Best Kafka UI tools for StreamNative Cloud
ComparisonsThe best Kafka UI tool for StreamNative Cloud is one that signs in the way the service already authenticates clients, with a service account’s token over SASL/PLAIN, reads StreamNative’s Schema Registry despite the endpoints it leaves out, and adds per-person roles, time-boxed production access, masking and an audit trail on top of StreamNative’s own roles, from one container in your own environment that stays out of the data path. Kpow, Lenses, Kafbat UI, the StreamNative Cloud Console, AKHQ and Conduktor each cover part of that. Scored on the six weighted criteria explained below the rankings, Kpow ranks first with 85 out of 100, ahead of Lenses and Kafbat UI, level at 67; Conduktor, listed last, totals 66.
Tools compared
| Rank | Tool | Total (out of 100) | Governance beyond platform RBAC | Out of the data path | StreamNative sign-in | Schema Registry and Connect | Inspecting topic data | On-prem and cloud together | Cost a year, one cluster (modelled) |
|---|---|---|---|---|---|---|---|---|---|
| 1 | Kpow | 85 | Per-person RBAC, approvals, time-boxed access, masking, audit | One container, no external database, not a proxy | Documented; service account token over SASL/PLAIN | Registry documented with two settings; Connect not documented | Data inspect with kJQ and masking | Up to 12 clusters per instance | $7,380 |
| 2 | Lenses | 67 | SSO, RBAC and audit from the Team tier | HQ on PostgreSQL plus an agent per cluster | Guide in StreamNative's documentation | Not covered by StreamNative's guide | SQL over topics | Any Kafka-compatible API | $6,880 for 15 users; custom above |
| 3 | Kafbat UI | 67 | RBAC, global masking, opt-in audit topic | One container | Client properties, no StreamNative guide | No StreamNative guidance | Message browsing | Several clusters | $8,640 |
| 4 | StreamNative Cloud Console | 66 | Predefined roles and SSO; no masking, approvals or expiry | Nothing to deploy | Your StreamNative user account | Both managed in the console | Dashboards; no record search described | StreamNative Cloud only | $0 |
| 5 | AKHQ | 65 | Group roles, no masking per viewer | One container | Client properties, no StreamNative guide | No StreamNative guidance | Topic data browsing | Named connections | $8,640 |
| 6 | Conduktor | 66 | RBAC, SSO, audit; data-level controls need Gateway | Console on PostgreSQL; Gateway, a proxy, for data-level controls | Client properties, no StreamNative guide | No StreamNative guidance | Browse and filter | Several providers | $32,880; $122,880 with Gateway Core and Protect |
The tools, ranked for StreamNative Cloud
Rank 1 Kpow
85 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
- Documented provider; SASL/PLAIN with a service account token, and the Schema Registry with SCHEMA_REGISTRY_OBSERVATION_VERSION=1
- Deployment
- One container or JAR, no external database
- Governance beyond platform RBAC ×3 weight, this criterion counts 3 times toward the total
- 9 out of 10
- Out of the data path ×2 weight, this criterion counts 2 times toward the total
- 9 out of 10
- StreamNative sign-in ×2 weight, this criterion counts 2 times toward the total
- 8 out of 10
- Schema Registry and Connect
- 7 out of 10
- Inspecting topic data
- 9 out of 10
- On-prem and cloud together
- 8 out of 10
Why these scores for Kpow
- Governance beyond platform RBAC 9 out of 10
- RBAC per action and resource, staged approvals, time-boxed temporary policies, masking in data inspect, tenants for shared clusters, sign-in through SAML, OIDC or LDAP, and an audit log that names the person are all documented as Enterprise features, the same score as on the Amazon MSK page.
- Out of the data path 9 out of 10
- It runs as one container or JAR, keeps its state in topics on your own cluster with no dependency beyond Kafka, and connects to StreamNative Cloud like any Kafka client, so it sits beside the cluster rather than in front of it; only the StreamNative Cloud Console, with nothing to deploy, scores higher.
- StreamNative sign-in 8 out of 10
- Kpow’s StreamNative documentation gives the working settings, SASL_SSL with the PLAIN mechanism, the service account’s email address as the username and its token as the password, and StreamNative named Factor House among the launch partners that validated integrations with its Kafka service. That documentation covers the API key over SASL/PLAIN and not the OAuth 2.0 sign-in StreamNative recommends for production workloads.
- Schema Registry and Connect 7 out of 10
- Kpow’s StreamNative documentation covers the Schema Registry with the two settings it needs, basic authentication where only the password is checked and SCHEMA_REGISTRY_OBSERVATION_VERSION=1, and it does not cover Kafka Connect on StreamNative Cloud.
- Inspecting topic data 9 out of 10
- Data inspect decodes records against the schema registry, kJQ filters them across topics, and data policies mask sensitive fields in the results.
- On-prem and cloud together 8 out of 10
- One deployment manages StreamNative Cloud alongside self-managed Apache Kafka, Confluent Platform, Confluent Cloud and MSK, capped at 12 clusters per instance before you run another, the same score as on the banking page.
On StreamNative Cloud. Kpow’s StreamNative provider page connects to StreamNative’s managed brokers on port 9093 with SECURITY_PROTOCOL=SASL_SSL and SASL_MECHANISM=PLAIN, the service account’s email address as the username and its token as the password, with no plugin or sidecar. The same page adds the Schema Registry with SCHEMA_REGISTRY_AUTH=USER_INFO, any username, the token as the password and SCHEMA_REGISTRY_OBSERVATION_VERSION=1. The integration guide starts it in Docker with one command. In its launch partner announcement of 7 April 2026, StreamNative listed Factor House among the partners that validated integrations with StreamNative Kafka Service.
