Best Kafka management tools for telecommunications companies

Tools compared

Rank	Tool	Total (out of 100)	Out of the data path	Who can see unmasked data	Inspecting topic data	Audit trail per person	Directory and Kafka sign-in	Many teams, shared clusters	Cost a year (modelled)
1	Kpow	84	One container, state in your Kafka, not a proxy	Server-side, String SerDes removed, no per-role exemption	kJQ across several topics, streaming search	Every action with the IdP user, data queries included	SAML, OIDC, LDAP; SCRAM, IAM, Kerberos or mTLS to brokers	Tenants and per-action RBAC	$20,880
2	Kafbat UI	70	One stateless container	Server-side, the same for every viewer	Message browsing, a core feature	Optional, reads at level ALL, no view	OAuth2, OIDC, LDAP; no SAML	Per-resource roles per cluster	$11,520
3	AKHQ	66	One stateless container	Global filters, the same for every viewer	Topic data browsing, a core feature	Opt-in, no reads, no view	LDAP, OIDC; no SAML	Groups by resource and cluster pattern	$16,320
4	Lenses	64	HQ on PostgreSQL, agent and database per cluster	Strictest, no exemption even for admins	SQL over topics, the strongest here	In-product audit log from Team tier	SSO incl. Entra ID and Okta	Roles on groups only	$2,880 plus quoted licence
5	Confluent Control Center	41	Dedicated host, broker reporter JAR	No masking described	Topics > Messages view, nested Avro key bug	Broker principal, not always the person	OIDC on self-managed	Admin access only, per TD	$2,880 plus quoted subscription
6	Conduktor	61	Console on PostgreSQL; Gateway proxy in the data path	Console exemptions per user or group	Browse and filter topic data	70+ event types with user, in the UI	LDAP, OIDC	Per user or group, most permissive grant wins	$122,880; $212,880 with Gateway Core and Protect

No tool meets every column, and telecommunications companies commonly pair a management tool for people with broker authentication and ACLs for services, and keep the most sensitive values, such as one-time passcodes, off general-purpose topics altogether.

The tools, ranked for telecommunications companies

What telecommunications companies need from a Kafka management tool

This page is about telecommunications companies specifically: mobile and broadband providers, network operators and the digital brands that large carriers run, whose Kafka topics carry subscriber records, billing events and customer notifications. Telcos share a good deal with the industries on Best Kafka management tools for energy and utility companies, because both run critical infrastructure, and the managed-service angle is covered in depth on Best Kafka UI tools for Amazon MSK. The rubric here is built from what a telco’s subscriber data and operating model ask of a tool that people use to look inside Kafka.

Telecommunications is a long-standing user of event streaming. Kai Waehner’s survey of the state of data streaming for telco collects public customer stories from Dish Network, British Telecom, Globe Telecom and Swisscom, including Dish’s cloud-native 5G network, which uses Kafka as the central communications hub between its infrastructure interfaces and IT applications. Closer to the customer, Belong, part of Telstra, described in its talk at the Kafka User Group Asia Pacific how bill reminders and marketing messages go out as SMS through a notification service built on Kafka topics, with retry topics and rate limiting for bursty traffic, while one-time passcodes stay off Kafka.

What sets telcos apart is the subscriber record. A telco holds the identity details of a large share of a country’s population, and the 2022 Optus data breach, which affected up to 10 million current and former customers of the Australian telco, a third of the country’s population, showed what those records contain: names, dates of birth and home addresses among them. The Optus breach did not involve Kafka, but the same fields travel through a telco’s CRM, billing and notification topics, which is why every tool that can display those topics becomes part of the data-protection question. In Australia, the Security of Critical Infrastructure Act 2018 lists communications among the 11 critical infrastructure sectors it covers, and in Europe GDPR Article 5 sets the principles, data minimisation and storage limitation among them, for any system that processes subscriber data.

Each of the six criteria this page scores comes from those needs.

Out of the data path. A management tool reaches a cluster as an ordinary Kafka client or as a proxy that applications connect through, and at a telco that difference decides what a network and security review has to cover. A client-side tool’s footprint is short to describe: its Kafka traffic goes over the Kafka protocol with the standard clients, and its only plain HTTP calls on the data side are to the REST APIs of the services it manages, such as the Kafka Connect REST API, plus schema registry and cloud provider APIs where those are configured. A firewall review therefore lists the brokers and those service endpoints. A proxy, by contrast, carries every billing, CRM and notification event that uses it, so it has to be sized and kept available for peak traffic.

