Best Kafka management tools for retailers

Tools compared

Rank	Tool	Total (out of 100)	Out of the data path	Many teams, shared clusters	Directory and Kafka sign-in	Production access on request	Audit trail per person	Inspecting topic data	Cost a year (modelled)
1	Kpow	90	One container, state in your Kafka, not a proxy	Tenants and per-action RBAC	SAML, OIDC, LDAP; any SASL or mTLS to brokers	Time-boxed temporary policies via API, staged approvals, masking per resource	Every action with the IdP user, data queries included	kJQ search across topics, streaming search	$20,880
2	Kafbat UI	71	One stateless container	Per-resource roles per cluster	OAuth2, OIDC, LDAP; no SAML	Per-resource RBAC, no approvals, masking for all viewers	Optional, reads at level ALL, no view	Browse and inspect messages	$11,520
3	AKHQ	64	One stateless container	Groups by resource and cluster pattern	LDAP, OIDC; no SAML	Regex groups, UI-only if JWT secret unset	Opt-in, no reads, no view	Browse topic data	$16,320
4	Lenses	59	HQ on PostgreSQL, agent and database per cluster	Roles on groups only	SSO incl. Entra ID and Okta	Strict global masking, no approvals	In-product audit log from Team tier	SQL over topics	$2,880 plus quoted licence
5	Confluent Control Center	42	Dedicated host, broker reporter JAR	Admin access only, per TD	OIDC on self-managed	Confluent RBAC, no DENY, no approvals	Broker principal, not always the person	Messages view, Avro key bug	$2,880 plus quoted subscription
6	Conduktor	59	Console on PostgreSQL; Gateway proxy in the data path	Per user or group, most permissive grant wins	LDAP, OIDC	Per-viewer masking, cross-team access requests	70+ event types with user, in the UI	Browse and filter topic data	$122,880; $212,880 with Gateway Core and Protect

No tool meets every column, and retailers commonly pair a management tool for people with broker ACLs or IAM policies for services.

The tools, ranked for retailers

What retailers need from a Kafka management tool

This page is about retailers as a whole: groups that sell through stores and online and run their own supply chain, where one platform team runs Kafka for many squads. For supermarket chains, grocers and foodservice distributors, where one tool has to reach a mix of self-managed and managed clusters, see best Kafka management tools for supermarkets and food distributors. For a bank’s version of the same shared-cluster problem, under a regulator, see best Kafka management tools for banks, and for the regulation-by-regulation view across financial services, see best Kafka governance tools for financial services. Online-first businesses, ecommerce companies and marketplaces that run Kafka with a small platform team are covered in best Kafka management tools for ecommerce companies and marketplaces.

A retailer’s Kafka is shared by many teams. Orders, stock, prices, deliveries, payments and store events all move through it, and the squads that own each of those services need to see their own topics and consumers without a ticket to the platform team. Retailers that run Kafka this way often build a GitOps pipeline so teams can provision topics, permissions, schemas and connectors themselves. US Foods gives around 130 developers across 12 product teams access to Kafka through Kpow, with delete rights held by two people. The management tool has to make that self-service safe: each team scoped to its own resources, and destructive actions kept to a few people.

The number of teams shapes the clusters as well as the permissions. Walmart’s real-time inventory system, which its director of engineering described in a 2020 post on Confluent’s blog, takes events from more than 10 sources and keeps one canonical view of inventory positions across stores, ecommerce and distribution centres; how Walmart uses Apache Kafka in production covers that system. With that many producing teams, a platform team tends to drift towards one of two opposite patterns, which a talk on cluster consolidation describes: staying on one monolithic cluster because it is easier, or giving every team its own cluster and ending up with 50 before long. The workable middle is a small number of clusters that many teams share, and the Apache Kafka multi-tenancy guide documents how that is done, with a topic namespace for each tenant, authentication and authorization, and quotas. This page’s cost model follows it, with one production cluster for ecommerce and one for stores and supply chain.

