AKHQ vs Lenses

AKHQ vs Lenses, compared

F1 Kpow, AKHQ and Lenses, side by side

Kpow AKHQ Lenses

Adding an engineerDoes the bill stay flat when somebody joins?Yes. No change up to the 100 users included with each cluster, because the licence counts clusters and not seats. Yes. No change to the bill. No. A step toward the 15-user Team ceiling, then custom pricing.

Adding a Kafka clusterCan one deployment manage more than one cluster?Yes. Up to 12 per instance, each with its own connection, Kafka Connect, schema registry and ksqlDB. Community Edition covers 3, and Enterprise sets no licensed cap. Yes. One deployment already reaches it. No. Another Agent and another Agent database. Federated multi-Kafka sits in the custom-priced top tier.

External dependenciesDoes it run without an external datastore?Yes. None. A single stateless container configured through environment variables, with no external database, no proxy layer and no persistent volume. Yes. None. No database and no sidecar. No. PostgreSQL for HQ as the only supported storage option, plus a database per Agent.

Data maskingIs data masking part of the product?Yes. Server side data masking policies for PII, applied at the console rather than in the data path, alongside role based access control. Enterprise. Yes. Four modes, configured as global YAML with one filter per topic. It does not vary by who is looking. Yes. Global by field name across every dataset, applying to every user including an admin.

Audit trailIs every user action recorded?Yes. Every user action on every cluster, recording who asked, what the request held and whether RBAC allowed it, readable in the UI or piped out as a webhook or a Kafka topic. Enterprise. No. Opt-in, written to a Kafka topic the operator nominates, with no audit view in the product. Yes. In-product audit logs carrying principal delegation, with listing granted to the Security Admin role.

SupportIs there a support channel under contract?Yes. Email support and an Enterprise support SLA, with priority support on Enterprise, and a community Slack channel and GitHub issues on both editions. No. GitHub issues. No SLA. Yes. A vendor under contract, with Team Support from the paid tier.

Pricing unitA unit of sale, not a pass or a fail.Not a yes or no. Per cluster. Enterprise starts at 4,500 US dollars per cluster per year with 100 users included, and Community Edition is free. Not a yes or no. Free, Apache 2.0, with no paid tier and no commercial support. Not a yes or no. Tiered by capability and user count. Team starts at 4,000 US dollars a year for up to 15 users.

Free tierDoes the free tier reach a fifty-person team?No. Community Edition, free with no time limit, covers 3 clusters and 10 users. RBAC, data masking, SSO and the audit log start on Enterprise. Yes. The whole product. No feature is held back from the open release. No. Community is 5 users with Basic Auth only, so no SSO and no RBAC.

Kpow meets 6 of 7 requirements on this page. One row is not a yes or no question.

Key takeaway

AKHQ is free under Apache 2.0 with no paid tier, while Lenses Team starts at 4,000 US dollars a year for up to 15 users, so the first question is whether you are buying software or buying somebody to call. Lenses HQ requires PostgreSQL and each Agent needs a database of its own, reaching one cluster each, where AKHQ holds no state and reaches many clusters from one deployment. Both mask data, and neither varies it by who is looking. Kpow by Factor House is licensed per cluster at a published price.

Kpow live demo

Test the trade-offs in a live Kafka UI

You have compared AKHQ vs Lenses. Open a live Kpow environment to test the everyday workflows a shared Kafka platform needs.

Built for platform and data teams managing shared Kafka clusters.

Try the Kpow demo→

What is AKHQ?

AKHQ is an open-source Kafka management UI under Apache 2.0, formerly KafkaHQ, self-hosted and built on Micronaut. One deployment reaches one cluster or many, covering topic browsing, live tailing, producing, consumer groups, Schema Registry, Kafka Connect, ACL management and role-based access with LDAP and OIDC.

What is Lenses?

Lenses is a commercial Kafka governance and data exploration platform that sits on top of clusters you already run. It operates as a Kafka client rather than a proxy, so nothing of it stands in the data path and no client configuration changes because it is there. The architecture is a central Lenses HQ node with a lightweight agent per cluster, and because that agent connects as an ordinary client, a KRaft cluster needs no broker-side modification to be reachable. Celonis acquired Lenses in early 2022, and 6.2.6 was released in August 2026. A data catalog groups topics by domain.

What is the official 2026 pricing of AKHQ and Lenses?

AKHQ costs nothing to license and its whole cost is operator time, so five people and fifty pay the same number. Lenses prices by capability and by user count. Community is free for up to 5 users with Basic Auth only, so no SSO and no RBAC. Team starts at 4,000 US dollars a year and reaches 15 users, adding SSO, SAML, RBAC and team support. Past the Team ceiling the only tier on the table is Multi-Kafka Enterprise, which is custom priced.

