Best Kafka UI tools for Redpanda

Tools compared

Rank	Tool	Total (out of 100)	Out of the data path	Production access on request	Audit trail per person	Directory and Kafka sign-in	Many teams, shared clusters	Redpanda's own APIs	Cost a year, one cluster (modelled)
1	Kpow	88	One container, no external database, not a proxy	Temporary policies, staged approvals, masking	Every action and data read, by user	SAML, OpenID, LDAP	Tenants per team	Kafka API and built-in Schema Registry; no Admin API, some disk metrics missing	$7,380
2	Redpanda Console	67	One container	Redpanda roles, no approvals or expiry	Broker audit topic through impersonation (Enterprise)	OIDC or basic (Enterprise)	Redpanda roles per user	Kafka API, Schema Registry and Admin API	$8,640 plus an unpublished licence for sign-in and RBAC
3	Kafbat UI	66	One container	Read-only clusters, no approvals	Opt-in log, no view in the product	OAuth2, OIDC, LDAP	Roles per resource	Kafka API and registry, no Redpanda guide	$8,640
4	AKHQ	58	One container	Group roles, no approvals	Opt-in topic, no reads	LDAP, OIDC	Regex groups	Kafka API and registry, no Redpanda guide	$8,640
5	Lenses	53	HQ on PostgreSQL plus an agent per cluster	Global masking, no approvals	In-product audit log	SSO from Team tier	Group roles	Any Kafka-compatible API	$6,880 for 15 users; custom above
6	Conduktor	58	Console on PostgreSQL; Gateway, a proxy, for data-level controls	Masking exemptions, owner approval	70+ event types in the UI	LDAP, OIDC	Groups; Virtual Clusters need Gateway	Redpanda partner listing; no Admin API	$32,880; $122,880 with Gateway Core and Protect

The tools, ranked for Redpanda

What teams on Redpanda need

This page is about tools that sit beside a Redpanda cluster: a UI for reading topics, managing consumer groups and schemas, and governing who does what. Redpanda itself is described in What is Redpanda?, and the broader field of Kafka tools on any distribution is in the best Kafka management tools.

Compatible with the Kafka API, not a Kafka distribution. Redpanda speaks the Kafka API, so any tool that connects to Apache Kafka can usually connect to Redpanda with the same bootstrap address and SASL or TLS settings. It is a separate implementation of that protocol, written in C++, and Kai Waehner’s comparison of Redpanda and Apache Kafka draws the distinction that Redpanda re-implements the Kafka protocol and is not a Kafka distribution, so components from the Kafka ecosystem are not guaranteed to behave the same way against it. A management tool is more exposed to that than an application is, because it sends the describe and admin requests that producers and consumers never send. One public example is the request that lists the producers writing to a partition, DescribeProducers. Until a fix merged in December 2023, Redpanda returned only producers with an open transaction, where Apache Kafka returns every idempotent and transactional producer it is still tracking. Jepsen’s analysis of Redpanda, published in 2022, tested transactions and idempotence under faults and reported three liveness and seven safety issues, seven of which Redpanda resolved in the releases around the report. Redpanda’s documentation now lists four exceptions to its Kafka compatibility: a user can hold only one SCRAM mechanism, the HTTP Proxy does not manage topics or ACLs, the request-rate quota is not supported, and the server side of KIP-890 is not implemented, so Kafka 4.x clients fall back to the original transaction protocol. This is why Kpow’s documentation supports Redpanda where Redpanda meets the standard Kafka API and feature set, and why any tool is worth running against a new Redpanda release in a test cluster before production is upgraded.

Redpanda’s own APIs. Redpanda Console goes further into Redpanda’s own Admin API, where the Redpanda version, SCRAM users, data transforms and debug bundles live, and no third-party tool on this page uses that API. The Admin API is an HTTP interface on its own port, separate from the Kafka API, and Redpanda’s documentation says that once authentication is required on it, it accepts superuser credentials only. A tool that reaches it with fixed credentials therefore holds a superuser, which the same documentation advises against using for everyday interaction with the cluster. A tool that stays on the Kafka API can run as an ordinary principal with only the ACLs it needs, which is the least privilege position for a shared tool, and Kpow publishes the minimum ACLs it needs to operate. Staying on the Kafka API has a benefit and a cost, because it is what lets a tool move to the next Kafka-compatible engine unchanged, given that the Kafka protocol is the one interface every such engine implements, and it is also why Kpow scores 7 on this criterion where Redpanda Console scores 10.