The Schema Registry setting. Kpow’s default schema observation engine downloads every schema in one request to the registry’s bulk /schemas endpoint. StreamNative’s registry does not implement that endpoint and returns 404, which StreamNative’s own compatibility reference confirms. Setting SCHEMA_REGISTRY_OBSERVATION_VERSION=1 makes Kpow list the subjects first and fetch each schema in turn, which the schema registry documentation describes as the mode with more REST calls on a large registry.
Where it falls short. Kpow’s documentation recommends giving its service account StreamNative’s instance-owner role, which is full administrative control of the instance, so the limits on what each engineer can do come from Kpow’s own roles rather than from the token. StreamNative’s migration guide recommends OAuth 2.0 for production workloads and describes API keys as suited to development and testing, and Kpow’s StreamNative documentation covers the API key only. It also does not cover Kafka Connect on StreamNative Cloud, or the difference between native Kafka clusters and Pulsar clusters with the Kafka protocol enabled. Kpow does not manage StreamNative’s own objects, such as service accounts, API keys, role bindings and lakehouse tables, which stay in the StreamNative Cloud Console. Kpow governs people working through Kpow, so applications keep their own service accounts. RBAC, masking, staged mutations and the audit log need Kpow Enterprise; Community Edition is free for 3 clusters and 10 users.
Compare Kpow vs LensesKpow vs Kafbat UI
Rank 2 Lenses
lenses.io
67 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
- A Lenses guide in StreamNative's documentation, and a Kafka Service launch partner
- Deployment
- HQ on PostgreSQL plus an agent and database per cluster
- Governance beyond platform RBAC ×3 weight, this criterion counts 3 times toward the total
- 7 out of 10
- Out of the data path ×2 weight, this criterion counts 2 times toward the total
- 4 out of 10
- StreamNative sign-in ×2 weight, this criterion counts 2 times toward the total
- 8 out of 10
- Schema Registry and Connect
- 5 out of 10
- Inspecting topic data
- 10 out of 10
- On-prem and cloud together
- 7 out of 10
Why these scores for Lenses
- Governance beyond platform RBAC 7 out of 10
- SSO, SAML and RBAC come from the Team tier and audit logs are in the product, one grade below the tools with approvals and time-boxed access, the same score as on the Amazon MSK page.
- Out of the data path 4 out of 10
- It is self-hosted, but a central HQ on PostgreSQL plus an agent and an agent database for every cluster is the heaviest footprint here apart from Conduktor with Gateway.
- StreamNative sign-in 8 out of 10
- StreamNative’s documentation has a guide to connecting Lenses with a service account’s API key over SASL/PLAIN, written for a cluster with the Kafka protocol enabled, and StreamNative named Lenses among the launch partners that validated integrations with its Kafka service.
- Schema Registry and Connect 5 out of 10
- It works with Confluent-compatible registries and Kafka Connect clusters in general, and StreamNative’s Lenses guide covers the broker connection only, so the registry’s missing endpoints are something to verify.
- Inspecting topic data 10 out of 10
- SQL over topics is the centre of the product and the strongest query model on this page, ahead of Kpow’s kJQ.
- On-prem and cloud together 7 out of 10
- It connects to any provider exposing a Kafka-compatible API, one agent per cluster, the same score as on the banking page.
On StreamNative Cloud. StreamNative’s documentation has a guide, Connect to your cluster using Lenses, written for a StreamNative cluster with the Kafka protocol enabled, that enters the cluster’s Kafka service URL as the bootstrap servers and signs in over SASL/PLAIN with the tenant and namespace as the username and token: followed by the service account’s API key as the password, then views and queries a topic in SQL Studio. StreamNative’s launch partner announcement of 7 April 2026 lists Lenses beside Factor House. 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 service chosen so there is nothing to operate. StreamNative’s guide stops at the broker connection and a query, and says nothing about the Schema Registry. No approval step or time-boxed grant is described, and 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 3 Kafbat UI
67 out of 100 Total
- Cost a year
- $0 licence, about $8,640 in operator time (modelled)
- On StreamNative
- Ordinary Kafka client properties; no StreamNative guide
- Deployment
- One container, no database
- Governance beyond platform RBAC ×3 weight, this criterion counts 3 times toward the total
- 6 out of 10
- Out of the data path ×2 weight, this criterion counts 2 times toward the total
- 9 out of 10
- StreamNative sign-in ×2 weight, this criterion counts 2 times toward the total
- 6 out of 10
- Schema Registry and Connect
- 5 out of 10
- Inspecting topic data
- 8 out of 10
- On-prem and cloud together
- 6 out of 10
Why these scores for Kafbat UI
- Governance beyond platform RBAC 6 out of 10
- It offers LDAP and OIDC sign-in, resource-level RBAC, global masking and an opt-in audit topic, which is one grade below the commercial tools, the same score as on the Amazon MSK page.
- Out of the data path 9 out of 10
- It is a self-hosted container with no database, the same pass as Kpow and the other open-source UIs.
- StreamNative sign-in 6 out of 10
- SASL/PLAIN with a token is an ordinary Kafka client property, which StreamNative documents for Kafka tools in general, but neither Kafbat UI nor StreamNative publishes a guide for the pair, so it is possible with work you verify yourself.
