Best Kafka management tools for Confluent Platform

Tools compared

Rank	Tool	Total (out of 100)	Out of the data path	Production access on request	Audit trail per person	Confluent Platform components	Directory and Kafka sign-in	On-prem and cloud together	Cost a year, 3 clusters (modelled)
1	Kpow	89	One container, state in your Kafka, not a proxy	Time-boxed temporary policies via API, staged approvals, masking per resource	Every action with the IdP user, data queries included	Schema Registry, Connect, ksqlDB, Streams via agent	SAML, OIDC, LDAP; Kerberos, SCRAM, OAuth or mTLS to brokers	Any distribution, 12 clusters per instance	$16,380
2	Kafbat UI	65	One stateless container	Per-resource RBAC, no approvals, masking for all viewers	Optional, reads at level ALL, no view	Schema Registry, Connect, basic ksqlDB	OAuth2, OIDC, LDAP; no SAML	Confluent Cloud broke in v1.4.x and v1.5.0	$8,640
3	AKHQ	59	One stateless container	Regex groups, UI-only if JWT secret unset	Opt-in, no reads, no view	Schema Registry, Connect, basic ksqlDB	LDAP, OIDC; no SAML	Named connections	$8,640
4	Lenses	56	HQ on PostgreSQL, agent and database per cluster	Strict global masking, no approvals	In-product audit log from Team tier	Schema Registry, Connect; own SQL, ksqlDB not listed	SSO incl. Entra ID and Okta	Any Kafka API, agent per cluster	$2,880 plus quoted licence
5	Confluent Control Center	47	Dedicated host, broker reporter JAR	Confluent RBAC, no DENY, no approvals	Broker principal, not always the person	Native view of every Confluent component	OIDC on self-managed	Confluent Platform only	$2,880 plus quoted subscription
6	Conduktor	63	Console on PostgreSQL; Gateway proxy in the data path	Per-viewer masking, cross-team access requests	70+ event types with user, in the UI	Schema Registry, Connect, ksqlDB	LDAP, OIDC	Confluent Cloud, Aiven, MSK, Cloudera	$62,880; $152,880 with Gateway Core and Protect

No tool meets every column, and teams on Confluent Platform commonly keep Confluent RBAC or ACLs for services and use a management tool to govern people.

The tools, ranked for Confluent Platform

What teams on Confluent Platform need

This page is about self-managed Confluent Platform: Confluent Server brokers with Schema Registry, Kafka Connect, ksqlDB and Kafka Streams applications, run in your own data centre or on Kubernetes with Confluent’s operator. Confluent Cloud is a managed service with its own API keys, Metrics API and managed connectors, and its tooling questions are different. For the same comparison on another distribution, see the best Kafka UI tools for Amazon MSK, and for the broader field on any distribution, the best Kafka management tools.

Confluent Platform already ships a console, Control Center, inside its subscription. The question a platform team asks is what a second tool adds and what it costs to run beside the one it has. TD’s platform team found Control Center could only accept admin access, so the bank’s many client teams could not serve themselves. NORD/LB, which runs Confluent’s Kubernetes operator, used Control Center first and then added Kpow as a companion for search across large schemas, schema-validated test data and tighter production access, while keeping Confluent as its platform. A team on Confluent Platform already operates brokers, Schema Registry, Kafka Connect and ksqlDB itself, so one more self-hosted container is a small addition to what it runs, which is not true of a team that chose a managed service to avoid operating anything.

Three things come up once several teams share a Confluent Platform cluster. The tool has to connect the way Confluent Server already authenticates clients, often Kerberos or mutual TLS, and read records serialised against Schema Registry, including schemas with references. It has to cover the rest of the platform, Kafka Connect, ksqlDB and Kafka Streams, because those are where many production problems turn up. And it has to answer the governance questions Confluent RBAC leaves open. Role bindings grant actions to the principal that connects, they have no DENY rules, and they offer no approval step, no grant that expires and no masking, so when twenty engineers share one tool, the broker sees the tool and only the tool’s own log can name the person. The six paragraphs below take the rubric’s criteria in order of weight and set out what each one means on Confluent Platform in particular.

Out of the data path. Where the tool runs affects every other requirement. A tool that runs as a container in your own environment and connects to the brokers the way any Kafka client does adds nothing to the path your producers and consumers take, and nothing to the brokers themselves. Kpow is one container with no external database, installed in your own environment and out of the data path. Control Center is not a proxy, but it needs a dedicated host and a metrics reporter configured on each broker. 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.