The built-in Schema Registry. The Schema Registry is built into the Redpanda binary, keeps its schemas in a compacted _schemas topic and speaks the Confluent REST API, which every tool here can use. Registries that are compatible at that level still differ at the edges, and Kpow is the tool on this page with a documented setting for Redpanda’s. Kpow’s default observation engine fetches every schema and its metadata in a single REST call, and it is not compatible with Redpanda’s registry, so a team on Redpanda sets the earlier mode, which lists the subjects and then fetches each one: SCHEMA_REGISTRY_OBSERVATION_VERSION=1. The per-subject mode makes more calls as a registry grows, which matters most on Redpanda Cloud Serverless, where Redpanda’s documentation limits the registry to 500 schemas and 500 subjects and rate limits it to 100 requests a second. With an Enterprise licence Redpanda also adds Schema Registry ACLs, a separate set from the Kafka ACLs, so on a cluster that enables them the tool’s user needs both before it can decode a record. Schema tooling on any distribution is compared in Kafka schema registry tools.

Directory and Kafka sign-in. Two sign-ins are involved, the one the tool uses to reach the brokers and the one people use to reach the tool. On the free Community edition, Redpanda authenticates clients with SASL/SCRAM or mTLS and authorises them with Kafka ACLs, and Redpanda Console has no sign-in at all. With Enterprise licences, Redpanda adds broker roles, OIDC and Kerberos, and an audit topic, and Console can pass each person’s own credentials through to the brokers so that the audit topic names them. Apache Kafka supports both SCRAM-SHA-256 and SCRAM-SHA-512, and a Redpanda user holds one of the two, so the mechanism in a tool’s connection settings has to be the one its user was created with. Console’s impersonation carries a condition that depends on the identity provider. Console forwards the person’s OAuth access token to the brokers, and Redpanda accepts it only if it is a JSON Web Token with an audience it trusts. OAuth 2.0 leaves the token format to the provider, and JWT access tokens are a separate profile, RFC 9068, that not every provider follows. Redpanda’s documentation names Google as a provider whose access tokens are opaque, in which case Console from version 3.11.0 can present the ID token, or impersonation is switched off and Console connects with a fixed service account, which is then the name the broker’s audit topic records for every person. On Redpanda Cloud Serverless, one broker user for each engineer runs out quickly, because Redpanda’s documentation limits a Serverless cluster to 30 users and 120 ACLs and offers neither broker RBAC nor mTLS there. A tool with its own directory sign-in connects as one user and applies its own roles to everyone who signs in.

Production access on request. Neither Redpanda edition gives an engineer access to production for an hour and then takes it away, or holds an offset reset for a second person’s approval. Redpanda’s roles, an Enterprise feature, are standing grants: a role is created, given ACLs and assigned to principals, and it stays assigned until someone removes it. Identity platforms treat time-limited elevation as a separate control, as Microsoft Entra Privileged Identity Management does with time-based and approval-based role activation, and for a Kafka-compatible cluster that control has to come from the tool people work through. The broker-level guard against deleting topics also differs from Apache Kafka, where switching topic deletion off is an ordinary broker setting: delete.topic.enable. Redpanda’s licensing overview lists the equivalent, delete_topic_enable, as an Enterprise feature named Topic Deletion Control, and says topic deletion reverts to enabled if the licence expires. On Redpanda Community, protection against an accidental delete therefore rests on ACLs and on an approval step in the tool. These are the controls a shared tool has to add once several teams share a cluster, and they are compared in Kafka RBAC tools and Kafka audit logging tools.

An audit trail that names the person. Redpanda’s audit log, an Enterprise feature, writes its events to the _redpanda.audit_log topic in the Open Cybersecurity Schema Framework format, the schema that Amazon Security Lake stores security events in, so it suits a SIEM well. It records less by default than its eight event types suggest, since Redpanda’s documentation enables three of them out of the box, which are management, authenticate and admin. A consumer group offset reset reaches the broker as an OffsetCommit request, which Redpanda groups under the consume event type together with Fetch, so on the defaults neither a reset nor a read of a topic is recorded, and enabling consume events records every fetch from every application on the cluster. The event also names the principal that connected, because Kafka’s authorisation model binds permissions to principals and has no notion of the person behind one. For a tool that connects with one service user, that principal is the tool. Per-person attribution then comes either from Console’s impersonation, with Enterprise licences on both Console and the brokers, or from the tool’s own log. A team that runs both keeps two trails that join on the tool’s principal, where the broker’s trail shows what the service user did and the tool’s shows which engineer asked for it.