- Schema Registry and Connect 5 out of 10
- It works with Confluent-compatible registries and Kafka Connect clusters by URL, and publishes no guidance on the endpoints StreamNative’s registry leaves out.
- Inspecting topic data 8 out of 10
- Message browsing and inspection are core features of the open-source UI.
- On-prem and cloud together 6 out of 10
- It covers self-managed Kafka, MSK and other managed services, but Confluent Cloud connectivity broke in v1.4.x and v1.5.0, the same score as on the banking page.
On StreamNative Cloud. Kafbat UI treats StreamNative Cloud as one more Kafka cluster: a bootstrap address on port 9093, SASL_SSL with the PLAIN mechanism and a service account’s token, and the Schema Registry configured as a Confluent-compatible registry. Its feature list covers topic and message browsing, consumer groups, schemas and Kafka Connect, and the Kafbat UI review covers its RBAC and release history in detail.
Where it falls short. There is no way to grant production access for an hour and have it expire, no approval before a change 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. There is no SLA, and paid help is a professional services engagement from the maintainers, quoted rather than listed. Its modelled running cost on one cluster is 6 engineer-hours a month, $8,640 a year at $120 an hour.
Rank 4 StreamNative Cloud Console
streamnative.io
66 out of 100 Total
- Cost a year
- $0 licence and nothing to run; it comes with the service (modelled)
- Sign-in
- StreamNative user accounts, which can be linked to single sign-on
- Deployment
- Nothing to deploy
- Governance beyond platform RBAC ×3 weight, this criterion counts 3 times toward the total
- 5 out of 10
- Out of the data path ×2 weight, this criterion counts 2 times toward the total
- 10 out of 10
- StreamNative sign-in ×2 weight, this criterion counts 2 times toward the total
- 8 out of 10
- Schema Registry and Connect
- 10 out of 10
- Inspecting topic data
- 3 out of 10
- On-prem and cloud together
- 2 out of 10
Why these scores for StreamNative Cloud Console
- Governance beyond platform RBAC 5 out of 10
- Predefined roles are bound to each user account from the organisation down to a cluster, tenant, consumer group or schema subject, resources a person cannot read are hidden in the console, user accounts can be linked to single sign-on, and audit events can be written to topics, but StreamNative’s documentation describes no masking of record contents, no approval step and no time-boxed grant.
- 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.
- StreamNative sign-in 8 out of 10
- Engineers use their own StreamNative user accounts, so no service account token is shared, although API keys and OAuth2 clients play no part in what the console shows.
- Schema Registry and Connect 10 out of 10
- The Schema Registry is switched on and given its roles in the console, and Kafka Connect connectors are created from the console’s Connectors page, which makes it the only option here that covers both as StreamNative runs them.
- Inspecting topic data 3 out of 10
- The console’s dashboards count topics, messages and subscriptions, and StreamNative offers a Kafka REST API that consumes records, but the documentation read for this page describes no search of Kafka records by key or value.
- On-prem and cloud together 2 out of 10
- It shows StreamNative Cloud only, so clusters elsewhere need another tool.
What it covers. The StreamNative Cloud Console is where an organisation is administered: instances, clusters, tenants, namespaces, topics, service accounts and their API keys, role bindings, secrets, connectors and billing. Its Kafka Clients page is a wizard that picks or creates a service account, issues its token and generates sample client code. StreamNative’s predefined roles run from org-admin down to roles for one tenant, one Kafka consumer group or one set of schema subjects, and a person without read permission on a resource does not see it in the console.
Where it falls short. StreamNative’s roles decide who may manage or read a resource, and its documentation describes no masking of fields for one viewer, no approval in front of a destructive change and no access that expires on its own. Its audit log is documented for Pulsar clusters, is switched on per cluster, records management events by default with produce and consume events off, and writes them to Pulsar topics that have to be consumed and given a retention policy. Clusters that are not on StreamNative Cloud are outside it.
Rank 5 AKHQ
65 out of 100 Total
- Cost a year
- $0 licence, about $8,640 in operator time (modelled)
- On StreamNative
- Ordinary Kafka client properties; no StreamNative guide
- Security default
- Disabled until you enable it
- Governance beyond platform RBAC ×3 weight, this criterion counts 3 times toward the total
- 5 out of 10
- Out of the data path ×2 weight, this criterion counts 2 times toward the total
- 9 out of 10
- StreamNative sign-in ×2 weight, this criterion counts 2 times toward the total
- 6 out of 10
- Schema Registry and Connect
- 5 out of 10
- Inspecting topic data
- 8 out of 10
- On-prem and cloud together
- 7 out of 10
Why these scores for AKHQ
- Governance beyond platform RBAC 5 out of 10
- It has LDAP, OIDC and basic sign-in with group-based roles, and no masking or audit comparable to the commercial tools, the same score as on the Amazon MSK page.
- Out of the data path 9 out of 10
- It is a self-hosted container with no database.
- StreamNative sign-in 6 out of 10
- SASL/PLAIN with a token is an ordinary Kafka client property in a named AKHQ connection, but neither AKHQ nor StreamNative publishes a guide for the pair, so it is possible with work you verify yourself.
- Schema Registry and Connect 5 out of 10
- It works with Confluent-compatible registries and Kafka Connect clusters by URL, and publishes no guidance on the endpoints StreamNative’s registry leaves out.
