Best Kafka UI tools for StreamNative Schema Registry
ComparisonsThe best Kafka UI tool for StreamNative Schema Registry is one that reaches the registry the way StreamNative secures it, with an API key as the basic authentication password, reads it subject by subject because the registry answers the bulk schema listing with 404, shows the compatibility each subject actually has, holds deletions for approval because nothing can be undeleted, keeps an audit trail that names each person, and runs as one container beside the cluster, out of the data path. Kpow, Kafbat UI, the StreamNative Cloud Console, AKHQ, Lenses and Conduktor are compared here. Scored on the six weighted criteria explained below the rankings, Kpow ranks first with 90 out of 100, ahead of Kafbat UI at 63 and the StreamNative Cloud Console at 59.
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 | StreamNative registry depth | Cost a year, one cluster (modelled) |
|---|---|---|---|---|---|---|---|---|---|
| 1 | Kpow | 90 | 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 of any kind per cluster (Enterprise) | Documented: API key auth, per-subject observation | $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 | Basic auth; no StreamNative guidance | $8,640 |
| 3 | StreamNative Cloud Console | 59 | Nothing to deploy | Per-subject roles, no approvals or expiry | Audit log documented for Pulsar clusters | StreamNative accounts, SSO | StreamNative clusters only | Switches it on and binds roles; schema work over REST | $0 |
| 4 | AKHQ | 56 | One container | Regex groups, no approvals | Opt-in topic, no reads | LDAP, OIDC | One per cluster | Basic auth; no StreamNative guidance | $8,640 |
| 5 | Lenses | 50 | HQ and agent databases | Global masking, no approvals | Readable in the product | SSO on paid tiers | One per environment | StreamNative guide covers brokers only | $6,880 for 15 users; 25 is quoted |
| 6 | Conduktor | 53 | PostgreSQL, and Gateway for data controls | Masking exemptions, owner approvals | 70+ event types in the UI | LDAP, OIDC | One per cluster | Basic auth; no StreamNative guidance | $32,880; $122,880 with Gateway Core and Protect |
The tools, ranked for StreamNative Schema Registry
Rank 1 Kpow
90 out of 100 Total
Try Kpow in the live demo No signup needed.
- Cost a year
- $4,500 per cluster with 100 users included, plus about $2,880 in operator time, so $7,380 on one cluster (modelled)
- On StreamNative Schema Registry
- Documented provider; API key as the basic auth password and SCHEMA_REGISTRY_OBSERVATION_VERSION=1
- Deployment
- One container or JAR, no external database
- Out of the data path ×3 weight, this criterion counts 3 times toward the total
- 9 out of 10
- Production access on request ×2 weight, this criterion counts 2 times toward the total
- 9 out of 10
- Audit trail per person ×2 weight, this criterion counts 2 times toward the total
- 9 out of 10
- Directory and Kafka sign-in
- 9 out of 10
- Multiple registries
- 10 out of 10
- StreamNative registry depth
- 8 out of 10
Why these scores for Kpow
- Out of the data path 9 out of 10
- It is one container or JAR whose snapshots, metrics and audit log live in topics on your own cluster, and it reads the registry over its REST API like any client, so nothing sits between your applications and the brokers or the registry.
- Production access on request 9 out of 10
- Temporary policies grant time-boxed access that an admin or a change system calling the Kpow API can create, staged mutations hold any action for approval, and data policies mask fields in inspection, though masking is per resource rather than per viewer.
- Audit trail per person 9 out of 10
- Every action is recorded with the user from the identity provider and the policy that allowed it, including data inspect queries, with a seven-day view in the product, the record written to an audit topic on your own cluster, and webhooks that send it to a SIEM for long-term retention.
- Directory and Kafka sign-in 9 out of 10
- People sign in with SAML, OpenID or LDAP, and Kpow connects to the brokers with the same SASL or TLS settings as any Kafka client.