Kafka’s own access control does not stretch to that arrangement by itself. An ACL binds one principal to one operation on one resource, so the number of rules grows with the number of teams multiplied by the number of topics, and some of the largest operators have moved the policy out of ACL lists altogether. Uber’s engineers describe in Securing Kafka infrastructure at Uber how they implemented Kafka’s Authorizer interface to delegate authorization to the company’s central identity and access management framework, and how they plan to hand access policy for each topic to the team that owns it, with an audit trail. Most retailers will not write their own authorizer, so the same job falls to the management tool, where the equivalent mistake is a role for every squad. The NIST model of role-based access control is built from users, roles, permissions, operations and objects, and a role only saves work when many users share it, so twelve roles for twelve product teams rebuild the ACL list under another name. The arrangement that holds up is a few generic roles combined with a scope for each team, which is what US Foods runs: self-service plans for about 130 developers, and delete rights for two solution architects.

Scoping what a team can see does not stop its workload from crowding out another team’s. On a shared cluster that is the job of quotas, which the same Kafka guide says multi-tenant clusters should generally be configured with, to protect against tenants that write or read very high volumes of data or send requests at an excessively high rate. In retail the case to plan for is a reporting or analytics job replaying months of sales on the cluster that also confirms orders. Quotas are enforced by the brokers, and tenants and roles in a management tool do not replace them.

The people who need access are not all Kafka engineers. Gmarket’s case study describes non-Kafka engineers resetting offsets and reassigning partitions through the UI, US Foods’ QA teams in India and Argentina sign in through single sign-on, and data, support and merchandising staff often need to check that an event arrived. Sign-in has to go through the identity provider the company already runs, and finding one order or one stock update has to work without writing a consumer against production. With users spread across countries, employers and time zones, single sign-on gives the retailer one consistent entry point, which the US Foods case study credits with making the same tool work the same way whichever time zone is logging in, and it gives the retailer one place to take access away when a contractor’s engagement ends.

Card payments add a formal version of that requirement. PCI DSS requires that every user is assigned a unique ID before being allowed access to system components or cardholder data, and that accounts used by third parties to access, support or maintain system components are enabled only during the time period needed, disabled when not in use and monitored (requirements 8.2.1 and 8.2.7, quoted in Microsoft’s guidance on PCI DSS requirement 8). Where a retailer’s Kafka users include agency staff, an outsourced test team or contractors, that requirement maps onto a directory group the retailer controls and access that is granted for a period and then ends. Kafka does not start from a secure position: the Apache Kafka security overview notes that security is optional and that non-secured clusters are supported, and a tool that sits in front of the cluster inherits that choice. In an evaluation the thing to confirm is that sign-in is enforced on the tool’s API as well as in its UI, and that nobody can reach production data without it.

Retail has its own peaks. Black Friday, seasonal sales and promotions drive consumer lag on order, inventory and pricing pipelines, and that is when the tool is used most and can least afford to get in the way. Large retailers rehearse those days, and the rehearsal shows what a tool’s position in the architecture costs. Shopify’s account of preparing for Black Friday and Cyber Monday in 2025 describes fire drills run through the year that simulate 150 percent of the previous year’s peak load, with extra time spent on checkout, payment processing, order creation and fulfilment, and it names Kafka among the bottlenecks those tests exposed. Every component that order traffic passes through is part of that test and of the capacity plan that follows it. A Kafka proxy is such a component, because every producer and consumer connects through it, and on the order path it is one more service that has to stay up through peak trading. A management tool that connects as an ordinary Kafka client carries no order traffic, so the volume of a sale never reaches it and its load follows the number of engineers using it.

For a retailer, that is the main difference between Kpow and Conduktor. Conduktor Console connects to clusters directly and masks data in its own UI, but it needs an external PostgreSQL database, and Conduktor’s encryption, masking of the data itself, policy enforcement on client traffic and Virtual Cluster multi-tenancy all run through Conduktor Gateway, which Conduktor’s own documentation describes as a Kafka proxy between client applications and brokers. The trade runs in both directions, because Gateway can enforce policy on applications and a client-side tool such as Kpow does not attempt that.

Many retailers also run more than one kind of cluster: Factor House’s retail page names Mercadona and Article among the retail platform teams that use Kpow across Confluent, MSK and self-hosted clusters, and Gmarket moved from the licensed Confluent distribution to community Kafka. Each kind of cluster arrives with its own console and its own metrics. Amazon MSK sends Apache Kafka metrics to CloudWatch, Confluent Cloud publishes its own through a separate Metrics API, and a team running both has two places to look and two metric formats to reconcile during an incident. Kafka-compatible services also differ at the edges: Confluent Cloud does not support the Kafka AdminClient call that normally returns disk usage, so a provider-neutral tool has to fetch it from that Metrics API, as Kpow’s Confluent Cloud documentation sets out. A console supplied by the provider also leaves with the provider. When Gmarket moved off the licensed Confluent distribution it lost Control Center, and with it the consumer lag and offset views its teams relied on.