Who can see unmasked data. Masking is only as good as the formats it understands. Structured redaction needs a deserializer that can read the record, so a tool masks individual fields in the formats it can parse and can only redact an opaque or base64 payload whole; Kpow’s data policies list Protobuf, Avro, JSON, Transit and EDN, plus custom SerDes that produce JSON. Masking at display time also keeps key material out of the tool. Security teams tend to resist field-level encryption handled inside a UI, because it makes one application the holder of every key, and NIST’s recommendation for key management is built around limiting who and what holds keys; with display-time masking, a tool protects mobile numbers and addresses without any key at all, and where payloads are encrypted, decryption stays in the company’s own SerDes code.

Inspecting topic data. Amazon MSK, the managed service many telcos run on, does not come with a way to look inside a topic. Belong’s team found the choice was the command line or a separate tool, and its Kafka User Group talk explains why it bought one. How a search is governed matters as much as how fast it runs. When access control is applied to the search plan rather than to the results, a query is first resolved into exactly which topics and partitions it will read, and the user’s permissions are checked against that plan, so a search across several topics, or by regular expression, cannot reach a topic the user may not read. That is the role-based access control model applied to data, not only to buttons.

Audit trail per person. At a telco, the search box is itself a place where personal data turns up: engineers chasing a duplicate SMS type a customer’s mobile number into a message filter. An audit log that records query text, which a complete audit log should, can therefore hold personal data, and its retention and access rules have to be set with the storage limitation and data minimisation principles of GDPR Article 5 in mind. Kpow’s audit log records data inspect queries with the person who ran them, which is what a risk team asks for, and the same record is one the company should retain and restrict deliberately.

Directory and Kafka sign-in. Two layers of authentication are often confused. Belong chose SASL/SCRAM on MSK, with each username acting as the principal its topic ACLs are written against, and required every producer and consumer to authenticate because the cluster would eventually hold PII, as its talk describes. That governs applications, while people who open a management tool should sign in through the company’s identity provider, with directory groups mapped to roles, while the tool itself connects to the brokers the same way the cluster already authenticates clients.

Many teams, shared clusters. Access policy works best when it follows the topic naming convention, because the convention is where ownership is already recorded on a shared cluster. Belong names topics by environment, event type, data set and data name, so that teams publishing and consuming the same data can find it and see who owns it. Kafka’s own multi-tenancy documentation recommends prefixed ACLs over such naming conventions for the same reason, and Kpow’s role-based access control accepts wildcards anywhere in a resource name, so one rule can cover every topic that starts with a team’s prefix or every dead-letter topic that ends in -dlq. Permissions are also split where risk differs: in Kpow, reading topic data and downloading it are separate actions, so engineers can search subscriber records without being able to export them, which is the least privilege principle applied to the worry that data gets copied out.

What telecommunications companies use Kpow for

One telecommunications company that runs Kpow is named here: Belong, Telstra’s low-cost mobile and internet brand in Australia. Its use is described only as far as its head of enablement stated it in a public talk, and the card is tagged with the rubric criteria that evidence speaks to.

How telecommunications companies run their Kafka with Kpow

The workflows below are how a telco’s platform team puts Kpow to work on clusters that carry subscriber and network events. Each one is built from documented Kpow features.

Install it beside the clusters. Kpow runs as one Docker container or Java JAR, or on Kubernetes with the Helm charts, inside the company’s own network or AWS account. It needs no external database, because snapshots, metrics and the audit log are held in topics on the company’s own clusters, and it connects with the same cluster security settings as any other client. A company on AWS can subscribe through AWS Marketplace, which keeps the purchase inside an agreement procurement has already approved.

Connect it to Amazon MSK and Glue. On MSK, Kpow connects with the cluster’s own authentication, SASL/SCRAM or IAM, as the MSK setup guide shows, and reads Avro records through AWS Glue Schema Registry. On MSK the registry is AWS Glue, as AWS’s own schema registry documentation describes, and monitoring runs through CloudWatch, so a team moving from a bundled platform assembles those pieces itself; Belong pairs MSK with Glue and Avro for its domain and integration events. Best Kafka UI tools for AWS Glue Schema Registry compares how other tools decode Glue-serialised records.