- Inspecting topic data 8 out of 10
- Topic data browsing is a core AKHQ feature.
- On-prem and cloud together 7 out of 10
- Each cluster is a named connection, with Confluent Cloud and MSK IAM examples in its documentation, the same score as on the banking page.
On StreamNative Cloud. AKHQ connects to StreamNative Cloud as a named Kafka connection with ordinary client properties, and the Schema Registry is set up as a Confluent-compatible registry with basic authentication. Its documentation covers topic browsing, consumer groups, schemas and Kafka Connect. 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 6 Conduktor
conduktor.io
66 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
- Ordinary Kafka client properties; no StreamNative guide
- Deployment
- Console on PostgreSQL 13+; data-level controls through Gateway, a proxy
- Governance beyond platform RBAC ×3 weight, this criterion counts 3 times toward the total
- 9 out of 10
- Out of the data path ×2 weight, this criterion counts 2 times toward the total
- 3 out of 10
- StreamNative sign-in ×2 weight, this criterion counts 2 times toward the total
- 6 out of 10
- Schema Registry and Connect
- 5 out of 10
- Inspecting topic data
- 8 out of 10
- On-prem and cloud together
- 8 out of 10
Why these scores for Conduktor
- Governance beyond platform RBAC 9 out of 10
- RBAC, SSO by OIDC or LDAP, an audit log and masking are strong, but encryption, field-level masking of the data itself and multi-tenancy run through Gateway, the same score as on the Amazon MSK page.
- Out of the data path 3 out of 10
- Console needs PostgreSQL 13 or later, and the data-level controls counted in its governance score run in Gateway, a proxy that clients connect through, so using them puts Conduktor in the data path.
- StreamNative sign-in 6 out of 10
- Conduktor states that Console works with any Kafka 2.5 and later, and SASL/PLAIN with a token is an ordinary client property, but its documentation has no StreamNative page and StreamNative publishes no guide for it, so it is possible with work you verify yourself.
- Schema Registry and Connect 5 out of 10
- Console manages Confluent-compatible registries and Kafka Connect clusters in general, and publishes no guidance on the endpoints StreamNative’s registry leaves out.
- Inspecting topic data 8 out of 10
- Console browses and filters topic data, the same pass as on the Amazon MSK page.
- On-prem and cloud together 8 out of 10
- Its cluster configuration covers Confluent Cloud, Aiven, Amazon MSK and Cloudera, and Console works across clusters, the same score as on the banking page.
On StreamNative Cloud. Conduktor states that Console works with Confluent, MSK, Redpanda or any Kafka 2.5 and later, so StreamNative Cloud is added as a cluster with its bootstrap address and SASL/PLAIN properties. Its documentation has no StreamNative page. The Conduktor review covers Console in detail. Conduktor’s Gateway documentation describes Gateway as a Kafka proxy between client applications and brokers, which is where its encryption, masking of the data itself and virtual clusters for multi-tenancy are enforced.
Where it falls short. Console connects to StreamNative Cloud 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, which puts Gateway in the data path for those clients. 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. Conduktor prices Gateway per cluster with a 3-cluster minimum, and the listing does not say how many clusters that figure covers.
What teams on StreamNative Cloud need
This page is about tools that sit beside a Kafka cluster on StreamNative Cloud: a UI for reading topics, managing consumer groups and schemas, and governing who does what. It is not about replacing the service or its console. The other managed and Kafka-compatible platforms have their own pages, for Amazon MSK, Confluent Cloud, Aiven for Apache Kafka, NetApp Instaclustr, Google Cloud Managed Service for Apache Kafka and Redpanda, and the broader field of Kafka tools on any distribution is in the best Kafka management tools.
StreamNative Cloud runs Kafka in two ways, and a tool has to work with whichever one the team is on. StreamNative Kafka Service runs native Apache Kafka on the Ursa Engine with no compatibility layer, and StreamNative’s comparison of the two lists native Kafka clusters as in Public Preview. Pulsar clusters serve Kafka clients through KSN, a protocol handler that translates the Kafka protocol onto Pulsar storage, and that option is generally available. The same comparison notes that Kafka clusters and Pulsar clusters cannot currently sit in the same StreamNative instance, and that Universal Linking replicates data between them for a team moving from KSN to native Kafka, so that move is a migration between two instances rather than a setting on one cluster.
Three things come up once a team is running Kafka on StreamNative Cloud in production. The tool has to sign in the way the service authenticates clients, it has to read StreamNative’s Schema Registry, which implements part of the Confluent API and behaves differently at the edges, and it has to answer the governance questions StreamNative’s roles leave open once several engineers share one tool. Where the tool runs cuts across all three.
Governance beyond StreamNative RBAC. StreamNative’s role-based access control binds predefined roles to principals, which are user accounts and service accounts, from the organisation down to a tenant, a consumer group or a set of schema subjects, and a principal without read permission on a resource does not see it in the console. Only a principal with the org-admin or account-admin role can create service accounts and bind roles to them. A shared tool connects with one service account, so StreamNative sees that service account whichever engineer is working, and StreamNative’s own Schema Registry security page says the same about registry calls: only the password is evaluated, the identity comes entirely from the API key, and the username should not be relied on to identify a caller in audit trails. The record of which person read a topic or registered a schema through a shared tool therefore has to come from the tool, and the same applies to the controls StreamNative’s roles do not provide: access to a production topic for an hour that then expires, an offset reset held for a second person’s approval, or a field masked in the records one person reads. Permission to read a topic and permission to see a field in the clear are separate decisions in Kpow’s data policies, which is the distinction a role on the topic alone cannot make.
