams/v1/state their state metrics, so one alert covers every Spring Streams application rather than one actuator per service.

Deciding who may do what. Kpow’s authorization actions put resetting offsets behind GROUP_EDIT and deleting groups behind GROUP_DELETE, and RBAC sets Allow, Deny or Stage per action, so a team can read its dead-letter topics without being able to move a Streams application’s offsets. Tenants give each team a view limited to its own topics and groups, and a temporary policy grants extra access that expires after the task.

Signing people in and keeping the record. Engineers sign in through SAML, OpenID Connect or LDAP. The audit log records every offset reset and data inspect query with the user from the identity provider, even though Kpow reaches Kafka as one client, and a webhook sends those records to Slack, Microsoft Teams or any HTTP endpoint, such as a SIEM collector.

Kpow live demo

See the Kpow UI before you add the Streams agent

The live Kpow demo runs on two Apache Kafka clusters on Amazon MSK. It shows brokers, topics, consumer groups, schema registries and the __oprtr_audit_log topic where Kpow keeps its audit trail, with no signup. Your own Spring applications are not in it, so pointing Kpow at your cluster and adding the Streams agent to one Kafka Streams application is the step to try next.

For teams choosing a Kafka tool for their Spring applications.

Try the Kpow demo→

FAQ

What is the best Kafka tool for Spring Kafka and Spring Cloud Stream?

On this page’s rubric, Kpow, with 99 of 110 points: of the tools here it is the only one that draws Kafka Streams topologies, through an open-source agent the Spring application registers, it shows every listener’s consumer group, simple groups included, and dead-letter records with their headers, it records every offset reset against the person who made it, and it runs as one container with no external database. Kafbat UI is the highest-scoring free option.

Does Kpow work with Spring Boot applications?

Yes. Kpow reads Kafka as an ordinary client, so every Spring Kafka listener and Spring Cloud Stream binder consumer shows up through its consumer group, topics and records with no change to the application. For Kafka Streams applications, the Kpow Streams agent adds topologies and metrics; Factor House publishes a Spring Kafka example and a Spring Cloud Stream example of the wiring, and the Kafka Streams documentation covers the agent. The Streams view is a Kpow Enterprise feature.

How do I see the topology of a Spring Cloud Stream Kafka Streams application?

The Kafka Streams binder can return the topology description at /actuator/kafkastreamstopology once the endpoint is enabled and exposed, as the Spring Cloud Stream documentation describes, and an external tool draws it. Kpow draws it directly once the application registers its streams and topology with the Kpow Streams agent.

Why does my Spring listener not show lag in my Kafka UI?

If the @KafkaListener names its partitions with topicPartitions, it assigns them itself and does not join a consumer group, and Kafka reports its committed offsets as a simple consumer group. A tool that lists only classic groups misses it. Kpow observes simple groups by default.

Is there a free Kafka tool for Spring applications?

Kafbat UI and AKHQ are open source and show a Spring application’s consumer groups, topics and records, without a Kafka Streams topology view. Spring Boot Actuator and Micrometer come with Spring and expose the metrics and a topology description per application. The Streams view in Kpow is an Enterprise feature; Kpow Community Edition is free on up to 3 clusters and 10 users for the rest of Kpow. More free options are compared in the best free Kafka UI tools.

How these tools were scored

Five of the six criteria are the ones Factor House scores on every page for teams that share a Kafka cluster; the sixth is Spring application visibility. They are listed here in order of weight. Each criterion is scored 0 to 10: 10 where a tool is the only one here doing it or clearly the best, 8 for a clean documented pass, 5 or 6 for partial support or support that needs work the reader must verify, 1 to 4 for a weak or indirect form, and 0 where it is absent. The weights add up to 11, so totals are out of 110.

1. Out of the data path (counts three times). The tool should run in your own environment, reach Kafka as an ordinary client and keep no data outside your own cluster. Scored lower: tools that need an external database of their own, and tools whose controls work only when application traffic passes through a vendor’s proxy. A self-hosted container with no external database and no proxy scores 9, an option that needs a metrics database of its own 6, and one that needs both a database and a proxy for its controls 3; 10 is kept for an option with nothing to deploy at all, and no option here qualifies. This criterion is scored the same way on every Factor House page that uses it, and only its weight changes with the reader.