Many teams, shared clusters. Apache Kafka’s own multi-tenancy guidance builds a shared cluster from topic naming, prefixed ACLs and quotas, and all three carry over to Redpanda with two differences. Kafka can also enforce a naming rule in the broker with a custom CreateTopicPolicy, which is a Java class the broker loads, and Redpanda has no JVM to load one, so naming rules beyond a prefixed ACL fall to whatever creates the topics. Redpanda’s documentation also says it applies byte-rate and topic-mutation quotas and does not support Kafka’s request-rate quota, which caps a client’s share of the broker’s request handling, so a client that is noisy in requests but light in bytes cannot be capped at the broker. Giving each team its own cluster avoids both questions and costs more on Redpanda than it first appears, because a Redpanda Console deployment reaches one cluster, so a cluster for each team is also a Console for each team. On a shared cluster, the tool has to limit what each team can see as well as what it may do.

Metrics and lag without JMX. Apache Kafka brokers report their metrics through JMX, and a class of Kafka tooling is built on scraping it. Redpanda has no JVM and therefore no JMX, and its documentation exposes metrics in Prometheus format on two endpoints of the Admin API port, /public_metrics and /metrics. A tool that depends on broker JMX has nothing to read on Redpanda, while one that derives its figures from the Kafka API works as it does on Apache Kafka, and Kpow computes its telemetry from the Kafka API with no dependency on JMX. Consumer lag divides the same way, because Redpanda’s dedicated lag gauges are off until consumer_lag is added to the enable_consumer_group_metrics cluster property, after which they report a maximum and a sum for each consumer group, collected every 60 seconds by default. Lag for a single partition, which is what shows one stuck partition in a group that otherwise looks healthy, comes from a tool that reads offsets through the Kafka API.

Out of the data path. Teams choose Redpanda for one binary with no ZooKeeper and no JVM, so a tool that needs a database of its own, an agent per cluster or a proxy that clients connect through adds back the components they chose to avoid. Kpow is one container with no external database, installed in your own environment and out of the data path. Conduktor Console also connects to Redpanda 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. A proxy can do things a tool beside the cluster cannot, such as encrypting records for every client without changes to the applications, and Kai Waehner’s review of Kafka proxies sets the costs beside those benefits: an extra network hop that can add latency, and a component that has to be deployed highly available or it becomes a single point of failure. On Redpanda those costs land on the reasons the platform is usually chosen, since his comparison of the two engines gives a preference for C++ infrastructure over the JVM and sensitivity to small performance differences as grounds for picking Redpanda.

Keeping no database has a cost of its own that belongs in the same comparison. Kpow keeps its state in five internal topics on the first cluster it is given and runs two Kafka Streams applications over them, which its system requirements size at up to 10 GB of replicated disk with the default one-week retention. Kpow is in that sense a Kafka Streams application running against Redpanda, the kind of ecosystem component whose behaviour has to be tested on an engine that is not Apache Kafka, and Redpanda is on the list of platforms Kpow is tested against. On a Redpanda Cloud Serverless cluster those topics and consumer groups count toward the limits Redpanda documents, 5,000 partitions and 200 consumer groups, and Kpow’s Redpanda provider page covers a self-managed broker, so Serverless is a combination to try before relying on it. With Redpanda’s Bring Your Own Cloud option the brokers run in a VPC in the customer’s own cloud account, according to Redpanda’s documentation, so a tool deployed as a container in that account reaches them over the private network as any application does, and no topic data leaves the account to be viewed.

Where other tools are stronger. Kpow does not lead on every point. It supports Redpanda where Redpanda meets the standard Kafka API, and it does not use Redpanda’s Admin API, so SCRAM users, data transforms and debug bundles stay with rpk or Redpanda Console. Some disk-related metrics and telemetry are not available when Kpow connects to Redpanda, and Kpow’s listing does not say which, so the brokers view is worth comparing with Redpanda’s own /public_metrics endpoint on first install. Its Schema Registry support on Redpanda needs the version 1 observation setting until the newer engine supports Redpanda’s registry. Redpanda Console, as Redpanda’s own UI, covers those Redpanda-specific objects, and with Enterprise licences it can put each person’s identity on the broker’s own audit log. A tool that stays outside the brokers also works with topics as they are. Kpow’s data inspect scans records with a pool of consumers and keeps no index, which suits finding records during an incident and does not suit analytical queries over a topic’s history. For those, Redpanda’s Enterprise Iceberg Topics write a topic out as an Apache Iceberg table, the Iceberg Kafka Connect sink does the same on any Kafka-compatible cluster, and Lenses has the stronger query model on this page with SQL over topics.

