Best Kafka UI tools for Buf Schema Registry (BSR)

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	Multiple registries	Buf Schema Registry and Protobuf	Cost a year, one cluster (modelled)
1	Kpow	89	One container, no external database, not a proxy	Temporary policies, staged approvals, masking	Every action and data read, by user	SAML, OpenID, LDAP	Several registries per cluster	BSR named as supported; no dedicated guide	$7,380
2	Kafbat UI	63	One container	Read-only clusters, no approvals	Opt-in log, no view in the product	OAuth2, OIDC, LDAP	One per cluster	Confluent-compatible registry, no BSR guide	$8,640
3	AKHQ	57	One container	Group roles, no approvals	Opt-in topic, no reads	LDAP, OIDC	One per connection	Named by Buf; Protobuf deserialise only	$8,640
4	Lenses	50	HQ on PostgreSQL plus an agent per cluster	Global masking, no approvals	In-product audit log	SSO from Team tier	One per environment	Confluent-compatible registry, no BSR guide	$6,880 for 15 users; custom above
5	Redpanda Console	46	One container	None in the free build; RBAC licensed	No record on non-Redpanda brokers	OIDC (Enterprise)	One per deployment	Confluent-compatible registry, no BSR guide	$8,640 plus an unpublished licence for sign-in and RBAC
6	Conduktor	53	Console on PostgreSQL; Gateway, a proxy, for data-level controls	Masking exemptions, owner approval	70+ event types in the UI	LDAP, OIDC	One per cluster	Confluent-compatible registry, no BSR guide	$32,880; $122,880 with Gateway Core and Protect

The tools, ranked for the Buf Schema Registry

What teams on the Buf Schema Registry need

This page is about the Kafka tool a team runs beside the Buf Schema Registry: a UI for reading topics whose records are serialised against BSR schemas, managing consumer groups, and governing who does what. The BSR itself is Buf’s registry for Protobuf modules, run as a public service at buf.build and as a private instance on Buf’s Pro, Enterprise and on-prem plans, according to Buf’s BSR documentation. Its Kafka side is a Confluent Schema Registry compatible API: Buf’s post on why a Protobuf schema registry says the BSR implements the same API as the Confluent Schema Registry, so it works with most Kafka clients and management tools. One caveat sets the scope of everything below. When this page was written on 3 October 2026, Buf’s current documentation no longer carried its Confluent integration pages, which redirect to the BSR overview, so the integration details here come from Buf’s Confluent Schema Registry integration overview as archived in June 2025, which also states that the feature is only available on the Enterprise plan. A team should confirm the current terms with Buf. Registry tooling on any distribution is compared in the best tools for Kafka schema registry management.

Out of the data path. A Kafka management tool reaches a cluster in one of two ways: as an ordinary Kafka client beside the applications, or as a proxy that every producer and consumer connects through, and only the second sits in the data path, where the tool’s latency and outages become the cluster’s, as Kai Waehner’s review of Kafka proxies sets out alongside what a proxy can do that a client cannot. The BSR’s Confluent integration already follows the client pattern: in Buf’s guide to Kafka clients, as archived in November 2025, producers and consumers use the standard Confluent serializers, fetch the schema from the registry and serialise in their own process. A tool that reads the same registry over the same REST API fits that design, and adopting or removing it changes nothing about how any application connects. Kpow is one container with no external database, installed in your own environment and out of the data path. 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.

Schemas are authored in Buf, not in the tool. The same archived guide says the BSR’s Confluent Schema Registry API runs in READONLY mode and blocks clients from auto-registering schemas or changing configuration through the API. Schemas reach a Confluent subject when a developer annotates a Protobuf message with the bufbuild/confluent module and pushes it to the BSR, where Buf’s breaking change detection checks it first, and a subject’s compatibility mode defaults to BACKWARD_TRANSITIVE, according to the archived integration overview. Compatibility is enforced at build time, before a schema exists in the registry, rather than at registration. For a Kafka tool, that removes one job and sharpens another: the schema editor, delete buttons and compatibility settings that matter on other registries have nothing to act on here, and what the tool has to do well is read subjects, decode Protobuf records against them, produce test records with the registered schema, and govern who sees the decoded data.