StreamNative’s own audit log is documented for Pulsar clusters and is switched on per cluster. Management events, such as creating a topic or granting a permission, are recorded by default, while producer and consumer events are off by default, and the events are written to Pulsar topics whose retention the team has to set so that the log is not cleaned up. On Pulsar clusters that serve Kafka through KSN, StreamNative maps Kafka ACLs onto Pulsar ACLs that carry only produce and consume, and creating, deleting or altering a topic needs a super user, so a tool that manages topics there holds broad rights and the limit on each engineer has to be set in the tool. Native Kafka clusters use topic-level ACLs. Per-person roles and audit are compared across tools in Kafka RBAC tools and Kafka audit logging tools.
StreamNative sign-in. StreamNative Cloud authenticates Kafka clients over TLS on port 9093 with SASL/PLAIN, where the username is the service account’s email address and the password is its API key, a token in JSON Web Token format (authentication overview), rather than with the OAUTHBEARER mechanism other token-based services use. Kpow’s StreamNative provider page gives those settings for the native Kafka service. StreamNative’s Lenses guide was written for a cluster with the Kafka protocol enabled, where the username is the tenant and namespace and the password is token: followed by the API key, so the two documented setups describe different cluster types. Kafbat UI, AKHQ and Conduktor take the same client properties with no published guide for StreamNative. StreamNative’s migration guide recommends OAuth 2.0 for production workloads and describes API keys as suited to development and testing, and none of the third-party tools here documents OAuth 2.0 against StreamNative Cloud.
The network path matters as well. StreamNative’s Kafka client compatibility page explains that it routes each Kafka connection by the TLS Server Name Indication extension, first to the bootstrap endpoint and then to each broker’s own hostname, and that any forward proxy in the path has to pass that extension through unchanged. A tool installed in a corporate network that sends outbound traffic through such a proxy fails at the TLS handshake until the proxy is set up that way, which is worth checking before a trial rather than during one.
Schema Registry and Connect. StreamNative’s Schema Registry is compatible with the Confluent Schema Registry API but implements a subset of it. Its compatibility reference lists the endpoints that return 404, the bulk listing of every schema among them, so a tool that loads a registry in one bulk call fails against it. Kpow is the one tool on this page with documented settings for both differences that matter: SCHEMA_REGISTRY_AUTH=USER_INFO with the API key as the password, and SCHEMA_REGISTRY_OBSERVATION_VERSION=1, which lists subjects first and fetches each schema in turn. The same reference records behaviour a team moving from Confluent’s registry should know about. A schema registered with Confluent data contract fields (metadata or ruleSet) is accepted and stored without them, with no error, so rules that were enforced before stop being enforced without any signal. The global GET /config always reports a compatibility level of NONE while the real default is BACKWARD, so a tool that shows the registry-wide level shows the wrong one and the level has to be read per subject. There is no undelete, so a soft-deleted schema can be read but not restored, and re-registering it creates a new version. StreamNative’s security page adds that GET /subjects returns only the subjects the credential can read, so a service account scoped to a few subjects sees what looks like a small or empty registry with no permission error. Schema tooling on any distribution is compared in Kafka schema registry tools.
On Pulsar clusters, StreamNative runs Kafka Connect as a managed service, with connectors created from the Connectors page of the StreamNative Cloud Console, and none of the third-party tools on this page documents managing them there, Kpow included.
Out of the data path. Teams choose StreamNative Cloud so that there are no brokers to provision or rebalance, and with BYOC so that the data stays in their own cloud account. A tool that runs as one container in that account and connects the way any Kafka client does adds nothing to the path producers and consumers take. Kpow is one container with no external database, installed in your own environment and out of the data path. Conduktor Console also connects to StreamNative Cloud 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.
Keeping no database of its own means Kpow keeps its state in the cluster it manages, and that is where StreamNative’s cluster profiles reach a tool. Kpow computes its metrics with Kafka Streams, and Kafka Streams keeps the changelog topics of its key-value stores with a compact cleanup policy. StreamNative’s cluster profiles page lists Kafka transactions and topic compaction as supported on the Latency-Optimized profile and coming soon on the Cost-Optimized profile, which writes straight to object storage with latencies typically above 200 milliseconds. A Cost-Optimized cluster is therefore the one to trial any stateful tool against before relying on it. The Cost-Optimized design is the one Apache Kafka’s own diskless topics proposal, KIP-1150, describes for protocol-compatible engines that use object storage in place of local disks, and on it local disk and replica figures say little, because there are no local disks and no replication between brokers. The diskless topics explainer covers what that changes for monitoring.
What KSN leaves out. A feature existing in Apache Kafka does not mean every Kafka-compatible service exposes it. On Pulsar clusters that serve Kafka through KSN, StreamNative’s protocol reference lists the requests that are not supported: log directory descriptions, partition reassignment and leader election, the ACL requests, client quotas, and the consumer group requests of the new rebalance protocol from KIP-848. A tool’s broker disk, reassignment, ACL and quota screens have nothing to show there, and the only topic configuration it can set is the cleanup policy. Those are the screens to check on a trial against a KSN cluster.