Who runs Kpow on Redpanda

No Kpow customer has published an account of running Kpow on Redpanda yet. Factor House’s own public material shows the pairing: the Redpanda provider page in the Kpow documentation, the Kpow and Redpanda integration guide, the Schema Registry note in release 95.1, and the 2026 APAC roadshow workshop, whose local platform runs its Kafka cluster on Redpanda with Kpow and Flex monitoring it alongside Apache Flink. For a customer account of Kpow on another Kafka-compatible platform, the Amazon MSK page carries Belong’s description of why it runs Kpow beside MSK.

How a team runs Redpanda with Kpow

Installing it beside the cluster. Kpow runs as one Docker container or Java JAR, or on Kubernetes with the Helm charts, next to a Self-Managed Redpanda cluster or in the same network as a BYOC or Redpanda Cloud cluster. It needs no external database, because its snapshots, metrics and audit log live in topics on the cluster itself. Kpow creates those internal topics on first start, which needs the Create permission on the cluster, or the platform team creates them in advance, and the PERSISTENCE_MODE setting described with the minimum ACLs reduces them to the audit topic or to none. A team on Redpanda’s Bring Your Own Cloud option runs Kpow in the same cloud account as the brokers. The integration guide starts a single Redpanda broker and Kpow in Docker in three commands.

Connecting to the brokers and the registry. Kpow connects to Redpanda’s Kafka API with the same cluster settings as any Kafka producer or consumer, SASL/SCRAM or mTLS included. Kpow gets a SCRAM user of its own, not the cluster’s superuser. Redpanda’s documentation advises against using the superuser to interact with the cluster, and a new SCRAM user has no permissions until ACLs are granted, so the team creates the user with rpk security user create, grants the ACLs Kpow documents, and sets the SCRAM mechanism in Kpow’s connection to the one the user was created with. The built-in Schema Registry is added with SCHEMA_REGISTRY_URL and the observation version set to 1, after which data inspect decodes Avro, Protobuf and JSON Schema records, and engineers filter them across topics with kJQ.

Signing people in. Engineers sign in to Kpow through Okta, Microsoft Entra ID or another identity provider over SAML or OpenID Connect, or through LDAP, so access follows the company directory rather than a shared SCRAM user. This works on Redpanda Community as well as Enterprise, because the sign-in is Kpow’s, not the broker’s.

Giving teams their own view of a shared cluster. Tenants limit which topics, groups and connectors each role can see, and RBAC sets Allow, Deny or Stage per action and resource. The Kafka ACLs that authorise applications on Redpanda are viewed, created and cloned from the ACLs view, with each change recorded in the audit log. The role design in Factor House’s London workshop is a workable starting point: editors inspect topics and produce freely, every structural change, such as creating a topic or updating a schema, is staged for approval, and ACL edits are denied to topic owners and editors alike, so they stay with the platform team. Any action that no policy allows is implicitly denied, so a role that is given no inspect action on a topic cannot read it.

Granting production access for one task. A temporary policy grants a role, such as the on-call engineers’ role, inspect access on a production topic for a set time and then expires, and staged mutations hold a topic deletion or an offset reset until an administrator approves it. Data policies mask sensitive fields in data inspect results on the server. A temporary policy can deny or stage as well as allow, lasts seven days at most by default and can be given an exact expiry time. Kpow reads its RBAC file when it starts, and Factor House’s announcement of Factor Platform lists dynamic RBAC with no more restarts as one of that product’s changes, so on Kpow a temporary policy is also how a permission changes without a restart. On Redpanda Community, where topic deletion cannot be switched off at the broker, staging the TOPIC_DELETE action is the approval step in front of it.

Watching lag and running Connect. Kpow shows each consumer group down to partition level and resets, clears or skips offsets from the same view, and exports group lag with broker and topic metrics to Prometheus. Kafka Connect clusters run beside Redpanda are managed through the Connect REST API. Redpanda generates a Grafana dashboard from its own metrics endpoints with rpk generate grafana-dashboard, which covers broker latency and throughput over time, and the two views answer different questions. The monitoring stack shows what happened over time and whether to page someone, in the sense of the Google SRE book’s chapter on monitoring, and the UI shows what is happening now and what to do about it.