Production access on request. A shared tool connects to the brokers as one principal, so the cluster’s own authorisation cannot tell the engineers working through it apart, and a BSR read-only registry protects schemas, not records. What a team needs from the tool is access to production for one task that then expires, and an approval step before a topic deletion or an offset reset runs. Kpow’s temporary policies grant a role an action on a resource for a set time, such as 30 minutes, and an administrator cannot grant more than their own permissions. For masking, field-level redaction needs a deserialiser that understands the record, and Kpow’s data policies list Protobuf among the formats it redacts by field, which matters on a cluster where most topics are Protobuf. Those controls are compared across the field in Kafka RBAC tools.

Audit trail per person. The BSR keeps an audit log, but of a different thing. Buf’s audit log documentation says a private BSR instance records mutations to the data it manages, across users, organizations, repositories and plugins, read by administrators through an audit API, and that the public BSR does not expose it. Nothing in that record says which engineer read which Kafka records once they were decoded. A complete audit trail records reads as well as writes, who searched which topic’s data and not only who changed what, which is the question asked about access to customer data. The fix is two-step authorisation: a person signs in to the tool with their own identity, the tool holds the technical Kafka and registry credentials and applies that person’s role, so every action is attributable to a named individual, the individual accountability NIST SP 800-53 catalogues. The options are compared in Kafka audit logging tools.

Directory and Kafka sign-in. Every client of the BSR’s Confluent API authenticates with a token: the archived client guide configures Confluent serializers with basic authentication whose user info is the BSR user name and token, and recommends a bot user per client application. Buf’s authentication documentation adds that a token can be limited to specific permissions and scopes, so a tool can be given a bot user and a scoped token of its own rather than an engineer’s. People should then sign in to the tool through the company directory with SAML, OpenID Connect or LDAP, so the token never reaches them.

Multiple registries. The BSR holds Protobuf, so a cluster with Avro or JSON Schema topics usually keeps a second registry beside it. The archived instance documentation also said that every user of a BSR can read every Confluent instance and its schemas, so the registry itself does not scope which team sees which subjects. A tool that attaches several registries to one cluster decodes each topic with the right one, and one with tenants gives each team its own view.

Buf Schema Registry and Protobuf. Registry support has two separate parts: the SerDes that read and write records, and the API that manages schemas. Because the BSR implements the Confluent API and works with the standard Confluent serializers, a tool needs no bespoke code to decode BSR-framed records, unlike AWS Glue, which has its own API and SerDes libraries, as the AWS Glue Schema Registry page describes. The first question about a team’s payloads is how they were framed. Built-in Protobuf deserialisers follow the Confluent wire format, where the schema ID travels in the first bytes of the record, so Protobuf written straight from generated code with no registry framing needs a custom SerDes, which Kpow’s SerDes documentation covers. Custom SerDes that contain generated Protobuf classes share the tool’s Protobuf library, so a major change such as Protobuf’s move to version 4 means regenerating them, as Kpow’s note on custom SerDes and Protobuf 4.31.1 announced.

No tool here leads on every point: Kpow does not author or version schemas in the BSR, which stays with the Buf CLI and the BSR’s own review flow, it has no dedicated BSR guide, and Lenses has the stronger query model with SQL over topics. Kpow governs people working through Kpow, so applications keep their own tokens and principals.

Who runs Kpow with the Buf Schema Registry

No Kpow customer has published an account of running Kpow with the Buf Schema Registry. The public record of the pairing is Factor House’s own: the BSR named on Kpow’s schema registry page, the Bufstream provider page, and the Kpow and Bufstream integration guide, which pair Kpow with a Buf registry’s Confluent-compatible endpoint. Teams on Bufstream, Buf’s Kafka-compatible broker now owned by CoreWeave, are covered on the Bufstream page. For customer accounts of Kpow with a schema registry, the Confluent Schema Registry page carries TD Bank and NORD/LB.