The dependency bill is the half no tier card shows. AKHQ holds no state and needs no database. Lenses HQ requires PostgreSQL, which is its only supported storage option, and each Agent needs a database of its own, with an embedded H2 alternative documented for evaluation only. One Agent connects to one Kafka cluster at a time, so a four-cluster estate is HQ, HQ’s Postgres, four agents and four agent databases. The product line also splits into DevX, the UI and governance surface, and K2K, which is replication. Exactly-once semantics, offset replication, autoscaling and schema migration sit on K2K Enterprise, from 1,000 US dollars per month with 5 clusters included and further clusters at 200 US dollars per month.

Where does each one run out?

Each tool here is marked out of 10 on five criteria, 50 points in all, and no criterion is weighted above another. Nothing sits behind a multiplier, so a total is the sum of its five marks and a reader can recompute it. The five are cost as teams grow, deployment footprint, support and maintenance, access control and audit, and multi-cluster reach, because those are the questions a Kafka interface is actually measured against after the first month: a second cluster, an access review with a date on it, an upgrade nobody owns, and a bill that moves when the team does. The widest gap between the two marks is on cost as teams grow, where AKHQ marks 10 and Lenses marks 4. The marks come from the same matrix used on every comparison on this site, so a tool scores the same here as it does anywhere else, and the reason behind each mark is in the card below, under Why these scores.

The dependency figures in the cards below were read on 24 September 2026 from each project’s published release artefact and matched against the NVD and GitHub advisory databases, so they move whenever a release or an advisory lands. Running it yourself is common to both. What differs is whether somebody is contracted to produce the fix.

AKHQ’s governance is present and shaped for a small team. Masking takes four modes, and the policy lives in application YAML keyed on topic and field path, so what is hidden is the same for everybody looking, with only one filter per topic supported.

Lenses runs out first at its own control plane. The current HQ deployment guide describes a single instance, with no replica count and no clustering guidance, so the node watching every cluster is the one thing without a second copy. Against Amazon MSK the default broker metrics refresh of roughly 5 seconds generates a high volume of JMX requests, and 30 seconds or higher is recommended.

Which should you pick?

AKHQ is the pick for a team that wants no state to run, because Lenses puts PostgreSQL behind HQ and another database behind every per-cluster agent, and its Team tier starts at 4,000 US dollars a year for fifteen users. Lenses earns that price on SQL access to topic data. Neither varies masking by who is looking, and Kpow by Factor House does, at a published per-cluster price.

Pick AKHQ if:

• the people using it are engineers who already hold cluster credentials
• the estate is more than one cluster and one deployment should reach all of them
• the cluster list and access model belong in source control as Helm YAML
• there should be no database to back up or upgrade

Pick Lenses if:

• the people who need the data are not Kafka engineers
• SQL over topics is the capability being bought, rather than a tidier browser
• stream processing jobs defined in SQL are part of the plan
• a vendor under contract is required

Pick neither on the compliance story alone. AKHQ’s masking is global, so it cannot vary by who is looking. Lenses masks by field name across every dataset it knows about, and that policy is global too, applying to every user including an admin. What Lenses adds is built-in roles separating Admin, Operator and Security Admin. Underneath both, the access model the brokers enforce belongs to Kafka’s own authorizer, which both tools surface rather than own. Two pages go further on that question: the best free Kafka UI tools compares what each free tier holds back, and Kafka data masking tools covers the policy models in depth.

Kpow: adding a cluster doesn’t add a database

Adding a cluster costs something on both sides here. Lenses adds another Agent and another Agent database for it, and AKHQ takes that same cost out of your own engineers’ time instead, since one deployment already reaches it but somebody still has to watch it. Kpow by Factor House is licensed per cluster: a single stateless JVM container, configured through environment variables, with no external database, no sidecar and no persistent volume. One instance manages up to 12 clusters, so the fourth or the fortieth cluster is a config line, not another Agent and another database.

See what one container adds up to against an agent-per-cluster bill. Point Kpow at your own cluster and find out.

How these tools were scored

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. Each criterion counts once, for a total out of 50. The options are listed by total.

Sources

• Apache Kafka documentation on consumer groups
• Apache Kafka documentation on KRaft
• Apache Kafka documentation on authorization

Related reading

• Integrate Confluent-compatible registries in Kpow
• Kafka consumer monitoring and performance tuning
• Integrate Kpow with Redpanda Streaming Platform
• Accelerating incident response with AI queries