Confluent’s own tooling shows what a console can cost to host. Legacy Control Center kept its metrics in Kafka itself, and when Confluent Platform 8.0 replaced it, TechTarget’s report on the release described the new Prometheus-based architecture as removing the need to run multiple Kafka clusters for that workload. Kpow keeps its state in Kafka too, in a deliberately small form. It computes its figures with Kafka Streams, which stores application state in internal topics on the cluster, and its system requirements put those topics at up to 10 GB of replicated disk with the default one-week retention. Nothing is added to the brokers’ configuration or classpath, and the connections a network team has to approve are the Kafka protocol to the brokers and HTTP to the Schema Registry, ksqlDB and Kafka Connect REST endpoints. Keeping state in the cluster has a cost that belongs in the same comparison. In a multi-cluster installation the first cluster configured is the primary cluster and holds the snapshots, metrics and audit records for the whole installation, so Kpow’s availability follows that cluster, and a team that also manages Confluent Cloud from the same instance decides where its audit records live by which cluster it lists first.

Confluent’s newer route to a single view across self-managed and cloud clusters is Unified Stream Manager. Confluent’s documentation describes it as establishing Confluent Cloud as the control plane for governance while Confluent Platform remains the data plane, through an agent in the customer’s environment that opens an outbound connection to Confluent Cloud and authenticates with a Confluent Cloud service account. Message data stays where it is under that model, and the console that monitors and governs the cluster does not. That rules it out for teams that are not in a public cloud at all, and for teams that keep cloud use to a minimum for regulatory or sovereignty reasons. Kai Waehner’s Q3 2026 data streaming landscape reports self-managed and private deployments recovering ground for reasons of jurisdiction, control and continuity more than cost, and notes that a fully managed service brings the vendor’s jurisdiction with it. A console that runs inside the same network as the brokers needs no such connection.

Production access on request. Confluent RBAC decides who holds a role and has no notion of for how long. A role binding stays in force until someone removes it, so a one-off need to read a production topic becomes a standing binding unless somebody remembers to delete it afterwards. Microsoft describes the alternative for its own directory as just-in-time privileged access, and the same model fits Kafka when the tool can grant one action on one topic for a fixed period. Kpow’s temporary policies do that with a duration or an end date and time, are capped at seven days unless the TEMPORARY_POLICY_MAX_MS setting is changed, and are each written to the audit log. Confluent’s RBAC documentation also recommends a soft limit of 1,000 role bindings and 15 requests a second to the API that adds, removes or looks up bindings, so a design that binds every engineer to every topic individually runs into the platform’s own limits, and bindings to groups and topic prefixes are the workable shape.

ksqlDB is where a typed statement turns into a standing production process. Confluent’s RBAC documentation for ksqlDB states that the server checks that the interactive user has the required permissions, and that the query itself then runs in the security context of the ksqlDB service principal, with some statements starting long-running persistent queries. A CREATE STREAM AS SELECT entered once therefore keeps reading and writing topics under the service principal long after the person who typed it has moved on. Starting or terminating a persistent query is a production change in the same class as altering a topic, which is why Kpow treats ksqlDB queries as their own resource type in RBAC, so that executing and terminating them can be allowed or denied per role, and each statement is written to the audit log with the person who ran it.

Audit trail per person. Confluent Server’s structured audit log is narrower by default than a team might assume. Confluent’s audit log documentation states that the default configuration captures the Management and Authorize categories of events only, in a topic named confluent-audit-log-events that is retained for 90 days, and that the produce, consume and interbroker categories are disabled by default. The same page advises being very selective when enabling produce and consume logging and configuring it only for the most sensitive topics, because each read and write then generates another audit message. Topic creation and role binding changes are therefore on the record out of the box, and reads of a topic are not. Where consume logging is enabled, each event names the authenticated principal, in the way a Kafka ACL is defined for a principal and not for a person, so work done through a shared tool is recorded against the tool’s principal and work done by a ksqlDB query against the ksqlDB service principal. Which engineer read a topic that holds customer data is a question only the tool people sign in to can answer, and the accountability controls auditors test against, such as those in NIST SP 800-53, are written about individuals. Retention runs the other way. Kpow’s audit records sit in a topic on the cluster with the one-week default retention of its other internal topics, where Confluent’s default is 90 days, so long-term retention of Kpow’s trail comes from forwarding it to a SIEM by webhook.