How a team runs the Buf Schema Registry with Kpow

Installing it beside the cluster. Kpow runs as one Docker container, from the Helm charts on Kubernetes, or as a Java JAR. It needs no external database, because its snapshots, metrics and audit log live in topics on the Kafka cluster itself, and it connects to the brokers with the same cluster settings as any producer or consumer.

Giving it a BSR identity of its own. Following Buf’s advice of a bot user per client application, the team creates a bot user for Kpow and a token for it, limited to the access it needs where Buf’s token scopes allow. Kpow’s schema registry configuration takes the instance’s Confluent-compatible URL in SCHEMA_REGISTRY_URL, and for a registry with basic authentication SCHEMA_REGISTRY_AUTH=USER_INFO with SCHEMA_REGISTRY_USER and SCHEMA_REGISTRY_PASSWORD; Buf’s archived client guide sets the same basic authentication with the BSR user name and token. Kpow’s documentation does not include a BSR-specific worked example, so a team checks the connection against its own instance first.

Holding the BSR beside other registries. SCHEMA_REGISTRY_RESOURCE_IDS attaches more than one registry to the same Kafka cluster, each with its own prefixed URL and credentials, so Protobuf topics decode through the BSR and Avro topics through the team’s other registry in the same view. Kpow fetches all schemas from a Confluent-compatible registry in one request by default, and the observation version setting switches to fetching them one at a time for a compatible registry that lacks the bulk endpoint, so a team checks which mode its BSR instance supports. The guide to Confluent-compatible registries shows several registries side by side in one Kpow.

Reading Protobuf records. Data inspect decodes records with the registry’s Protobuf SerDes, and engineers filter them across topics with kJQ. Data produce writes test records with the same SerDes, serialised against the schema the BSR already holds, which keeps hand-written test records in the shape every consumer expects.

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 the BSR token and the Kafka credentials stay with Kpow.

Giving teams their own view. Tenants limit which topics, groups and connectors each role can see, and RBAC sets Allow, Deny or Stage per action and resource.

Granting production access for one task. A temporary policy grants a role inspect access on a production topic for a set time and then expires, staged mutations hold a topic deletion or an offset reset until an administrator approves it, and data policies mask sensitive Protobuf fields in data inspect results on the server.

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, where they sit beside the BSR’s own audit events for schema changes.

Kpow live demo

See the Kpow UI before you connect the BSR

The live Kpow demo runs on two Apache Kafka clusters on Amazon MSK with their own registry, not the Buf Schema Registry. It shows the views a team gets with the BSR too: topics, consumer groups, schema registries, data inspect and the __oprtr_audit_log topic where Kpow keeps its audit trail, with no signup. Pointing Kpow at your own BSR instance is the step to try next.

For platform teams choosing a Kafka tool alongside the Buf Schema Registry.

Try the Kpow demo→

FAQ

What is the best Kafka UI for the Buf Schema Registry?

On this page’s rubric, Kpow, with 89 of 100 points: it names the BSR among the registries it supports, decodes Protobuf records through a Confluent-compatible registry, holds the BSR beside another registry on the same cluster, 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. Kafbat UI is the highest-scoring free option.

Does Kpow work with the Buf Schema Registry?

Kpow’s schema registry documentation names the Buf Schema Registry among the provider-specific registries it supports, and connects to a Confluent-compatible registry with SCHEMA_REGISTRY_URL and basic authentication settings. There is no dedicated BSR guide; the worked example on the Bufstream provider page uses Buf’s public demo registry.

Can I create or edit BSR schemas from a Kafka UI?

Not through the Confluent API. Buf’s client guide, as archived in November 2025, says the BSR’s Confluent Schema Registry API runs in READONLY mode and blocks clients from registering schemas or changing configuration. Schemas are published by pushing annotated Protobuf files to the BSR, and a Kafka UI reads and decodes them.

Does the Buf Schema Registry still support Kafka clients?

Buf’s post on why a Protobuf schema registry says the BSR implements the Confluent Schema Registry API. On 3 October 2026 Buf’s current documentation no longer carried its Confluent integration pages, and the archived pages describe the feature as Enterprise-only, so the current availability and plan are a question for Buf.

Does a Kafka UI need to sit in the data path to govern access to BSR-encoded data?