Order topics carry customer names, addresses and payment details. A retailer that takes card payments works under PCI DSS and one that sells to customers in the EU under GDPR, and Factor House’s retail page describes a European supermarket chain whose Kpow audit trail of cluster operations supports both. A retailer may be less regulated than a bank, but it still has to know which third parties can see that data, which is why this page weights staying out of the data path highest.

Both regimes turn on who handles the data on the retailer’s behalf. GDPR Article 28 allows a controller to use only processors that provide sufficient guarantees, and the PCI Security Standards Council’s glossary defines a service provider as a business involved in the processing, storage or transmission of cardholder data on behalf of another entity, including companies whose services could affect the security of that data. A console hosted by its vendor that reads order topics puts the vendor in both categories, with the contract and the review that follow. Software that the retailer runs inside its own network, and that sends no topic data out of it, is one more product to patch but adds no new party that holds customer data.

The actions that do the most damage during trading are the ones that write, and two of them are riskier than they look. A topic delete does not always stay deleted, because Apache Kafka’s brokers create topics automatically by default, so a client that is still running can recreate the deleted topic at once with the broker’s default partition count, retention and replication, and the mistake appears later as a topic that behaves differently. The broker setting is auto.create.topics.enable, which the Apache Kafka broker configuration reference lists with a default of true. Managed services can differ, and Amazon MSK’s default configuration sets it to false, so a retailer with self-managed and managed clusters has to check each one. Producing a message by hand is the other, because a record written to an order topic from a console is a real order to every consumer downstream. TD describes the rule it applies in its talk on running Kafka at bank scale: reading production data is available on request, while anyone who wants to publish to a production topic has to ask for permission and an exception and explain why. A retailer has the same reason to separate the two, with inspection open to every squad and deletes, offset resets and production writes kept to a few people, as US Foods does with delete rights, or held for a second person’s approval. Card data adds a review cycle on top, since PCI DSS requires all user accounts and access privileges, including those of third parties and vendors, to be reviewed at least once every six months (requirement 7.2.4, quoted in Microsoft’s guidance on PCI DSS requirement 7), and a grant that expires on its own leaves nothing standing for that review to find.

Masking decides whether engineers can be let into order topics at all. Without masking of individual fields, the only safe policy for a topic that carries names and addresses is to deny access, after which developers raise tickets and a platform engineer extracts and cleans the data by hand, a slow process that is itself an error-prone control over data that GDPR Article 32 requires to be protected with measures appropriate to the risk. Belong, an Australian telecommunications brand, describes in its talk on implementing Kafka at Belong how masking mobile numbers, names and addresses in its Kafka console helped with its cyber security and risk teams, in part because it helps prevent people copying data out. For card numbers, the PCI Security Standards Council’s guidance on masking and truncation is that only the minimum number of digits needed for the business purpose should be displayed, and its example is a customer service agent who needs only the last four digits to verify a card.

The record of access matters to the same two regimes, and reads matter in it as much as changes. PCI DSS requires audit logs that capture all individual user access to cardholder data and all actions taken by anyone with administrative access, and requires the history to be kept for at least 12 months with the most recent three months immediately available (requirements 10.2.1.1, 10.2.1.2 and 10.5.1, quoted in Microsoft’s guidance on PCI DSS requirement 10). An audit trail held in a Kafka topic lasts only as long as that topic’s retention, so a retailer comparing tools should ask where each tool’s audit records are kept after the first week or month. GDPR Article 33 gives a retailer 72 hours to notify the supervisory authority of a personal data breach and asks the notification to describe, where possible, the approximate number of data subjects concerned. If an engineer’s account is compromised, a log of the topics that account searched is what lets the retailer count the customers in those topics and not every customer on the cluster. The brokers cannot supply that log, because when people work through a shared tool the broker sees only the tool’s own service account.