On-prem and cloud together. Many teams arrive on StreamNative Cloud from Amazon MSK, Confluent or self-managed Kafka, and StreamNative’s migration guide runs both clusters in parallel with Universal Linking, has the team inventory every topic, consumer group and connector before it starts, and keeps the source cluster for 24 to 72 hours after cutover as a rollback path. The consumer that nobody listed is the one that fails after a move, which is why that inventory comes first (cluster consolidation talk). A tool that already shows the cluster being retired and the StreamNative cluster replacing it, with consumer group lag on each, covers both sides of that window, and StreamNative’s own console shows StreamNative Cloud only.
Inspecting topic data. The StreamNative Cloud Console counts topics, messages and subscriptions, and the documentation read for this page describes no search of Kafka records by key or value in it. Reading a message, finding one key or checking that a record arrived after an incident is the day-to-day job engineers need a tool for. Kpow runs each search as a parallel plan over a pool of consumers, sampling across partitions, which is how it searches a topic without an index or a database of its own, and it decodes records against StreamNative’s registry once the two registry settings above are in place.
No tool here leads on every criterion. Lenses has the stronger query model with SQL over topics. The StreamNative Cloud Console needs nothing deployed and manages StreamNative’s own objects, service accounts, role bindings and connectors, which Kpow does not. Kpow’s StreamNative documentation covers the API key over SASL/PLAIN and the Schema Registry, and does not cover OAuth 2.0, Kafka Connect on StreamNative Cloud, or which cluster types and profiles it was tested on, and its registry support needs the version 1 observation setting, which makes more REST calls on a large registry. Kpow governs people working through Kpow, so applications keep their own service accounts, and StreamNative’s roles stay the control for them.
Who runs Kpow on StreamNative Cloud
No Factor House customer has yet described in public how it runs Kpow on StreamNative Cloud, so the public record is what the two companies have published. StreamNative named Factor House among the launch partners of StreamNative Kafka Service in its launch partner announcement of 7 April 2026, in a group it describes as having validated integrations with the service, and its partner directory has a Factor House page. On the Factor House side, the StreamNative provider page in the Kpow documentation and the integration guide on this site give the working configuration.
The integration needed no plugin because the Kafka protocol, not any one implementation of it, is the standard that a new engine is judged against: StreamNative describes its Kafka service as native Apache Kafka that standard clients and tools use without modification, and a tool that speaks only the Apache Kafka protocol works with it without vendor-specific code. Factor House’s tools stay close to open source Apache Kafka and do not modify or extend it, which is why they connect to each Kafka provider in the same way, an approach described on the Behind the Stream podcast. For customer accounts of Kpow on other managed services, the Amazon MSK page carries Belong’s description of why it runs Kpow beside MSK, and the Confluent Cloud page covers TD Bank and Allianz Direct.
How a team runs StreamNative Cloud with Kpow
Creating the service account. An org-admin or account-admin in the StreamNative Cloud Console creates a service account for Kpow and generates its API key. Kpow’s StreamNative documentation recommends granting that account the instance-owner role, because Kpow manages and monitors the whole cluster, and then restricting what each engineer can do with Kpow’s own authorisation. A narrower role on the registry has a visible cost, since StreamNative’s registry returns only the subjects the key can read, and Kpow would then show a partial registry with no error.
Installing it beside the cluster. Kpow runs as one Docker container or Java JAR, or on Kubernetes with the Helm charts, in a network that reaches the cluster’s Kafka endpoint, which for a BYOC cluster can be the team’s own cloud account. On Kubernetes its configuration is environment variables, so the API key belongs in a Kubernetes Secret rather than in the chart values. It needs no external database, because its snapshots, metrics and audit log live in topics on the cluster itself. The integration guide starts it in Docker with one command.
Connecting to the brokers. Kpow connects with BOOTSTRAP set to the cluster’s Kafka address on port 9093, SECURITY_PROTOCOL=SASL_SSL and SASL_MECHANISM=PLAIN, and a JAAS configuration whose username is the service account’s email address and whose password is its API key. These are the same cluster settings as any Kafka producer or consumer uses, with no plugin or sidecar.
Connecting the Schema Registry. The registry is added with SCHEMA_REGISTRY_URL, SCHEMA_REGISTRY_AUTH=USER_INFO, any username and the API key as the password, because StreamNative’s registry checks only the password. SCHEMA_REGISTRY_OBSERVATION_VERSION=1 is required: Kpow’s default observation engine calls the bulk /schemas endpoint, which StreamNative’s registry answers with 404. With both set, 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 over SAML, Microsoft Entra ID, OpenID Connect or LDAP, so access follows the company directory rather than one shared API key.
Giving teams their own view of a shared cluster. Tenants limit which topics, groups and schemas each role can see, and RBAC sets Allow, Deny or Stage per action and resource. This is the layer that narrows the instance-owner service account down to what each engineer is allowed to do. Kpow’s policies apply at each depth of the resource tree, so a policy on a cluster does not by itself grant topic-level actions such as inspecting data or producing, which need a policy at the topic level.
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.
Watching consumer lag. Kpow shows each consumer group down to partition level and resets, clears or skips offsets from the same view, and exports group lag with other computed metrics to Prometheus. During a migration with Universal Linking, the source cluster and the StreamNative cluster can sit in the same Kpow instance, so lag on both sides of the cutover is in one view.