Keeping the record. The audit log records each action, data inspect queries included, with the user from the identity provider, and a webhook sends those records to Slack, Microsoft Teams or any HTTP endpoint, such as a SIEM collector. On Redpanda Community, which has no broker audit log, this is the only record of which engineer did what. A team with Redpanda Enterprise sends both trails to the same SIEM, Redpanda’s audit topic in OCSF format and Kpow’s records by webhook, and joins them on the SCRAM user Kpow connects with. An offset reset during an incident is a deliberate decision to skip or replay data, and Kpow’s audit log records it with the engineer who made it, where Redpanda’s default audit settings do not record it at all.

Running Redpanda beside other Kafka clusters. One Kpow instance manages up to 12 clusters, so Redpanda clusters sit in the same view as Apache Kafka, Confluent or Amazon MSK clusters run by other teams, under the same roles and audit log. Offset-preserving replication is something vendors have built for their own platforms. Redpanda’s Shadowing, an Enterprise feature, replicates between Redpanda clusters, and KIP-1279 proposes cluster mirroring in Apache Kafka itself. For a move between Apache Kafka and Redpanda the route Redpanda documents is MirrorMaker 2, and the tool’s part is to show the cluster being retired and the one replacing it side by side, with consumer group lag on each.

Kpow live demo

See the Kpow UI before you connect Redpanda

The live Kpow demo runs on two Apache Kafka clusters on Amazon MSK, not on Redpanda. It opens on MSK Secondary, where you can browse brokers, topics, consumer groups and schema registries, and open the __oprtr_audit_log topic where Kpow keeps its audit trail, with no signup. Connecting your own Redpanda cluster is the step to try next.

For platform teams choosing a Kafka tool for Redpanda.

Try the Kpow demo→

FAQ

What is the best Kafka UI for Redpanda?

On this page’s rubric, Kpow, with 88 of 100 points: it connects to Redpanda as an ordinary Kafka client, reads the built-in Schema Registry, adds time-boxed production access, approvals, masking, tenants and an audit trail that names each person, and runs as one container beside the cluster with no external database. Redpanda Console scores next and covers the most of Redpanda’s own Admin API, and Kafbat UI is the highest-scoring open-source option.

What is the best alternative to Redpanda Console?

On this page’s rubric, Kpow: it adds time-boxed production access, staged approvals, masking in data inspect, tenants and an audit trail that names each person, it works on Redpanda Community without a Redpanda Enterprise licence because the sign-in and roles are Kpow’s own, and one instance manages up to 12 clusters where a Console deployment reaches one. Kafbat UI is the closest open-source alternative. The Redpanda Console review compares the wider field.

What happens to Redpanda Console when the Enterprise trial licence expires?

New Redpanda clusters receive a 30-day trial licence automatically. Redpanda’s licensing overview says that once Console’s Enterprise features, sign-in, RBAC, debug bundles and partition reassignment, are enabled without a valid licence, every page redirects to a licence-expiration page and other access is restricted, and on the brokers read access to the audit log topic is denied while logging continues. Kpow’s sign-in, roles and audit log are licensed to Kpow itself, so they do not depend on a Redpanda licence.

Is Redpanda Console free?

Redpanda Console is source-available under the Business Source License, and its free build browses topics, consumer groups and schemas with no licence fee. Sign-in through OIDC or basic authentication, RBAC, debug bundles and partition reassignment need a Redpanda Enterprise licence whose price is not published, and Redpanda’s licensing overview says that without a valid licence, every page redirects to a licence-expiration page once those features are enabled. The same overview says code under the Redpanda Business Source License converts to Apache 2.0 four years after each merge and may not be offered to others as a commercial streaming or queuing service, which is the difference from Kafbat UI and AKHQ, both under Apache 2.0. The Redpanda Console review covers the licence in detail.

Does Kpow work with Redpanda’s Schema Registry?

Yes. Kpow connects to Redpanda’s built-in Schema Registry with SCHEMA_REGISTRY_URL, and since release 95.1 a team on Redpanda sets SCHEMA_REGISTRY_OBSERVATION_VERSION=1, because Kpow’s newer observation engine is not compatible with Redpanda’s registry (Kpow schema registry docs).