Much of what a retail platform team is asked comes down to where one order’s events went. Where there is no tool for answering that, teams build their own. Pickles, which runs live and online auctions, found that confirming a message had been delivered could take hours, started building custom tooling, and could not keep building a microservice for every use case. Retail adds a difficulty of its own, since the events that make up one order’s history sit in topics owned by different teams, so a search has to cover several topics at once and decode each team’s schema.

Customer deletion requests reach Kafka too, because GDPR Article 17 gives customers the right to have their personal data erased, and on a compacted topic erasing a customer’s records means producing a tombstone, a record with the customer’s key and no value. Teams are commonly caught out in two ways: by sending JSON null through a JSON serializer, which writes the text null as the value and deletes nothing, and by expecting a tombstone to remove data at once, when it removes nothing until log compaction next cleans that part of the log. The article on deleting records in Kafka walks through both. Confirming that the tombstone is on the topic, and later that the earlier records are gone, is an inspection task.

The size of the audience shapes the bill as well as the controls. A retailer has a few platform engineers and a much larger group of occasional users, testers, support staff and analysts, who each look at a topic a few times a month. Pricing per seat charges for every one of them and pricing per cluster does not, and the effect runs in both directions: on the published prices in this page’s cost model, $1,200 a seat against $4,500 a cluster, four clusters cost the same as 15 seats, so a team smaller than that pays less per seat and a larger one pays less per cluster. Claritev’s case study reports the same comparison from the buyer’s side, with pricing by the cluster coming out ahead at about forty users. Free tools carry a cost of their own in the engineering time that keeps them patched. In 2024 GitHub Security Lab published three remote code execution vulnerabilities in Kafka UI, the original open-source project of which Kafbat UI is the maintained fork, one of them reported in November 2023 and patched in April 2024, and applying a fix of that kind is work for the team that runs the tool, which is the operator time this page’s cost model counts for the free options.

What retailers use Kpow for

Five retailers and distributors that run Kpow are named here: US Foods, Gmarket, Mercadona, Article and Reecetech. US Foods and Gmarket have described their use in case studies published by Factor House, and Factor House’s retail solutions page names Mercadona and Article as Kpow users, so those four cards are tagged with the rubric criteria their evidence speaks to. Reecetech has not published how it uses Kpow, so its card carries public information about the company itself.

How a retailer runs its Kafka with Kpow

The workflows below are how a retail platform team puts Kpow to work on shared clusters. Each one is built from documented Kpow features, and the US Foods and Gmarket case studies describe several of them in production.

Install it beside the clusters. Kpow runs as one Docker container or Java JAR, or on Kubernetes with the Helm charts, inside the retailer’s own network or cloud account. It connects to brokers as an ordinary Kafka client, with the same cluster security settings as any other client, so order and payment events keep flowing from applications to brokers directly. It needs no external database: its system requirements state that snapshots, metrics and the audit log are held in topics on the retailer’s own cluster and that Kpow has no dependencies beyond Kafka, and the Kpow product page says it runs “with no data leaving your environment”. One instance manages up to 12 clusters, whether they are self-managed Apache Kafka, Confluent Platform, Confluent Cloud or Amazon MSK, so ecommerce, store and supply chain clusters sit in the same view, and a larger fleet runs more than one instance.

Give each squad its own view. A tenant includes or excludes topics, consumer groups and whole Connect clusters or schema registries by name, prefix or suffix, so a team whose topics start with checkout- sees only those, plus the topics its consumer groups read, as if they were the only resources on the cluster. Tenants and RBAC are defined in a YAML file, which fits a GitOps review, and RBAC sets Allow, Deny or Stage per action and resource, with Deny winning where policies overlap. A new squad is onboarded by adding its prefix and its directory group to that file and not by creating a cluster. The file is read when Kpow starts, so a permanent change to it takes a restart, a limit Factor House’s post on its commitment to engineers acknowledges when it lists RBAC changes without restarts among the differences in Factor Platform, and a change that cannot wait is made as a temporary policy.

Sign people in through the company directory. Kpow takes SAML from Okta or Microsoft Entra ID, any OpenID Connect provider, or LDAP, and maps directory groups to roles and tenants. Offshore QA teams, contractors and analysts use the same entry point as the developers, with the access their group carries.