Keeping the record. The audit log records each action, data inspect queries included, with the user from the identity provider, which is the per-person record StreamNative’s registry and roles cannot give for work done through a shared service account. Kpow shows the last seven days in the product and writes the record to the __oprtr_audit_log topic on the team’s own cluster, where Kpow’s topics default to one week of retention, so for a longer record a webhook sends each event to Slack, Microsoft Teams or any HTTP endpoint, such as a SIEM collector.
The public Kpow demo runs on two Amazon MSK clusters rather than StreamNative Cloud and needs no signup. It has no data policies configured, so it cannot show masking; StreamNative sign-in, masking and temporary policies are the things to test against your own cluster. One Kpow instance manages up to 12 clusters, so a StreamNative cluster sits in the same view as Apache Kafka, Confluent or Amazon MSK clusters run by other teams, under the same roles and audit log; the multi-cluster page lists the providers.
Kpow live demo
See the Kpow UI before you connect StreamNative Cloud
The live Kpow demo runs on two Apache Kafka clusters on Amazon MSK, not on StreamNative Cloud. It shows the views a team gets on StreamNative too: brokers, topics, consumer groups, schema registries and the __oprtr_audit_log topic where Kpow keeps its audit trail, with no signup. Connecting your own StreamNative cluster is the step to try next.
For platform teams choosing a Kafka tool for StreamNative Cloud.
Try the Kpow demoFAQ
What is the best Kafka UI for StreamNative Cloud?
On this page’s rubric, Kpow, with 85 of 100 points: it has a documented StreamNative setup with a service account token over SASL/PLAIN, reads the Schema Registry with the two settings the service needs, adds per-person roles, time-boxed production access, approvals, masking and an audit trail on top of StreamNative’s roles, and runs as one container in your own environment outside the data path. Lenses and Kafbat UI score next, level at 67, and Kafbat UI is the highest-scoring free open-source option.
Does StreamNative Cloud have its own Kafka UI?
The StreamNative Cloud Console administers instances, clusters, topics, service accounts, API keys, role bindings and connectors, and its Kafka Clients page generates client configuration. Its dashboards count topics, messages and subscriptions. The StreamNative documentation read for this page describes no search of Kafka records by key or value in the console, and it covers StreamNative Cloud only.
How does Kpow connect to StreamNative Cloud?
As an ordinary Kafka client. Kpow’s StreamNative provider page sets BOOTSTRAP to the cluster’s address on port 9093, SECURITY_PROTOCOL=SASL_SSL and SASL_MECHANISM=PLAIN, with the service account’s email address as the username and its API key as the password. No plugin, sidecar or proxy is involved.
Does Kpow work with the StreamNative Schema Registry?
Yes, with two settings. The registry uses basic authentication and checks only the password, so Kpow is given any username and the API key as the password, and SCHEMA_REGISTRY_OBSERVATION_VERSION=1 is required because StreamNative’s registry does not implement the bulk /schemas endpoint that Kpow’s default observation engine calls (Kpow schema registry docs).
Does Kpow work with StreamNative’s native Kafka service and with KSN?
Kpow connects through the standard Kafka protocol, and StreamNative named Factor House a launch partner of StreamNative Kafka Service, the native Apache Kafka option, in April 2026. Kpow’s documentation describes StreamNative’s managed Kafka brokers and does not separate native Kafka clusters from Pulsar clusters that serve Kafka clients through KSN. On KSN clusters StreamNative’s protocol reference lists the ACL, quota, log directory and partition reassignment requests as unsupported and limits topic configuration to the cleanup policy, so those are the screens to check on a trial.
Which role does the Kpow service account need on StreamNative Cloud?
Kpow’s documentation recommends the instance-owner role, because Kpow manages and monitors the whole cluster. What each engineer can then see and do is set inside Kpow, with RBAC or, on a small team, simple access control.
Does a Kafka UI need to sit in the data path to govern access on StreamNative Cloud?
No. Kpow runs as one container beside the cluster, connects to StreamNative Cloud 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 StreamNative Cloud and other Kafka clusters together?
Kpow manages up to 12 clusters per instance across StreamNative Cloud, 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 migrating to StreamNative Kafka Service from MSK, Confluent or self-managed Kafka can keep the cluster being retired and the one replacing it in one Kpow view, with consumer group lag on each. The StreamNative Cloud Console shows StreamNative Cloud only. The general comparison is the best tools to manage multiple Kafka clusters.
Which Kafka UI keeps an audit trail of who did what on StreamNative Cloud?
Kpow records every action and every data inspect query with the user from the identity provider. StreamNative’s own audit log, as documented for Pulsar clusters, writes management events to Pulsar topics, with producer and consumer events off by default, and a shared tool connects with one service account, so that account is what the service sees. StreamNative’s Schema Registry security page says the same of registry calls: the identity comes entirely from the API key. Conduktor and Lenses keep in-product audit logs, and Kafbat UI and AKHQ write opt-in audit topics. Audit options are compared in the best tools for Kafka audit logging.
How do I mask PII in Kafka topics on StreamNative Cloud?
Kpow’s data policies mask fields such as card numbers or email addresses in data inspect results on the server, for every engineer working through Kpow, with no change to producers or consumers. The StreamNative documentation read for this page describes no masking of record contents in the console. 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. Masking options are compared in the best tools for Kafka data masking.
Is there a free Kafka UI for StreamNative Cloud?
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. The paid line is drawn at governance rather than at core Kafka functions: RBAC, masking, staged approvals and the audit log need Kpow Enterprise, as the pricing page sets out. More free options are compared in the best free Kafka UI tools.