- Multiple registries 10 out of 10
- Several registries of different kinds can be attached to one Kafka cluster with
SCHEMA_REGISTRY_RESOURCE_IDS, Confluent beside Apicurio, Karapace, AWS Glue or Google’s registry, which no other UI here documents; more than one registry needs Kpow Enterprise. - StreamNative registry depth 8 out of 10
- Its StreamNative documentation covers the registry with basic authentication, any username and the API key as the password, and
SCHEMA_REGISTRY_OBSERVATION_VERSION=1, which reads each subject’s metadata and compatibility in turn instead of the bulk call the registry answers with 404; it manages subjects, compatibility and soft-deleted schemas, and it does not document mutual TLS or OAuth to this registry.
On StreamNative Schema Registry. Kpow’s StreamNative provider page adds the registry with SCHEMA_REGISTRY_URL, SCHEMA_REGISTRY_AUTH=USER_INFO, any username and the API key as SCHEMA_REGISTRY_PASSWORD, plus SCHEMA_REGISTRY_OBSERVATION_VERSION=1. The schema registry configuration describes that mode: Kpow lists the subjects in one query, then makes two calls per schema, one for its metadata and one for its compatibility. The schema management page covers creating subjects in Avro, JSON Schema or Protobuf, editing them, updating compatibility, deleting subjects and permanently deleting soft-deleted ones.
Where it falls short. Kpow governs people working through Kpow; producers and consumers still call the registry with their own API keys, and StreamNative’s roles remain the control for them. The per-subject mode costs more REST calls than the bulk mode on a large registry, which is the price of reading a registry without the bulk endpoint. Kpow’s StreamNative documentation covers basic authentication only, not the mutual TLS or OAuth that StreamNative also offers for the registry. More than one registry per cluster, RBAC, masking, staged mutations and the audit log need Kpow Enterprise; Community Edition is free for 3 clusters and 10 users.
Compare Kpow vs Kafbat UIKpow vs AKHQ
Rank 2 Kafbat UI
63 out of 100 Total
- Cost a year
- $0 licence, about $8,640 in operator time (modelled)
- On StreamNative Schema Registry
- Basic auth to a Confluent-compatible registry; no StreamNative guidance
- Sign-in
- OAuth2, OIDC and LDAP, free
- Out of the data path ×3 weight, this criterion counts 3 times toward the total
- 9 out of 10
- Production access on request ×2 weight, this criterion counts 2 times toward the total
- 4 out of 10
- Audit trail per person ×2 weight, this criterion counts 2 times toward the total
- 6 out of 10
- Directory and Kafka sign-in
- 7 out of 10
- Multiple registries
- 4 out of 10
- StreamNative registry depth
- 5 out of 10
Why these scores for Kafbat UI
- Out of the data path 9 out of 10
- It is one stateless container with no database and no proxy, the same pass as Kpow.
- Production access on request 4 out of 10
- RBAC grants actions per resource and a cluster can be set read-only, but there is no approval step, no time-boxed grant, and its masking applies the same way to every viewer.
- Audit trail per person 6 out of 10
- Its audit log names the logged-in user and records reads when the level is set to ALL, but it writes to a topic or the console with no view in the product, so reading the trail is something you build.
- Directory and Kafka sign-in 7 out of 10
- It supports OAuth2 and OIDC, including Microsoft Entra ID, and LDAP or Active Directory, and its documentation does not list SAML.
- Multiple registries 4 out of 10
- It manages one registry per Kafka cluster, with extra registries only as deserializers, and many clusters per install.
- StreamNative registry depth 5 out of 10
- Its registry settings take a username and password, the form StreamNative’s registry accepts with the API key as the password, and it edits schemas and compatibility, but its documentation says nothing about the endpoints StreamNative’s registry leaves out, so how it lists schemas there is the reader’s to test.