Find one order across topics. When an order, a delivery or a stock update looks wrong, an engineer uses Data Inspect with a kJQ filter to search for one order ID across several topics at once, on the server, by key, value or header. The result reports, for each partition, the start and end offsets, the number of records scanned and the offsets still to query, as the article on querying a Kafka topic shows, so an engineer can tell an order that is not on the topic from a search that has not finished. Data policies redact names, addresses and card fields in the results, down to the last four digits of a card number, so the engineer sees the event history without the customer’s details. A data policy is set per resource and applies to every viewer, so it cannot show the team that owns a topic the real value and everyone else the masked one, which Conduktor Console can, and teams that want SQL over topics will find Lenses stronger than kJQ.

Fix lag during peak trading. Kpow shows each consumer group’s lag by topic and partition, and publishes it with broker, topic and connector metrics on Prometheus endpoints for Grafana, AlertManager or the retailer’s own monitoring, so the alert rules sit with the rest of the retailer’s alerting. Lag by partition matters in retail because a group’s total hides the usual shape of a promotion incident, in which one partition falls far behind because one product’s or one store’s key is busy while the rest are caught up, a pattern the article on topic and partition best practices calls partition skew.

Rising lag also invites the wrong fix, and the talk on Kafka operational issues describes one case, a service whose 400 instances each started 100 consumers in one group, which made 40,000 members for 1,000 partition assignments. When lag grew the team doubled the instances, which doubled the idle members and lengthened every rebalance, and then added brokers, which added work for a group coordinator already at 100 percent CPU, and nothing on its dashboards showed the size of the group. Apache Kafka’s developers redesigned that rebalance protocol in KIP-848, whose motivation section calls group membership and rebalancing a major pain point in operations, and until every client uses the new protocol a group’s member count and state belong beside its lag when a team decides whether to scale.

A second pattern sends customers the same message twice. A consumer that takes longer over a batch than the consumer group’s failure-detection timeout allows is treated as dead, its partitions are given to another consumer and the batch is delivered again. Belong describes this happening to SMS notifications on default settings in its talk on implementing Kafka at Belong, where customers received several copies of the same message, and the retail equivalent is an order confirmation or a delivery notice sent twice in the busiest week of the year. The timeout is max.poll.interval.ms, which the Apache Kafka consumer configuration reference defines as the maximum delay between polls before the consumer is considered failed and the group rebalances.

When a consumer has to skip or replay messages, the group actions reset, clear or skip offsets for a whole group, a host, a topic or a single partition, scheduled to run once the group is stopped, which is how US Foods runs offset changes as part of its production deployments.

Add partitions before a sale with care. Capacity work before a sale often includes raising partition counts, and Shopify’s preparation for 2025 found that its analytics pipelines needed Kafka partition increases to keep data fresh during spikes. The Apache Kafka operations guide lists the side effects of that change. Records with the same key may be routed to a different partition after the increase, which affects ordering for existing keys, so the events for one order can end up split across two partitions. Consumers configured to start from the latest offset can also miss messages written to the new partitions before they discover them, which is one of the incidents in the operational issues talk, where a planned partition increase left tens of thousands of messages unread until a separate consumer was started to replay them. Before the change, Data Inspect shows whether the topic’s records carry keys, and after it the lag view by partition shows whether the new partitions are being read.

Hold risky changes for a second person. With staged mutations, a role can be set to Stage on actions such as deleting a topic or resetting offsets in production, so the request waits in Kpow until an administrator approves or denies it. Where an engineer needs extra rights for one task, an admin creates a temporary policy that expires on its own. An offset reset made during an incident is a decision to skip or repeat orders, so the request, the approval and the reset are all written to the audit log with the people who made them. Producing to a topic is a separate permission in the RBAC file, so a role can inspect production topics and be denied writing to them. A team that keeps topic changes in its own GitOps pipeline can still plan them in Kpow, because the topic creation form generates the equivalent kafka-topics.sh command as it is filled in, which the article on topic and partition best practices describes.

Restart a failed connector. Kpow manages Kafka Connect clusters beside the Kafka clusters they serve, so the team that owns a connector loading orders into a warehouse or a search index can see its tasks, read the error and restart it from the same UI, within its tenant. The task is the level to read, because a connector can report that it is running while one of its tasks has failed: the Kafka Connect REST API reports the status of a connector and the status of each task separately, and a failed task carries its own error information.