Mask subscriber fields. Data policies redact mobile numbers, names and addresses in Data Inspect results on the server, before anything reaches the browser, and String SerDes are removed from Data Inspect while a policy applies, so nobody can read a structured message as raw text to get around it. Best tools for Kafka data masking compares masking across tools in more depth.

Find the messages behind one customer’s complaint. When a customer reports a duplicate SMS or a missing bill reminder, an engineer opens Data Inspect, selects the notification topics and filters with a kJQ expression on the customer or message ID. The filters are compiled and executed on the server, which the documentation says allows tens of thousands of messages a second to be searched, and the masked fields stay masked in the results.

Replay a failed message. After a downstream outage, Clone to topic copies a record from search results to a retry topic, an action that has to be enabled for a role in access control, so the replay is done in the same console, under the same roles and audit log as everything else, instead of with a one-off script. The patterns and traps of retry and dead-letter topics are covered in Dead letter queues in Kafka: patterns and pitfalls.

Give each team its own view. The platform team adds a tenant for each team, defined by the topic prefixes the naming convention already gives it, so CRM, billing, network and marketing each see their own topics, consumer groups and connectors on a shared cluster, and RBAC sets what each role may do within them.

Grant production access for one incident. Temporary policies give a role read access to a production topic for a fixed time, and the Kpow API lets a change system create them, so an engineer can investigate a live incident without a standing grant. Staged mutations hold risky changes, such as an offset reset on a notification consumer group, for a second person’s approval.

Watch consumer lag on the notification path. Kpow shows consumer group offsets and lag in the UI and publishes them, with broker and topic metrics, on Prometheus endpoints, so a group that falls behind during a burst of outage notices shows up in the company’s existing alerting.

Keep what the team learned in one place. Kafka operating knowledge is tacit, and teams lose it as people move on; in the question time after Belong’s talk, the team said post-incident reviews and knowledge articles help, but no method is foolproof. A shared console with a per-person audit trail keeps part of that record: what was changed, by whom and when, readable by the next engineer on call.

To see these screens before installing anything, the live Kpow demo needs no signup and shows data inspect, consumer groups, topic management and the audit log, though data masking cannot be shown in it.

Kpow live demo

Open Kpow the way a telco platform team would

The live Kpow demo needs no signup. Search topic data with kJQ, check consumer groups and lag, then read the audit trail on the __oprtr_audit_log topic of the MSK Secondary cluster.

For platform teams running Kafka for billing, CRM, network and notification teams.

Try the Kpow demo→

FAQ

What is the best Kafka UI for a telecommunications company?

On this page’s rubric, Kpow: it masks subscriber fields on the server, searches live topics across several topics at once with kJQ, records every action and query with the person from the directory, and gives each team its own view of a shared cluster, from one container that runs beside the brokers. It scores 84 out of 100, ahead of Kafbat UI at 70 and AKHQ at 66. Lenses has the stronger query language, and Conduktor Console the more flexible masking exemptions.

Does Amazon MSK have a built-in Kafka UI for browsing messages?

The MSK console manages clusters and configuration; for looking inside topics, teams use the command line or a separate tool, which is the choice Belong’s team described in its talk. Best Kafka UI tools for Amazon MSK compares the options on MSK specifically.

How can engineers debug notification topics without seeing customers’ mobile numbers?

Mask the subscriber fields in every tool engineers use to inspect those topics, on the server and for every format the topics use, and keep reading topic data separate from downloading it. Masking at display time leaves the stored records unchanged, so it protects what people see, not what consumers read.

Does masking in a Kafka UI protect the data on the brokers?

No. View-time masking hides values from people inspecting topics through that tool and leaves the stored records and what consumers read unchanged. Values that no consumer needs in full, such as one-time passcodes, are better kept off general-purpose topics, which is the choice Belong describes for OTPs in its talk.

Is a Kafka management tool in scope for the SOCI Act?

The Security of Critical Infrastructure Act 2018 applies to assets in the sectors it covers, communications among them, and what it asks of a given telco depends on which of its assets are covered. This page does not assess any company’s obligations; it scores tools on what any system that can display subscriber data should do.

Does Kpow add latency to SMS or billing events?

Not to the events themselves. Kpow connects to the brokers as an ordinary Kafka client beside the producers and consumers, so their traffic never passes through it, and it reads records only when a person runs a query.

How these tools were scored

Six criteria, each taken from a situation telco platform teams meet when subscriber and network events flow through Kafka, score every option from 0 to 10. They are listed here in order of weight.