On StreamNative Schema Registry. Kafbat UI attaches one Confluent-compatible registry to each cluster connection. Its configuration reference lists SCHEMAREGISTRYAUTH settings for a username and password, which is how StreamNative’s registry is reached with an API key. The Kafbat UI review covers its RBAC and release history.
Where it falls short. There is no way to grant production access for an hour and have it expire, no approval before a compatibility change or a subject deletion runs, and masking cannot exempt the team that owns the data. Nothing published says how it behaves against a registry that returns 404 for the bulk schema listing. No vendor is under contract to ship fixes. Its modelled running cost on one cluster is 6 engineer-hours a month, $8,640 a year at $120 an hour.
Rank 3 StreamNative Cloud Console
streamnative.io
59 out of 100 Total
- Cost a year
- $0 licence and nothing to run; it comes with the service (modelled)
- On StreamNative Schema Registry
- Switches the registry on and binds its per-subject roles
- Deployment
- Nothing to deploy
- Out of the data path ×3 weight, this criterion counts 3 times toward the total
- 10 out of 10
- Production access on request ×2 weight, this criterion counts 2 times toward the total
- 3 out of 10
- Audit trail per person ×2 weight, this criterion counts 2 times toward the total
- 4 out of 10
- Directory and Kafka sign-in
- 6 out of 10
- Multiple registries
- 3 out of 10
- StreamNative registry depth
- 6 out of 10
Why these scores for StreamNative Cloud Console
- Out of the data path 10 out of 10
- There is nothing to deploy and nothing between clients and brokers, since this is StreamNative’s own control plane, which makes it the best on this criterion.
- Production access on request 3 out of 10
- Its predefined schema roles, from schema-reader to schema-owner, can be bound to one subject or a subject prefix, which keeps each team to its own schemas, but StreamNative’s documentation describes no approval before a change, no grant that expires and no masking of record contents.
- Audit trail per person 4 out of 10
- StreamNative’s cloud audit log is documented for Pulsar clusters, is switched on per cluster with produce and consume events off by default, and is written to topics a team has to consume, and the registry itself identifies callers by API key rather than by person.
- Directory and Kafka sign-in 6 out of 10
- Engineers use their own StreamNative user accounts, which can be linked to single sign-on, and the console is not a Kafka client a team configures for other clusters.
- Multiple registries 3 out of 10
- It shows the registry that each StreamNative cluster serves on its own endpoint, and nothing outside StreamNative Cloud.
- StreamNative registry depth 6 out of 10
- The registry is switched on and its roles bound here, but StreamNative’s registry documentation describes registering schemas, reading versions and setting compatibility through the REST API, and this page found no schema browser or version comparison in the console’s documentation.
What it covers. The StreamNative Cloud Console administers the organisation, its clusters, service accounts, API keys and role bindings. StreamNative’s RBAC roles include four for the registry, scoped per subject, and its registry security page suggests schema-writer for producers, schema-reader for consumers, schema-manager for CI pipelines that check compatibility and schema-owner for platform owners.
Where it falls short. Day-to-day registry work in StreamNative’s schema management guide is a set of REST calls with an API key, so reading a subject’s versions or comparing two of them is done outside the console. Its audit log is documented for Pulsar clusters and records management events by default. Registries outside StreamNative Cloud are outside it.
Rank 4 AKHQ
56 out of 100 Total
- Cost a year
- $0 licence, about $8,640 in operator time (modelled)
- On StreamNative Schema Registry
- Basic auth to a Confluent-compatible registry; no StreamNative guidance
- Security default
- Disabled until you enable it
- Out of the data path ×3 weight, this criterion counts 3 times toward the total
- 9 out of 10
- Production access on request ×2 weight, this criterion counts 2 times toward the total
- 3 out of 10
- Audit trail per person ×2 weight, this criterion counts 2 times toward the total
- 4 out of 10
- Directory and Kafka sign-in
- 6 out of 10
- Multiple registries
- 4 out of 10
- StreamNative registry depth
- 5 out of 10
Why these scores for AKHQ
- Out of the data path 9 out of 10
- It is one stateless container with no database and no proxy, the same pass as Kpow.