Does Kpow work with Redpanda Cloud?

Kpow connects to any Redpanda cluster through the Kafka API with standard client settings, so the same configuration reaches Self-Managed, BYOC and Redpanda Cloud clusters. Kpow’s documentation states the scope as Redpanda’s standard Kafka API and feature set, and the integration guide covers the production paths through Helm and the JAR.

How do I run Kpow with Redpanda in Docker?

The integration guide does it in three commands: create a Docker network, start a single Redpanda broker with its built-in Schema Registry, and start Kpow pointed at both. Kpow needs three settings for this, as the Redpanda provider page lists: BOOTSTRAP for the broker, SCHEMA_REGISTRY_URL for the registry, and SCHEMA_REGISTRY_OBSERVATION_VERSION=1. Community Edition is free for 3 clusters and 10 users.

Does a Kafka UI need to sit in the data path to govern access on Redpanda?

No. Kpow runs as one container beside the cluster, connects to Redpanda like any Kafka client and applies roles, masking, approvals and the audit trail to the people working through it, so producers and consumers keep connecting straight to the brokers. Conduktor Console also connects directly, while Conduktor’s data-level controls run in Gateway, a Kafka proxy that client applications connect through.

Can one Kafka UI manage Redpanda and Apache Kafka clusters together?

Kpow manages up to 12 clusters per instance across Redpanda, self-managed Apache Kafka, Confluent and Amazon MSK, under one set of roles and one audit log. Kafbat UI, AKHQ, Conduktor and Lenses on its Multi-Kafka Enterprise tier also reach several clusters from one deployment. A team moving from Apache Kafka to Redpanda can keep the cluster being retired and the Redpanda cluster replacing it in one Kpow view, with consumer group lag on each and the same roles and audit log across both, while Redpanda documents MirrorMaker 2 for moving the data itself. Redpanda Console connects to one broker cluster per deployment. The general comparison is the best tools to manage multiple Kafka clusters.

Which Kafka UI keeps an audit trail of who did what on Redpanda?

Kpow records every action and every data inspect query with the user from the identity provider, on any Redpanda edition. With Enterprise licences for both Console and the brokers, Redpanda Console’s user impersonation lets Redpanda’s own audit topic name the person behind each Kafka API and Admin API request. Conduktor and Lenses keep in-product audit logs, and Kafbat UI and AKHQ write opt-in audit topics.

Does Redpanda’s audit log record a consumer group offset reset?

Not on its default settings. Redpanda’s audit logging, an Enterprise feature, enables the management, authenticate and admin event types by default, and an offset reset reaches the broker as an OffsetCommit request, which Redpanda’s documentation places under the consume event type together with Fetch. Enabling consume events records the reset and also every fetch from every application, and the event names the connecting principal, which for a shared tool is the tool’s own user. Kpow’s audit log records the reset with the engineer who made it on any Redpanda edition.

Do JMX-based Kafka monitoring tools work with Redpanda?

Not for broker metrics. Apache Kafka brokers report metrics through JMX, and Redpanda has no JVM, so its documentation exposes metrics in Prometheus format on the /public_metrics and /metrics endpoints of the Admin API port. Tools that compute their figures from the Kafka API, as Kpow does, work on Redpanda as they do on Apache Kafka, and Redpanda’s own consumer lag gauges have to be enabled in the enable_consumer_group_metrics cluster property before they are exported.

How do I mask PII in Redpanda topics?

Kpow’s data policies mask fields such as card numbers or email addresses in data inspect results, nested JSON fields included, on any Redpanda edition with Kpow Enterprise. Redpanda’s Console documentation and licensing overview describe no masking feature. Kafbat UI and AKHQ mask from configuration the same way for every viewer, and Conduktor masks in its Console and, for the data itself, through Gateway Protect, a paid add-on to its proxy.

Is there a free Kafka UI for Redpanda?

Kpow Community Edition is free on up to 3 clusters and 10 users. Kafbat UI and AKHQ are open source, and Redpanda Console’s free build has no sign-in or access control. RBAC, masking, staged approvals and the audit log need Kpow Enterprise. More free options are compared in the best free Kafka UI tools.

How these tools were scored

Five of the six criteria are the same ones, scored the same way, as on Factor House’s best Kafka management tools for banks, so a tool scores the same on both pages wherever the evidence is the same; the sixth is specific to Redpanda, and it replaces the banking page’s on-prem and cloud criterion. Running Redpanda beside other Kafka clusters is covered in the FAQ, where Kpow manages up to 12 clusters per instance and Redpanda Console one per deployment. 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.