Confluent Platform components. ksqlDB is built on Kafka Streams, and Confluent’s architecture documentation states that the engine compiles statements into Kafka Streams applications and keeps its own metadata in topics named with the _confluent-ksql- prefix and the server’s service ID, such as the command topic. Kafka Streams uses its application ID as the consumer group ID and as the prefix of its internal topic names, so each persistent query appears on the cluster as a consumer group with its own changelog and repartition topics. The lag of a ksqlDB query is therefore ordinary consumer group lag, and a stalled query can be found from the consumer group view of any tool on this page, including the ones with no ksqlDB support. What separates the tools is whether the query can be read, started and terminated from the same place.

Kafka Streams applications written by the team are a different case, because a topology and its task-level metrics are reported by the application and never reach the brokers. A tool can only draw a topology that the application hands it. Kpow’s Streams Agent is an Apache 2.0 library that writes a snapshot to a Kpow topic on the cluster once every minute and does not talk to Kpow directly, so the only access it needs is permission to write to that topic, and the cost is a dependency added to each application.

Schema Registry has a two-stage delete that a tool should keep visible. Confluent’s documentation describes a soft delete, which removes a schema version while its schema ID stays available for lookups, and a hard delete with the permanent=true flag, which removes all metadata including schema IDs and is allowed only after a soft delete. Kpow’s schema management lists soft-deleted subjects and offers the permanent delete as a separate action. Confluent’s licensing documentation places data contracts, the feature behind Schema Registry data rules, under the Confluent Enterprise License, and since release 95.1 Kpow’s data inspect identifies records that fail a rule and filters for them.

Directory and Kafka sign-in. Two layers are involved, and Confluent Platform fills each differently. On the cluster side, Kerberos is the mechanism that most often delays a new tool. A Kerberos client needs a keytab and a JAAS login configuration, and the Apache Kafka documentation notes that all hosts have to be resolvable by their fully qualified domain names, so a tool running in a container needs the keytab mounted as a secret and the same DNS view of the brokers that the applications have. On the people side, Confluent RBAC reads users and groups from LDAP or an OIDC identity provider through its Metadata Service, and its documentation notes that the user ID in a group role binding is case-specific and has to match the case of the Active Directory record. A tool with its own sign-in sits in front of that. Kpow accepts SAML as well as OIDC and LDAP, which matters where the identity team issues SAML applications only, and since release 96.5 its API accepts OpenID Connect too, so a script or command-line session carries a person’s identity in place of a shared key.

On-prem and cloud together. Mixed installations are normal on Confluent. TD runs more than 20 clusters in four flavours, on-prem virtual machines, on-prem physical servers for very low latency, and two kinds of Confluent Cloud cluster, with more than 300 projects onboarded. Control Center documents Confluent Platform clusters only, so a tool that also manages Confluent Cloud (set up Kpow with Confluent Cloud), Amazon MSK and open-source Kafka keeps working whichever way the topology changes.

Cost and portability are the other reasons teams look beyond Control Center. It is not sold separately, and the Control Center review records practitioners rating Confluent’s pricing 5 to 6 out of 10. Gmarket, the South Korean marketplace, ran the licensed Confluent distribution, moved to community Kafka to cut licence cost, and replaced Control Center with Kpow for consumer lag, topic offsets and data inspect across 10 clusters and about 150 users. A vendor’s console leaves with the vendor, and a provider-neutral one can be installed before a migration and kept through it, which gives the team one view of both sides while it moves. Confluent’s licensing documentation also places Control Center, Confluent RBAC and structured audit logs under the Confluent Enterprise License, while Schema Registry, Kafka Connect and ksqlDB are available under the Confluent Community License, so a team running only the community-licensed components has no Control Center and no Confluent RBAC to build on. The total cost of moving off a licensed Confluent distribution is worked through in the migrating to open source Kafka talk.