- Production access on request 3 out of 10
- Groups bind actions to resources by regex, but there is no approval step or time-boxed grant, masking is global, and without the JWT signing secret the restriction is in the UI only.
- Audit trail per person 4 out of 10
- Audit events are opt-in to a Kafka topic, reads are not recorded, and there is no view for the trail.
- Directory and Kafka sign-in 6 out of 10
- It supports LDAP, OIDC and header authentication from a proxy, does not list SAML, and ships with security disabled until you enable it.
- Multiple registries 4 out of 10
- It manages one registry per cluster connection, Confluent or TIBCO, with Glue for decoding only.
- StreamNative registry depth 5 out of 10
- Its connection settings take a basic authentication user and password, which StreamNative’s registry accepts with the API key as the password, but its documentation says nothing about the endpoints StreamNative’s registry leaves out.
On StreamNative Schema Registry. AKHQ sets a schema-registry block on each cluster connection with a URL, basic-auth-username and basic-auth-password (AKHQ cluster configuration), so the API key goes in as the password and any value as the username. The AKHQ review covers the rest.
Where it falls short. Security is off until you configure it, the audit trail is an opt-in topic that does not record reads, and there is no approval step or time-boxed grant. Nothing published says how it behaves against StreamNative’s registry. Its modelled running cost on one cluster is 6 engineer-hours a month, $8,640 a year at $120 an hour.
Compare Kpow vs AKHQAKHQ review
Rank 5 Lenses
lenses.io
50 out of 100 Total
- Cost a year
- Team is $4,000 for up to 15 users on one cluster, $6,880 with operator time; 25 engineers needs a custom quote (modelled)
- On StreamNative Schema Registry
- StreamNative's Lenses guide covers the broker connection only
- Deployment
- HQ on PostgreSQL plus an agent and database per cluster
- Out of the data path ×3 weight, this criterion counts 3 times toward the total
- 4 out of 10
- Production access on request ×2 weight, this criterion counts 2 times toward the total
- 4 out of 10
- Audit trail per person ×2 weight, this criterion counts 2 times toward the total
- 7 out of 10
- Directory and Kafka sign-in
- 7 out of 10
- Multiple registries
- 4 out of 10
- StreamNative registry depth
- 5 out of 10
Why these scores for Lenses
- Out of the data path 4 out of 10
- It runs a central HQ on PostgreSQL plus an agent and an agent database beside every cluster, and HQ has no high-availability option.
- Production access on request 4 out of 10
- Its masking is the strictest view-time model, global with no escape even for admins, but no approval step or time-boxed grant is described.
- Audit trail per person 7 out of 10
- Audit logs can be read in the product, with no need to build a consumer first.
- Directory and Kafka sign-in 7 out of 10
- SSO spans Okta, Keycloak, OneLogin, Google and Entra ID, with basic authentication only on Community.
- Multiple registries 4 out of 10
- Its agent connects to the Schema Registry of each environment, one registry beside each Kafka cluster, with many environments under one HQ.
- StreamNative registry depth 5 out of 10
- It works with Confluent-compatible registries in general, and StreamNative’s own Lenses guide stops at the broker connection, so whether it reads a registry without the bulk endpoint is the reader’s to verify.
On StreamNative Schema Registry. StreamNative publishes a guide to connecting Lenses to its clusters, which covers the broker connection and a first query and does not mention the registry. SQL over topics is the centre of Lenses and the strongest query model on this page. The Lenses review covers its tiers and deployment.
Where it falls short. A central HQ on PostgreSQL plus an agent and an agent database for every cluster is a heavy footprint beside a managed registry that needs none. The Team licence stops at 15 users on one cluster, so a larger team is on a custom quote.