1. Out of the data path. A management tool that runs in the company’s own environment, connects as an ordinary Kafka client and keeps no data outside the company’s clusters is the shortest answer when a security review asks which components handle subscriber data. Scored lower: tools that need an external database, and tools whose controls work only when application traffic passes through a vendor’s proxy. A self-hosted container with no external database and no proxy scores 9, a tool with a database of its own 6, one with several databases or a component on the brokers 4, and one that needs both a database and a proxy for its controls 3; 10 is kept for an option with nothing to deploy at all. This criterion is scored the same way on every Factor House page that uses it, and only its weight changes with the reader.

2. Who can see unmasked data. It scores who can see a real mobile number, name or address through the tool, whether masking can be bypassed, and whether the team that owns the data can be exempted while everyone else sees it masked. The scores are the same as on Best tools for Kafka data masking, and Control Center, which that page does not score, gets 2 because no masking is described for it.

3. Inspecting topic data. How well people can find and read the messages they need: search by field across topics, the query language, and how quickly a search returns on a busy topic. This is the reason most telco engineers open a Kafka tool at all, during an incident or a customer complaint.

4. Audit trail per person. When people work through a shared tool, the broker only sees the tool’s service account, so only the tool’s own log can name the person. The log has to include data reads and queries as well as changes and be readable without building a consumer first. Best tools for Kafka audit logging compares the layers in detail.

5. Directory and Kafka sign-in. People should sign in through the company’s identity provider, whether that is SAML, OIDC or LDAP, with directory groups mapped to roles rather than shared logins, and the tool has to connect to the brokers the way the company’s clusters already authenticate clients, including SASL/SCRAM and IAM on Amazon MSK. Best tools for Kafka SSO integration covers the protocol detail.

6. Many teams, shared clusters. CRM, billing, network and marketing teams on a handful of clusters need each team scoped to its own topics, consumer groups and connectors, so the platform team can onboard a team with a policy rather than a cluster, and nobody sees another team’s subscriber topics by default.

The cost figures model a telecommunications company running 4 clusters (development, test, pre-production and production) for 100 engineers at $120 an engineer-hour, and each card prints its own assumptions. The general listicle view, without the telco weighting, is in Best Kafka management tools for 2026.

F1 What a telecommunications company runs into, and what the Kafka tool has to do about it

What happens at a telecommunications company What the Kafka tool has to do

Subscriber data in topics CRM, billing and notification topics carry mobile numbers, names and home addresses Mask those fields on the server for everyone who inspects the topics, for every format the topics use

A customer got the same SMS twice An engineer has to find the messages behind one customer's notifications, in production, during the incident Search live topics by field across several topics at once, and show which partitions and offsets the search covered

Copying data out Security and risk teams worry about subscriber records leaving the platform Keep reading topic data and downloading it as separate permissions, and record every query with the person who ran it

Many teams, one platform CRM, billing, network and marketing teams publish to the same clusters under a topic naming convention Write access policy against the naming convention, so one rule covers every topic a team owns

Managed Kafka The clusters run on a managed service whose console does not browse topic data Connect as an ordinary Kafka client with the cluster's own authentication, beside the brokers and not in front of them

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, Who can see unmasked data counts twice, Inspecting topic data counts twice, Audit trail per person counts once, Directory and Kafka sign-in counts once and Many teams, shared clusters counts once, for a total out of 100. Out of the data path counts three times because a telco's Kafka carries billing, CRM and customer notification events around the clock, and a tool that sits between the applications and the brokers puts a vendor's proxy on the path every one of those events takes, while a tool with a database of its own is one more system to patch and keep available. Who can see unmasked data and inspecting topic data count twice: subscriber topics carry mobile numbers, names and home addresses, and the operational reason engineers open a Kafka tool at a telco is to find the messages behind one customer's bill, SMS or outage notice. The per-person audit trail, directory and Kafka sign-in, and shared clusters count once each. 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 84 out of 100. The other options follow by total. Conduktor is listed last whatever its total; on its total of 61 it would place fifth.

Related reading

• Kafka: the complete guide
• Implementing Kafka at Belong
• Best Kafka UI tools for Amazon MSK
• Best Kafka UI tools for AWS Glue Schema Registry
• Best tools for Kafka data masking
• Best Kafka management tools for energy and utility companies
• Best Kafka management tools for banks
• Best Kafka governance tools for financial services