Ownership is part of the same question since IBM completed its acquisition of Confluent on 17 March 2026. Nothing about the platform changed on that date, and NORD/LB’s arrangement shows that the Kafka distribution and the tool people use to manage it can be separate decisions. For financial firms the reason to keep them separable is written down, because DORA Article 28 requires exit strategies for ICT services that support critical or important functions. An exit inventory for Confluent Platform starts with the features Confluent’s licensing documentation lists under Confluent Server, among them Cluster Linking, Schema Linking, Self-Balancing Clusters, Confluent RBAC and structured audit logs, then the commercial and premium connectors, which carry the same licence, and then whether daily operations depend on the vendor’s console. Replication is the hardest item on that list. Confluent describes Cluster Linking as byte-for-byte mirroring with consistent offsets that is built into Confluent Server and Confluent Cloud, while the open-source alternative, MirrorMaker 2, translates offsets in a way that KIP-1279 itself calls lossy, and that proposal to bring cluster mirroring into Apache Kafka is the one to watch. Schema Registry is the more portable part, since Karapace presents itself as a drop-in replacement for it on the client and server sides, and Kpow connects to the Confluent-compatible registries as it does to Confluent’s own.

Control Center and Kpow do different jobs on Confluent Platform. Control Center is Confluent’s own console, comes inside the subscription and gives the deepest native view of Confluent’s components. Kpow adds what a shared platform needs for people (tenants, approvals, expiring production access, masking and an audit trail that names the person) and reaches clusters Control Center cannot, priced per cluster with 100 users included. No tool here leads on every criterion. Kpow governs people working through Kpow, so Confluent RBAC or ACLs stay the control for applications, and Confluent RBAC role bindings are still managed in Confluent’s tools, where a change takes effect centrally through the Metadata Service. Kafka Streams topologies appear only for applications that include the Kpow Streams Agent, ksqlDB and Streams are Enterprise features, and Control Center’s native view of Confluent’s components remains deeper.

Who runs Kpow on Confluent Platform

Two Kpow customers have described in public how Kpow sits beside Confluent’s own tooling. NORD/LB runs Confluent Platform on Confluent’s Kubernetes operator and develops its ksqlDB queries in Kpow. TD, which runs on-prem clusters beside Confluent Cloud, put its client teams on Kpow because Control Center could only accept admin access. Each card is tagged with the rubric criteria its evidence speaks to, and the other tags name what else the source covers. TD’s talk also shows what running in the bank’s own environment looks like as a procedure. A client team opens the firewall, creates an Active Directory group and raises a ServiceNow change, and the platform team then sets up the team’s tenant and access policy, so onboarding is a network and directory change made inside the bank through its existing change process.

How a team runs Confluent Platform with Kpow

The workflows below are how a platform team puts Kpow to work on Confluent Platform. Each one is built from documented Kpow features.

Install it beside the cluster. Kpow runs as one Docker container or Java JAR, or on Kubernetes with the Helm charts, in the same environment as the brokers. It needs no external database, because its snapshots, metrics and audit log live in topics on your own cluster. On Kubernetes beside Confluent’s operator, the chart sets resource requests equal to limits, 2 CPU and 8 Gi by default for an instance that manages several clusters, which puts the pod in the Guaranteed quality of service class that Kubernetes evicts last when a node runs short. To try it first, the Confluent Platform quickstart starts a Confluent broker, Schema Registry, Kafka Connect and ksqlDB with Kpow in one Docker Compose file.

Connect to Confluent Server. Kpow takes the same cluster connection settings as a Kafka producer or consumer, with SASL GSSAPI (Kerberos) as the default mechanism, SCRAM, OAuth or mutual TLS, so it uses a principal the platform team creates and scopes with Confluent RBAC or ACLs like any other client. For Kerberos the JAAS login configuration is passed in the SASL_JAAS_CONFIG setting and the keytab is mounted into the container. Kpow’s documentation lists compatibility with Apache Kafka 1.0 and later, which covers the older Confluent Platform clusters that large organisations still run beside their current ones.

Add Schema Registry, Connect and ksqlDB. Each Kafka cluster can carry several Schema Registries, Kafka Connect clusters and ksqlDB servers. Engineers create and edit schemas with references, manage connectors and set failed ones to restart on their own, and run ksqlDB queries under RBAC actions such as KSQLDB_QUERY, KSQLDB_EXECUTE and KSQLDB_TERMINATE_QUERY. A platform with this many services has more that can be down at once, and with SCHEMA_REGISTRY_STARTUP_VALIDATION set to false Kpow starts when a registry is unreachable, shows the error in the UI and keeps trying to reconnect. TD’s operators use the connector view to restart a failed connector or read its stack trace, and compare two versions of a schema side by side to see what was added.

See Kafka Streams applications. Adding the Kpow Streams Agent to a Streams application lets Kpow show its topology and its thread, state store and RocksDB metrics, and export them to Prometheus for alerting.