Compare Kpow vs LensesLenses review
Rank 6 Conduktor
conduktor.io
53 out of 100 Total
- Cost a year
- 25 Console seats at $1,200 is $30,000 plus $2,880 operator time, so $32,880; Gateway Core adds $60,000 and Gateway Protect, which carries encryption and masking, a further $30,000 (modelled)
- On StreamNative Schema Registry
- Basic auth to a Confluent-compatible registry; no StreamNative guidance
- Deployment
- Console on PostgreSQL 13+; data-level controls through Gateway, a proxy
- Out of the data path ×3 weight, this criterion counts 3 times toward the total
- 3 out of 10
- Production access on request ×2 weight, this criterion counts 2 times toward the total
- 6 out of 10
- Audit trail per person ×2 weight, this criterion counts 2 times toward the total
- 8 out of 10
- Directory and Kafka sign-in
- 7 out of 10
- Multiple registries
- 4 out of 10
- StreamNative registry depth
- 5 out of 10
Why these scores for Conduktor
- Out of the data path 3 out of 10
- Console needs PostgreSQL 13 or later, and its encryption, data-level masking and Virtual Clusters only work when client traffic goes through Gateway, a proxy in the data path.
- Production access on request 6 out of 10
- Masking can exempt users or groups, which beats every other tool here on who sees unmasked data, and cross-team access requests are approved by the owning team, but no expiring grant is described and topic creation that passes policy is a direct API call.
- Audit trail per person 8 out of 10
- Console logs produce, consume and admin requests across more than 70 event types with user, IP and timestamp, browsable in the UI and exported as CloudEvents.
- Directory and Kafka sign-in 7 out of 10
- Its SSO configuration covers LDAP and OIDC, with guides for Okta, Entra ID and Keycloak, and does not describe SAML.
- Multiple registries 4 out of 10
- Its cluster reference takes one registry per Kafka cluster, with many clusters per Console.
- StreamNative registry depth 5 out of 10
- Its registry connection takes basic authentication, which StreamNative’s registry accepts with the API key as the password, and its Console compares versions and checks compatibility, but Conduktor publishes no guidance on the endpoints StreamNative’s registry leaves out, including the global compatibility setting it cannot change.
On StreamNative Schema Registry. Conduktor’s Console documentation describes creating subjects, changing compatibility per subject or globally, checking compatibility before an update and comparing schemas of one subject. On StreamNative’s registry the global setting cannot be changed, so only the per-subject part applies. Its resource reference documents a registry per Kafka cluster with BasicAuth security. The Conduktor review covers the rest.
Where it falls short. Console connects to the registry directly and needs PostgreSQL. Conduktor’s data-level controls, such as encryption, masking of the data itself and virtual clusters for multi-tenancy, run in Gateway, a Kafka proxy that client applications connect through. On AWS Marketplace, Conduktor Enterprise lists Console at $1,200 a seat for the first 100 seats, Gateway Core, which carries virtual clusters, at $60,000 a year, and Gateway Protect, the add-on for encryption and masking, at a further $30,000.
Compare Conduktor review
What teams using StreamNative Schema Registry need
This page is about tools for a team whose schemas live in StreamNative’s Kafka Schema Registry and that wants one UI for reading topics, managing subjects and governing who does what. The wider StreamNative picture, from broker sign-in to consumer lag, is ranked on the best Kafka UI tools for StreamNative Cloud, and the registries themselves are compared in the best tools for Kafka schema registry management. Teams on Confluent’s own registry have their own page.
Registry support in any tool has two parts: the serialisers that read and write records, and the REST API that manages schemas. StreamNative’s registry overview says the standard Confluent serialisers and REST clients work against its registry unchanged, so decoding Avro, JSON Schema and Protobuf records is the easy part. The API is where it differs: StreamNative’s Confluent API compatibility reference states that the registry implements a subset of Confluent’s API and lists the endpoints that return 404 and the ones that behave differently. A tool that has only been tested against Confluent’s registry finds those differences in production.