1. Out of the data path (counts three times). The tool should run in your own environment, reach 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, 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. This criterion is scored the same way on every Factor House page that uses it, and only its weight changes with the reader.

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 grant? 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, the broker usually sees only the tool’s own credentials, so only the tool’s log can name the person. Redpanda Console with Enterprise licences is the exception on this page, because impersonation puts each person’s identity on Redpanda’s own audit topic. The trail should include data reads as well as changes, and be readable without building a consumer first.

4. Directory and Kafka sign-in (counts once). People should sign in through the company directory, whether that is LDAP directly or SAML and OIDC in front of it, with directory groups mapped to roles, and the tool has to connect the way the cluster already authenticates clients. Kafka SSO tools covers the protocol detail.

5. Many teams, shared clusters (counts once). Several teams on one cluster need each team scoped to its own topics, consumer groups and connectors, so the platform team can onboard a team with a policy rather than a cluster.

6. Redpanda’s own APIs (counts once). Every tool here reaches Redpanda’s Kafka API and its Confluent-compatible Schema Registry. This criterion scores what goes beyond that: documented Redpanda setup and Schema Registry behaviour, and use of Redpanda’s Admin API for SCRAM users, data transforms, the Redpanda version and debug bundles. A tool with no Redpanda-specific documentation scores 6, a documented provider 7, and Redpanda’s own UI 10.

Costs are modelled for one production Redpanda 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 and Redpanda Console’s free build carry 6 hours a month, $8,640 a year, to run, secure and keep current; Redpanda Console’s sign-in, RBAC and the broker audit log need a Redpanda Enterprise licence whose price is not published, so that line cannot be modelled. Lenses Team is $4,000 a year for up to 15 users on one cluster, so 25 engineers is a custom quote and the $6,880 figure is a floor. Kpow’s $7,380 uses the published price of $4,500 per cluster with 100 users included; Kpow Community Edition is free for 3 clusters and 10 users, so a 25-engineer team is on Enterprise.

The criteria map onto Redpanda’s features in the figure below.

F1 From Redpanda feature to what a Kafka tool has to do

What Redpanda does What the tool has to do

Kafka API Implements the Kafka protocol in one C++ binary, with no ZooKeeper and no JVM Connect as an ordinary Kafka client and stay within the standard Kafka API and feature set

Built-in Schema Registry Runs a Confluent-compatible Schema Registry inside the brokers Read and manage subjects over the Confluent REST API and decode Avro, Protobuf and JSON Schema records

Client sign-in SASL/SCRAM and mTLS in every edition; OIDC and Kerberos with an Enterprise licence Connect with whichever mechanism the cluster enforces

RBAC and audit logging Enterprise features: roles on the broker, and an audit topic that names the user of each Kafka API and Admin API request Either pass each person's own identity through to the broker, or keep per-person roles and an audit trail of its own

Admin API SCRAM users, data transforms, the Redpanda version and debug bundles are handled over Redpanda's Admin API Call the Admin API, or leave those tasks to rpk and Redpanda Console

Where the tool runs Self-Managed, BYOC or Redpanda Cloud, with producers and consumers connecting to the brokers directly Run beside the cluster as a client, not as a proxy that applications route through

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, Production access on request counts twice, Audit trail per person counts twice, Directory and Kafka sign-in counts once, Many teams, shared clusters counts once and Redpanda's own APIs counts once, for a total out of 100. Out of the data path counts three times, and production access on request and the per-person audit trail count twice. Redpanda's appeal is a small footprint, one binary with no ZooKeeper and no JVM, and a tool that clients connect through, or that needs a database of its own, adds back the moving parts the team chose Redpanda to avoid. Production access and the audit trail are the two questions that decide whether a shared tool can be pointed at production at all: who could read or change it, and who actually did. Directory sign-in, shared clusters and coverage of Redpanda's own APIs 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 88 out of 100. The other options follow by total. Conduktor is listed last whatever its total; on its total of 58 it would place fourth.

Related reading

• Kafka: the complete guide
• What is Redpanda?
• Integrate Kpow with the Redpanda Streaming Platform
• Redpanda Console: pricing and alternatives
• Best Kafka UI tools for Amazon MSK
• Best Kafka management tools for banks
• Best Kafka governance tools for financial services