Read and search the data. Data inspect decodes Avro, Protobuf and JSON Schema records against Schema Registry and filters them with kJQ, the partial-key search and filtering NORD/LB uses on 10-digit business partner IDs and financial statement schemas of more than 10,000 lines of JSON. Teams that encrypt payloads load their own SerDes, as TD does. A bounded search answers a different question from ksqlDB. Confluent’s documentation describes a ksqlDB push query as a subscription whose response is of indefinite length, which suits a standing question, and finding the one record behind an incident is a search with an end.

Sign people in and scope each team. Engineers sign in through LDAP, SAML or OpenID Connect, with directory groups mapped to roles. Tenants limit which topics, groups, connectors and ksqlDB servers each team can see, and RBAC sets Allow, Deny or Stage per action and resource, with Deny winning where policies overlap, which is the rule Confluent RBAC role bindings cannot express. Resources can be named by prefix or suffix, so a policy follows the same topic naming convention that prefixed role bindings use on the Confluent side.

Grant production access for one task. A change system can call the Kpow API to create a temporary policy that grants inspect access on a named topic for an hour or two and then expires, and staged mutations hold a topic deletion or offset reset until a second person approves it. Data policies mask sensitive fields in data inspect and ksqlDB results on the server.

Keep the record. The audit log records each action, data inspect queries and ksqlDB statements included, with the person from the directory and the policy that allowed it, and a webhook sends those records to Slack, Microsoft Teams or the SIEM. The records are written to the __oprtr_audit_log topic on the primary cluster, so they stay inside the environment and cover the reads that Confluent Server’s default audit categories leave out.

Watch lag across every cluster. Kpow shows each consumer group down to partition level and resets offsets from the same view, and publishes lag, broker, topic and connector metrics on Prometheus endpoints. ksqlDB persistent queries appear in that view as consumer groups, so their lag is exported with the rest. Confluent’s next-generation Control Center is itself built on Prometheus, so a team that already runs Prometheus and Alertmanager for the platform can put Kpow’s metrics under the same alert rules. At TD, collecting consumer lag with the Kafka command-line tools took 20 minutes or more for each cluster, and the same job through Kpow runs within two minutes, so the bank collects it every five minutes and its client teams read their own lag. One instance manages up to 12 clusters, so Confluent Platform clusters sit beside Confluent Cloud, Amazon MSK or open-source Kafka in the same view.

Govern Flink jobs with the same sign-in. Teams that run Apache Flink beside Confluent Platform can put the same directory sign-in and Allow, Deny or Stage policies over their Flink jobs with Flex.

The live Kpow demo needs no signup. It runs on two Amazon MSK clusters rather than Confluent Platform, and shows topics, consumer groups, a Kafka Connect cluster, schema registries, data inspect and the __oprtr_audit_log topic. To run the workflows against your own Confluent Platform cluster, install Kpow; tenants, RBAC, temporary policies, staged mutations, masking, ksqlDB, Kafka Streams and the audit log are Kpow Enterprise features.

Kpow live demo

Open the screens a Confluent Platform team would use

The live Kpow demo needs no signup. It runs on two Amazon MSK clusters with a Kafka Connect cluster and several schema registries, so the topic, consumer group, connector, schema and data inspect screens are the same ones a team on Confluent Platform works in.

For platform teams running self-managed Confluent Platform.

Try the Kpow demo→

FAQ

What is the best Kafka management tool or Kafka UI for Confluent Platform?

On this page’s rubric, Kpow, with 89 of 100 points: it connects to Confluent Server over Kerberos, SCRAM, OAuth or mTLS, covers Schema Registry, Kafka Connect, ksqlDB and Kafka Streams, adds expiring production access, approvals, masking and a per-person audit trail on top of Confluent RBAC, manages other distributions from the same deployment, and runs as one container with no external database and nothing in the data path. Kafbat UI is the highest-scoring free option.

Is there an alternative to Confluent Control Center?

Yes. Kpow, Kafbat UI, AKHQ, Lenses and Conduktor all connect to Confluent Platform clusters. TD put its client teams on Kpow because Control Center could only accept admin access, and Gmarket replaced Control Center with Kpow when it moved from the licensed Confluent distribution to community Kafka. Control Center keeps the deepest native view of Confluent’s own components. The two are compared feature by feature in Kpow vs Confluent Control Center.

What changes with the next-generation Control Center?