Out of the data path. The registry is not a separate service on StreamNative Cloud. Its security page says it is served over HTTPS on port 443 at the cluster’s own endpoint and inherits the cluster’s network settings, so a cluster reachable only over private networking has a registry reachable only there too. A management tool fits that design when it runs in a network that already reaches the cluster and calls the registry as one more client. Kpow is one container with no external database, installed in your own environment and out of the data path; its working state lives in topics on the cluster. Conduktor’s data-level controls, by contrast, run in Gateway, a proxy that client applications connect through, as Conduktor’s Gateway documentation describes. A proxy of that kind is one more component every producer and consumer then depends on, which Kai Waehner’s review of Kafka proxies sets out as the trade-off.
Production access on request. Deletion on this registry is final in a way Confluent users may not expect. The compatibility reference says a soft-deleted schema stays readable but cannot be restored, registering the same schema again creates a new version rather than reviving the old one, and a hard delete, which needs a soft delete first, is unrecoverable. It also blocks deleting a schema that another schema references. A role that can read schemas but not delete them, an approval step before a deletion or compatibility change runs, and production access for one task that then expires are therefore worth more here than on a registry with an undo. Some Pulsar clusters still authorise the registry through one ACL on its backing topic, where, in the security page’s words, the same produce grant covers reads and writes; on those clusters the tool’s own roles are the only way to give someone a read-only view.
Audit trail per person. StreamNative’s registry checks only the password of a basic authentication request, which is an API key, and ignores the username. Its security page warns against relying on the username to identify a caller in audit trails, because the identity comes entirely from the key. When a team shares one tool, every schema change it makes reaches the registry under the tool’s key, so the record of which engineer deleted a subject or changed a compatibility level has to come from the tool. NIST’s SP 800-53 gives audit and accountability a control family of its own, and the options are compared in the best tools for Kafka audit logging.
Directory and Kafka sign-in. The tool has to sign people in through the company directory and connect to the brokers and the registry with what each requires. StreamNative’s registry takes basic authentication with an API key from every Kafka client, OAuth2 from the Java client with a custom token provider, and mutual TLS on a TLS listener. A tool that signs people in through SAML, OpenID Connect or LDAP keeps the API key in its configuration rather than in each engineer’s hands.
Multiple registries. Teams reach StreamNative from somewhere else. StreamNative’s registry replaces Confluent’s Schema Linking with Universal Linking for replicating schemas between clusters, and a team moving across may keep its old registry beside the new one for a while. A tool that attaches one registry per cluster shows only one of them beside the topics they serve.
StreamNative registry depth. Three behaviours separate a tool that works with this registry from one that merely connects. The bulk GET /schemas call returns 404, so a tool that loads a registry in one request fails and one that reads subject by subject works. The global GET /config always reports NONE, while the real default is BACKWARD and there is no call to change it, so a tool that shows the registry-wide value shows the wrong one, and the level that applies is the one read from each subject. And data contracts are not supported: a schema registered with rules is accepted, the rules are discarded and no error is returned, so a team migrating from Confluent loses rule enforcement without any signal, as the compatibility reference warns. A tool cannot restore those rules; what it can do is show each subject’s real level and keep compatibility changes behind a role.
The registry’s checks also stop short of the data. StreamNative’s broker-side schema ID validation rejects a record whose schema ID is missing or not registered for the topic’s subject, but it does not inspect the payload or check that the data matches the schema. A record can carry a valid ID and still be wrong, and reading the decoded records is how a team finds it.
No tool here leads on every point. The StreamNative Cloud Console needs nothing deployed and is where the registry’s per-subject role bindings are made, which Kpow does not do, and Lenses has the stronger query model with SQL over topics. Kpow governs people working through Kpow, so applications keep their own API keys and StreamNative’s roles stay the control for them.