2. Spring application visibility (counts twice). Whether the tool shows each Spring application’s consumer group and lag, simple groups included, reads dead-letter records with their headers, and, for Kafka Streams applications, draws the topology and shows thread, task and state store metrics across applications. A tool that shows consumer groups and records but no Streams view scores 4.

3. Production access on request (counts twice). Whether an engineer can be granted access to production for one task and have it expire, whether permissions separate reading records from resetting offsets, whether a destructive change can be held for a second person’s approval, and whether sensitive fields can be masked from people who do not need them.

4. Audit trail per person (counts twice). Whether the tool records each action, data queries included, against the person from the identity provider, and whether that record can be read in the product and sent to the systems that keep it long term.

5. Directory and Kafka sign-in (counts once). Whether people sign in through SAML, OpenID Connect or LDAP, and whether the tool connects to Kafka with the same SASL or TLS settings as any client.

6. Many teams, shared clusters (counts once). Whether each team can be given its own view of its own topics and consumer groups on a shared cluster, and whether one deployment reaches several clusters.

Costs are modelled for one production Kafka cluster and 25 engineers at $120 per engineer hour, using the same hours per tool class as Factor House’s other comparison pages. Tools with a licence carry the published price plus 2 hours a month to run. The open-source UIs carry 6 hours a month, $8,640 a year, to run, secure and keep current, and Spring Boot Actuator and Micrometer, with the metrics store and dashboards behind them, carry 10 hours a month, $14,400, the figure used for Prometheus and Grafana elsewhere on the site. Kpow’s $7,380 uses the published price of $4,500 per cluster with 100 users included. Conduktor’s Console is $1,200 a seat on AWS Marketplace, and its Gateway Core and Gateway Protect prices are added for the data-level controls.

The criteria map onto how Spring applications use Kafka in the figure below.

F1 From how Spring runs Kafka to what a tool has to do

What the Spring application does What the tool has to do

Listeners Reads through a consumer group, or assigns partitions itself when a listener names them Show lag for every group, simple groups included

Dead letters Publishes a failed record to a dead-letter topic with the exception class, message and stack trace as headers Read the dead-letter topic with its headers

Streams apps Uses its application.id as the consumer group, the internal topic prefix and the state directory name Tie the topology, the group and the internal topics together

Streams metrics Reports thread, task and state store metrics from inside the application, not from the broker Collect them from the application and show them across applications

Actuator Exposes endpoints that can pause, stop and restart bindings, secured per application Give people controls by person and action, and record who used them

Resets Needs every instance stopped and internal topics and local state cleared for a full reset Gate offset resets behind a permission and keep the record

Every option is scored from 0 to 10 on each criterion, from the evidence and sources this page cites, and the reason for each score is on its card. The criteria are weighted: Out of the data path counts three times, Spring application visibility counts twice, Production access on request counts twice, Audit trail per person counts twice, Directory and Kafka sign-in counts once and Many teams, shared clusters counts once, for a total out of 110. Out of the data path counts three times. Every Spring application is a Kafka client with its own bootstrap address, so a tool whose controls work through a proxy has to sit in front of every listener and Streams application, and a tool that needs a database of its own is one more stateful service beside them. Spring application visibility, production access on request and the per-person audit trail count twice: Kafka Streams metrics and topologies only exist inside the application, a Spring application reaches Kafka as one service identity whoever changes its offsets, and Spring's own controls are endpoints secured application by application. Directory and Kafka sign-in, and shared clusters, count once. This page is published by Factor House, which makes Kpow. Every option is scored on the same rubric and the same sources: Kpow's per-criterion scores are set the same way as every other option's and are not adjusted, and the weights apply to every option alike. Kpow ranks first on its total of 99 out of 110. The other options follow by total. Conduktor is listed last whatever its total; on its total of 59 it would place fourth.

Related reading

• Kafka: the complete guide
• Kafka with Spring Boot
• Kafka consumers in production
• Best tools to manage a dead letter queue in Kafka
• Best Kafka tools for self-managed ksqlDB
• Best Kafka tools for Apache Kafka Connect
• Best Kafka tools for self-managed Apache Kafka
• Best Kafka management tools for technology and SaaS companies