The next-generation Control Center shipped with Confluent Platform 8.0 in May 2025. It moved to a Prometheus-based architecture and starts in about a minute rather than 15 to 50, but moving from the legacy version is a full migration that discards historical metrics, and it still documents Confluent Platform clusters only. A team planning that migration can run Kpow beside the cluster first, since Kpow keeps its own snapshots, metrics and audit log in topics on the cluster and does not depend on Control Center. The Control Center review covers both generations.

Can a Kafka UI manage ksqlDB on Confluent Platform?

Kpow Enterprise manages ksqlDB servers, runs queries, and governs them with RBAC actions, tenants and the audit log; NORD/LB moved all of its ksqlDB query development to Kpow. Kafbat UI, AKHQ and Conduktor include basic ksqlDB support, and the Lenses review lists its own SQL rather than ksqlDB.

Does Kpow work with Kerberos-secured Confluent Platform clusters?

Yes. Kpow takes the standard Kafka client security settings, and its cluster configuration uses SASL with GSSAPI as the default mechanism, with the Kerberos service name and login settings available as environment variables. SCRAM, OAuth and mutual TLS are configured the same way.

Do Confluent Platform audit logs record who read a topic?

Not by default. Confluent’s audit log documentation states that the default configuration captures the Management and Authorize categories only, and that the produce and consume categories are disabled unless a team enables them for chosen topics. Where consume logging is on, the event names the principal that connected, which for a person working through a shared tool is the tool’s principal. A record of which person read a topic comes from the tool’s own audit log, and Kpow’s includes data inspect queries and ksqlDB statements.

How does a ksqlDB persistent query appear in a Kafka UI?

As a consumer group. ksqlDB compiles each persistent query into a Kafka Streams application, and Kafka Streams uses its application ID as the consumer group ID and the prefix of its internal topics, so the query’s lag shows in the consumer group view of any Kafka tool. Confluent’s documentation also states that the query runs in the security context of the ksqlDB service principal and not of the person who started it.

Does a Kafka management tool replace Confluent RBAC?

No. Confluent RBAC or ACLs still control what applications and the tool’s own principal can do on Confluent Server. A tool such as Kpow adds per-person roles with Deny rules, approvals, expiring production access, masking and an audit trail for the engineers who work through it.

Will the same Kafka tool keep working after a move from Confluent Platform to Confluent Cloud or Apache Kafka?

Yes, if the tool is vendor-agnostic. Kpow manages self-managed Apache Kafka, Confluent Platform, Confluent Cloud and Amazon MSK from one deployment, up to 12 clusters per instance. Control Center documents Confluent Platform clusters only.

Does Kpow run alongside Confluent for Kubernetes?

Yes. Kpow is not a Confluent for Kubernetes resource; it installs beside it with its own Helm charts and connects to the brokers as an ordinary Kafka client, with the same security settings any producer or consumer uses. NORD/LB runs its Kafka on Confluent’s Kubernetes operator and uses Kpow beside it, as its case study describes.

Can a Kafka UI decode records that use Confluent schema references and data rules?

Kpow handles Confluent schema references since release 93.3 and applies Confluent Schema Registry data rules written in CEL, CEL_FIELD or JSONata when querying records in data inspect since release 95.1. Schemas with references can be created and edited in Kpow’s schema management.

Is there a free Kafka UI for Confluent Platform?

Control Center, Confluent RBAC and structured audit logs sit under the Confluent Enterprise License, so a team running only Confluent’s community-licensed components (Schema Registry, Kafka Connect, ksqlDB) has no Control Center. Kpow Community Edition is free on up to 3 clusters and 10 users and covers Schema Registry and Kafka Connect; ksqlDB, Kafka Streams, RBAC, masking and the audit log need Kpow Enterprise. Kafbat UI and AKHQ are open source. More free options are compared in the best free Kafka UI tools.

How these tools were scored

Six criteria score every option from 0 to 10: five that Factor House pages apply to any shared Kafka platform, and one for the Confluent Platform components the tool has to cover. They are listed here in order of weight. A criterion that appears on other Factor House pages scores each option the same way here, and only its weight changes with the reader.

1. Out of the data path (counts twice). The tool should run in your own environment, connect to the brokers as an ordinary Kafka client and keep no data outside your own cluster. Scored lower: tools that need an external database of their own or a component on the brokers, 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, a tool with a database of its own 6, one with several databases or a component on the brokers 4, 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.