No. Kpow runs as one container, reads the registry and the topics like any 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 and the registry. Conduktor Console also connects directly, while Conduktor’s data-level controls run in Gateway, a Kafka proxy that client applications connect through.

Is there a free Kafka UI for the Buf Schema Registry?

Kpow Community Edition is free on up to 3 clusters and 10 users. Kafbat UI and AKHQ are open source. RBAC, masking, staged approvals, the audit log and more than one registry per cluster 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 ones Factor House scores on its other schema registry pages; the sixth is the Buf Schema Registry with its Protobuf records. 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 and the registry over the same private network your applications use 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, a tool with a database of its own 6, one with several databases or an agent per cluster 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, and none is scored here. 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). Whether an engineer can be granted access to production for one task and have it expire, 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.

3. Audit trail per person (counts twice). Whether the tool records each action, reads 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.

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

5. Multiple registries (counts once). Whether one Kafka cluster can have more than one registry attached, of the same or different kinds, and whether one deployment reaches several clusters. These scores are the same as on the best tools for Kafka schema registry management and the Confluent Schema Registry page for the tools scored there.

6. Buf Schema Registry and Protobuf (counts once). Whether the tool’s own documentation, or Buf’s, covers the Buf Schema Registry, and whether the tool decodes Protobuf records through a Confluent-compatible registry. A tool that can work through the standard API but is documented nowhere for the BSR scores 5; a tool Buf names scores 6; a tool whose own documentation names the BSR as supported, without a dedicated guide, scores 7.

Costs are modelled for one production 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. Kpow’s $7,380 uses the published price of $4,500 per cluster with 100 users included. Lenses publishes a Team price of $4,000 a year for up to 15 users on one cluster, so 25 engineers is a custom quote. 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; Conduktor prices Gateway per cluster with a 3-cluster minimum, and the listing does not say how many clusters that figure covers. The BSR’s own cost is not modelled, because it is the same whichever Kafka tool a team picks; Buf’s pricing page lists its plans. Kpow Community Edition is free for 3 clusters and 10 users, so a 25-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 Buf Schema Registry’s design in the figure below.

F1 From the Buf Schema Registry's design to what a Kafka tool has to do

What the Buf Schema Registry does What the tool has to do

Registry API Serves Protobuf schemas over a Confluent Schema Registry compatible API, one URL per instance Connect to that API like a Confluent client and decode Protobuf records without custom code

Schema changes Schemas are published with buf push and checked for breaking changes on the way in; the API is read-only to clients Read subjects and versions, and leave authoring and compatibility to the Buf workflow

Credentials Clients authenticate with a BSR user name and token; Buf recommends a bot user per client application Take a token of its own, held as registry credentials, so the tool is one more named client

Audit A private instance logs changes to users, organizations, repositories and plugins Record who read or changed which Kafka records, which the registry never sees

Other registries Holds Protobuf only, so Avro and JSON Schema topics keep another registry Attach more than one registry to the same cluster and decode each topic with the right one

Where the tool runs Clients fetch schemas and serialise in their own libraries, with the registry beside the brokers Run beside the cluster as a client, not as a proxy that applications route through, and need no database of its own

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, Multiple registries counts once and Buf Schema Registry and Protobuf counts once, for a total out of 100. Out of the data path counts three times. The Buf Schema Registry's Confluent integration is built so that producers and consumers fetch Protobuf schemas and serialise records in their own client libraries, with the registry beside the brokers, and a tool that applications connect through, or that needs a database of its own, adds the component that design leaves out. Production access on request and the per-person audit trail count twice, because the registry only records changes to schemas, so who could read decoded records, and who actually did, is recorded nowhere unless the tool records it. Directory sign-in, holding several registries, and the Buf Schema Registry with its Protobuf records 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 53 it would place fourth.

Related reading

• Kafka: the complete guide
• Best tools for Kafka schema registry management
• Best Kafka UI tools for Confluent Schema Registry
• Best Kafka UI tools for AWS Glue Schema Registry
• Best Kafka UI tools for Bufstream
• Integrate Kpow with Bufstream
• Best Kafka audit logging tools
• Best Kafka management tools for banks