Does StreamNative’s Schema Registry enforce Confluent data contract rules?
No. StreamNative’s compatibility reference says a schema registered with metadata or ruleSet fields is accepted and stored without them, with no error, so rules a team relied on in Confluent’s registry stop being enforced without any signal. The same reference lists client-side field level encryption and schema contexts as unsupported, says the global GET /config always reports NONE while the real default compatibility level is BACKWARD, and notes that a soft-deleted schema cannot be restored.
Which sign-in does StreamNative recommend for tools and clients in production?
StreamNative’s migration guide recommends OAuth 2.0 for production workloads and describes API keys as suited to development and testing. Kpow’s StreamNative documentation, and StreamNative’s own Lenses guide, use an API key over SASL/PLAIN, and none of the third-party tools on this page documents OAuth 2.0 against StreamNative Cloud, so a team with an OAuth-only policy should test it on a trial.
How these tools were scored
Three of the six criteria, governance, the data path and inspecting topic data, are the same ones, scored the same way, as on Factor House’s best Kafka UI tools for Amazon MSK, and on-prem and cloud together is scored as on the banking page, so a tool scores the same on each page wherever the evidence is the same. The other two start from StreamNative’s documentation. 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 six options are the StreamNative Cloud Console, the two Kafka tools StreamNative named as launch partners of its Kafka service, Kpow and Lenses, and three widely used tools that connect with ordinary Kafka client properties.
1. Governance beyond platform RBAC (counts three times). The platform here is StreamNative Cloud, whose roles decide what a user account or a service account may do. When engineers share one tool, the tool’s service account is what StreamNative sees, and StreamNative’s documentation describes no masking, no approval step before a topic is deleted and no access that expires. This criterion scores per-person roles, production access granted on request and expiring, masking, directory sign-in, tenants for teams sharing a cluster, and an audit trail that names the person. It is scored the same way as governance beyond IAM on the Amazon MSK page. The requirements for regulated teams are covered in Best Kafka governance tools for financial services.
2. Out of the data path (counts twice). The tool should run inside your own environment, reach the brokers over the same network your applications use 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, here 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.
3. StreamNative sign-in (counts twice). StreamNative Cloud authenticates Kafka clients over TLS with SASL/PLAIN and a service account’s token, or with OAuth2. A tool scores 8 when its own documentation or StreamNative’s shows it connecting that way, and 6 when the connection rests on ordinary Kafka client properties with no published guide for the pair. The console scores 8 because engineers use their own StreamNative accounts.
4. Schema Registry and Connect (counts once). StreamNative’s Schema Registry implements a subset of the Confluent API, and Kafka Connect connectors are created and run by StreamNative. A tool scores well when it documents the registry’s behaviour on StreamNative, and best when it also covers connectors as StreamNative runs them, which only the console does. Tools with no StreamNative-specific guidance score 5, because the registry’s missing endpoints have to be tested.
5. Inspecting topic data (counts once). Reading a message, searching a topic for one key or replaying a record after an incident is the day-to-day job engineers need a tool for.
6. On-prem and cloud together (counts once). Many teams on StreamNative Cloud also run clusters elsewhere, or are migrating from them. This criterion is scored the same way as on the banking page: whether one deployment manages StreamNative Cloud alongside self-managed Kafka and other managed services.
Costs are modelled for one production cluster on StreamNative Cloud 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. Kafbat UI and AKHQ carry 6 hours a month, $8,640 a year, to run, secure and keep current. The StreamNative Cloud Console has no licence and nothing to run, and comes with the service you already pay for. 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. Conduktor’s $32,880 is 25 Console seats plus operator time; Gateway Core adds $60,000 and Gateway Protect, where data-level masking matters, a further $30,000, which is $122,880. Conduktor prices Gateway per cluster with a 3-cluster minimum, and the listing does not say how many clusters that figure covers. 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. Cost is shown for comparison and is not a scored criterion.
The criteria map onto StreamNative Cloud’s features 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: Governance beyond platform RBAC counts three times, Out of the data path counts twice, StreamNative sign-in counts twice, Schema Registry and Connect counts once, Inspecting topic data counts once and On-prem and cloud together counts once, for a total out of 100. Governance beyond the platform's own RBAC counts three times because StreamNative's roles authorise the service account a shared tool connects with, so per-person roles, production access granted on request, masking, directory sign-in, tenants for shared clusters and a per-person audit trail are the only record of which engineer did what through that tool. Out of the data path and StreamNative sign-in count twice: a tool that clients connect through becomes part of the path every producer and consumer depends on, a tool that needs a database of its own is one more component to run beside a service chosen so there would be nothing to operate, and a tool that cannot sign in with a service account's token cannot be used at all. The Schema Registry and Kafka Connect, inspecting topic data, and StreamNative alongside other clusters count once. This page is published by Factor House, which makes Kpow. Every option is scored on the same rubric and the same sources: Kpow's per-criterion scores are set the same way as every other option's and are not adjusted, and the weights apply to every option alike. Kpow ranks first on its total of 85 out of 100. The other options follow by total. Conduktor is listed last whatever its total; on its total of 66 it would place fourth.
Related reading
- Kafka: the complete guide
- Kafka-compatible cloud brokers
- Integrate Kpow with StreamNative Cloud
- Best Kafka UI tools for Amazon MSK
- Best Kafka UI tools for Confluent Cloud
- Best Kafka tools for Aiven for Apache Kafka
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