2. Production access on request (counts twice). Can an engineer be given read access to a production topic for a task, approved and time-boxed, without a standing role binding? Can production writes be held for a second person’s approval? And is sensitive data masked for the people who do get in? The wider set of controls over deletes and offset resets is compared in Kafka destructive operations tools.

3. Audit trail per person (counts twice). When people work through a shared tool, Confluent Server’s audit log records the tool’s principal, so only the tool’s own log can name the person. The log should include data reads as well as changes, and be readable without building a consumer first. Kafka audit logging tools compares the layers in detail.

4. Confluent Platform components (counts twice). Schema Registry, Kafka Connect, ksqlDB and Kafka Streams applications are part of most Confluent Platform deployments. A tool scores 10 only with a native view of all of them, 9 when it covers all four with documented limits, 7 when it covers Schema Registry, Connect and basic ksqlDB, and lower where ksqlDB is absent or reported broken. Registry choices are compared more broadly in Kafka schema registry tools, and connector tooling in Kafka Connect monitoring tools.

5. Directory and Kafka sign-in (counts once). People should sign in through the company directory, LDAP directly or SAML and OIDC in front of Active Directory, with directory groups mapped to roles. On the cluster side the tool has to connect the way Confluent Server already authenticates clients, which often means SASL GSSAPI (Kerberos). Kafka SSO tools covers the protocol detail.

6. On-prem and cloud together (counts once). One deployment of the tool should reach Confluent Platform clusters and whatever runs beside them or replaces them, Confluent Cloud, Amazon MSK or open-source Kafka; the general comparison is best tools to manage multiple Kafka clusters from one place.

Costs are modelled for 3 Confluent Platform clusters (development, test and production) and 50 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. Control Center’s licence sits inside a Confluent Platform subscription that is quoted, not published, so only its running cost is shown. Kpow’s $16,380 uses the Enterprise price of $4,500 per cluster with 100 users included; Community Edition is free for 3 clusters and 10 users, so a 50-engineer team is on Enterprise. For free options compared at any team size, see the best free Kafka UI tools.

The criteria map onto the Confluent Platform components in the figure below.

F1 From a Confluent Platform component to what a Kafka tool has to do

What Confluent Platform runs What the tool has to do

Broker security Confluent Server authenticates clients with Kerberos (SASL GSSAPI), SCRAM, OAuth or mutual TLS Connect with the same client settings any producer or consumer uses

Schema Registry Producers serialise Avro, Protobuf and JSON Schema records against schemas, often with references between them Decode those records, manage the schemas and respect the registry's own authentication

Kafka Connect One or more Connect clusters run the connectors that move data in and out Show connector and task state, and pause, restart or reconfigure connectors without the REST API by hand

ksqlDB and Kafka Streams Stream processing runs as ksqlDB queries and as Kafka Streams applications Run and govern ksqlDB queries, and show Streams topologies and their metrics

Confluent RBAC Role bindings grant actions to the principal that connects When people share one tool, add per-person roles, expiring production access, masking and an audit trail, because the principal is the tool's own

Where the tool runs Brokers run in your own data centre or Kubernetes, and applications connect to them directly Run beside the cluster as an ordinary Kafka client, not on the brokers and not as a proxy applications route through

Other distributions Confluent Platform often runs beside Confluent Cloud, or is migrated to open-source Apache Kafka Manage every cluster from one deployment, so the tooling does not change when the distribution does

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 twice, Production access on request counts twice, Audit trail per person counts twice, Confluent Platform components counts twice, Directory and Kafka sign-in counts once and On-prem and cloud together counts once, for a total out of 100. Out of the data path counts twice because Confluent Platform already ships a console that needs a dedicated host and a metrics reporter on every broker, so a second tool that adds another database, or a proxy that client applications must connect through for its controls to apply, is more to run on infrastructure the team already operates itself. Production access on request and the audit trail per person count twice because Confluent RBAC grants role bindings to the principal that connects, with no approval step, expiring grant or masking, so the tool has to supply both. Confluent Platform components count twice because a team on Confluent Platform runs Schema Registry, Kafka Connect, ksqlDB and Kafka Streams, and a tool that leaves any of them out sends engineers back to a second console. Directory and Kafka sign-in, and on-prem and cloud together, 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 89 out of 100. The other options follow by total. Conduktor is listed last whatever its total; on its total of 63 it would place third.

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• Apache Kafka vs Confluent Kafka