Kpow and StreamNative on the public record
No Factor House customer has yet described in public how it runs Kpow against StreamNative’s registry, so this page names none. What is on the record comes from the two vendors. StreamNative lists Factor House in its partner directory, on a Factor House partner page, and included it in the launch partners for StreamNative Kafka Service. The Kpow StreamNative provider page documents both settings the registry needs, and the walkthrough Integrate Kpow with StreamNative Cloud on this site sets up the brokers and the registry together. Customer accounts of Kpow’s schema work on other registries are on the Confluent Schema Registry page.
How a team runs Kpow with StreamNative Schema Registry
Installing it beside the cluster. Kpow runs as one Docker container, a Java JAR or through the Helm charts, in a network that reaches the StreamNative cluster’s endpoint, which serves the registry too. It needs no external database, because its snapshots, metrics and audit log live in topics on the cluster itself.
Choosing the service account. Kpow connects with a StreamNative service account and its API key. The StreamNative provider page recommends the instance-owner role for that account, because Kpow manages and monitors the whole cluster, and then narrowing what each engineer can do inside Kpow. A narrower registry role has a quiet cost: StreamNative’s security page says GET /subjects returns only the subjects the key can read, with no permission error, so an account scoped to one team’s prefix shows a registry that looks partly empty. Subject names also map to Pulsar coordinates; the compatibility reference parses a RecordNameStrategy subject such as com.acme.MyRecord as tenant com and namespace acme, so the account has to cover those tenants and namespaces too.
Connecting to the registry. The registry is added with SCHEMA_REGISTRY_URL and SCHEMA_REGISTRY_AUTH=USER_INFO, any value as SCHEMA_REGISTRY_USER and the API key as SCHEMA_REGISTRY_PASSWORD, plus SCHEMA_REGISTRY_OBSERVATION_VERSION=1. Kpow’s default observation reads every schema in one call to GET /schemas, which StreamNative’s registry answers with 404; the observation version setting makes Kpow list the subjects first and then fetch each schema’s metadata and compatibility in turn. Reading compatibility per subject is also what returns the level actually in force, rather than the NONE the global setting reports. Setting SCHEMA_REGISTRY_STARTUP_VALIDATION=false lets Kpow start while the registry is unreachable and reconnect when it returns.
Managing subjects. The schema view creates subjects in Avro, JSON Schema or Protobuf, edits them, updates a subject’s compatibility and deletes subjects, and it lists soft-deleted schemas apart from live ones, with permanent deletion as a separate step, which matches StreamNative’s soft-then-hard delete order. RBAC sets Allow, Deny or Stage per schema action, so a team can read its subjects while a compatibility change or a deletion waits for approval through staged mutations.
Reading and producing records. Data inspect decodes Avro, JSON Schema and Protobuf records with the registry’s serialisers and filters them with kJQ, which is how a record with a valid schema ID but wrong contents is found. Data produce fetches the registered schema and serialises test records against it. Data policies mask sensitive fields in inspect results on the server.
Signing people in and granting access. Engineers sign in through SAML, OpenID Connect or LDAP, tenants give each team its own view of a shared cluster, and a temporary policy grants one role production access for a set time.
Keeping the record. The audit log records each action, schema changes and data inspect queries included, with the user from the identity provider, which is the per-person record a registry that sees only the tool’s API key cannot keep. A webhook sends those records to Slack, Microsoft Teams or any HTTP endpoint, such as a SIEM collector.
Kpow live demo
See the Kpow schema view
The live Kpow demo runs on Apache Kafka clusters on Amazon MSK, and the registry attached to it is AWS Glue rather than StreamNative's. Open Schema to see the view a team gets on StreamNative Schema Registry too: each subject with its version, compatibility mode and status, with no signup.
For platform teams choosing a Kafka tool for StreamNative Schema Registry.
Try the Kpow demoFAQ
What is the best Kafka UI for StreamNative Schema Registry?
On this page’s rubric, Kpow, with 90 of 100 points: it is the only tool here with documented settings for StreamNative’s registry, it reads the registry subject by subject, manages subjects, compatibility and soft-deleted schemas under per-person roles and approvals, and runs as one container with no external database. Kafbat UI is the highest-scoring free option.
Does Kpow support StreamNative Schema Registry?
Yes. Kpow’s StreamNative provider page covers the registry with two settings: basic authentication where only the password, the API key, is checked, and SCHEMA_REGISTRY_OBSERVATION_VERSION=1.
Why does Kpow need observation version 1 for StreamNative’s registry?
Kpow’s default observation fetches every schema with one call to GET /schemas, and StreamNative’s registry does not implement that endpoint and returns 404, as its compatibility reference lists. Version 1 lists the subjects and then reads each one, which works on any registry that serves GET /subjects.
Do Confluent data contract rules work on StreamNative Schema Registry?
No. StreamNative’s compatibility reference lists data contracts, including CEL and JSONata rules, as not supported, and a schema registered with rules is stored without them and with no error. Teams that depend on those rules need another way to enforce them before moving.
Does a Kafka UI need to sit in the data path to govern schema changes?
No. Kpow reads the registry over its REST API and applies roles, approvals and the audit trail to the people working through it, while producers and consumers keep calling the registry with their own API keys. 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 StreamNative Schema Registry?
Kpow Community Edition is free on up to 3 clusters and 10 users. Kafbat UI and AKHQ are open source, and the StreamNative Cloud Console comes with the service. Several registries per cluster, RBAC, masking, staged approvals and the audit log need Kpow Enterprise. More free options are compared in the best free Kafka UI tools in 2026.
How these tools were scored
Four of the six criteria are the ones Factor House scores on every page for teams that share a Kafka cluster; the other two are holding several registries and the depth of StreamNative Schema Registry support. They are listed here in order of weight. Each criterion is scored 0 to 10: 10 where a tool is the only one here doing it or clearly the best, 8 for a clean documented pass, 5 or 6 for partial support or support that needs work the reader must verify, 1 to 4 for a weak or indirect form, and 0 where it is absent. The first five criteria carry the same scores as on the best Kafka UI tools for Confluent Schema Registry for every tool scored on both pages, and the StreamNative Cloud Console’s out of the data path score is the one it has on the StreamNative Cloud page.
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 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 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, which here is the StreamNative Cloud Console. 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, such as a subject deletion or a compatibility 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.
6. StreamNative registry depth (counts once). Whether the tool documents connecting to StreamNative’s registry, reads it without the bulk endpoint, shows each subject’s own compatibility, and manages subjects and soft-deleted schemas. A tool that connects to Confluent-compatible registries with basic authentication but publishes nothing about StreamNative’s differences scores 5. Kpow scores 8 rather than higher because its StreamNative documentation covers basic authentication only.
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. The StreamNative Cloud Console comes with the service, so it adds nothing to run. 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. 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 in 2026.
The criteria map onto StreamNative Schema Registry’s behaviour in the figure below.
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 StreamNative registry depth counts once, for a total out of 100. Out of the data path counts three times. StreamNative Schema Registry is served on the cluster's own endpoint, and producers and consumers fetch schemas through their own serialisers, so a tool that applications connect through, or that needs a database of its own, adds a component the managed service was chosen to avoid. Production access on request and the per-person audit trail count twice, because the registry identifies a caller by API key alone and has no undelete, so neither a deleted subject nor a compatibility change made through a shared tool can be traced to a person by the registry, and the deletion cannot be undone. Directory sign-in, holding several registries, and the depth of StreamNative Schema Registry support 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 90 out of 100. The other options follow by total. Conduktor is listed last whatever its total; on its total of 53 it would place fifth.
Related reading
- Kafka: the complete guide
- Best Kafka UI tools for StreamNative Cloud
- Best tools for Kafka schema registry management
- Best Kafka UI tools for Confluent Schema Registry
- Best Kafka UI tools for AWS Glue Schema Registry
- Integrate Kpow with StreamNative Cloud
- Best Kafka governance tools for financial services