Keep a record of who did what. The audit log records each action, data inspect queries included, with the user from the directory and the policy that allowed it. Kpow shows the last seven days in the product and writes the record to the __oprtr_audit_log topic on the retailer’s own cluster, where Kpow’s topics default to one week of retention, so for a longer record webhooks send mutations, queries or both to Slack, Microsoft Teams or the retailer’s SIEM. A retailer in PCI DSS scope needs 12 months of history with three months immediately available, so either the audit topic’s retention is raised or the SIEM is treated as the system of record.

Keep broker ACLs and quotas for services. Kpow governs people working through Kpow, not services connecting to brokers, so broker ACLs or IAM policies remain the control for applications, and quotas remain the control for one workload crowding out another. Policy enforced on application traffic itself is what a proxy such as Conduktor Gateway provides, and a client-side tool does not attempt it.

To see these screens before installing anything, the live Kpow demo needs no signup and runs on two MSK clusters. It lets a visitor do what a retail engineer does when an order looks wrong: search a topic for one key, follow a consumer group’s lag, then open the __oprtr_audit_log topic on MSK Secondary to see what the audit trail records. The demo has no SSO, tenants or data policies configured, so sign-in, per-team views and masking are the things to test in the retailer’s own environment. To run the workflows against the retailer’s own clusters, install Kpow from its container image or JAR; tenants, RBAC, temporary policies, staged mutations, masking and the audit log are Kpow Enterprise features, and Community Edition is free for 3 clusters and 10 users but holds none of them.

Kpow live demo

Open Kpow the way a retailer's engineers would

The live Kpow demo needs no signup. It runs on two Amazon MSK clusters: search topics for one key, follow a consumer group's lag, then read the audit trail on the __oprtr_audit_log topic of the MSK Secondary cluster.

For platform teams running Kafka for many retail squads on shared clusters.

Try the Kpow demo→

FAQ

What is the best Kafka UI for a retailer?

On this page’s rubric, Kpow: it scopes each squad to its own topics, groups and connectors with tenants, signs people in through Okta, Entra ID or any SAML, OIDC or LDAP directory, searches several topics at once on the server, holds production changes for approval, records every action with the person, manages self-managed, Confluent and MSK clusters from one deployment, and runs as one container with no external database and nothing in the data path. Kafbat UI is the strongest free option for a single team that signs in with OIDC.

How do you give many retail teams self-service access to one Kafka cluster?

Scope each team to its own resources instead of giving everyone the whole cluster. In Kpow a tenant includes a team’s topics and consumer groups by name, prefix or suffix and maps to the team’s directory group, so the team sees and works on only its own resources, while RBAC keeps deletes and offset resets in production to a few people or holds them for approval. US Foods runs about 130 developers across 12 product teams this way, with delete access held by two solution architects.

Is a free Kafka UI enough for an online store?

For a small team browsing topics and consumer groups, often yes. Kafbat UI and AKHQ are free and run as one container each, and Kpow Community Edition is free for up to 3 clusters and 10 users, with topic search and inspection, consumer group and offset management, schema registry and Kafka Connect. None of the free options gives each team its own view of a shared cluster, an approval step before a change, or SAML sign-in without a separate proxy; in Kpow those are Enterprise features.

What replaces Confluent Control Center after moving to open-source Kafka?

Control Center comes with the Confluent licence, so a retailer that moves to community Kafka loses it. Gmarket made that move and replaced Control Center with Kpow for consumer lag and offset visibility, Data Inspect and self-service operations across 10 clusters. Kpow vs Confluent Control Center compares the two in detail.

How do retailers watch Kafka consumer lag during Black Friday and other sales?

Watch lag per consumer group and partition on the pipelines that carry orders, stock and prices, and alert on it from the monitoring the team already runs. Kpow shows each group’s lag by topic and partition and publishes it on Prometheus endpoints for Grafana or AlertManager; the consumer group metric is group_offset_lag. When a consumer has to skip or replay messages, Kpow’s offset actions are scheduled and run once the group’s consumers are stopped, and with staged mutations they can wait for a second person’s approval during trading hours.

Does a Kafka management tool need to be a proxy to govern access?

No. Governing what people can see and do in a tool, with tenants, RBAC, masking in inspection and an audit log, works from a tool that connects as an ordinary Kafka client. A proxy is only needed to enforce policy on application traffic, and it puts a component in front of every producer and consumer that sends or reads an order.

How these tools were scored

Six criteria, each taken from how retailers run Kafka, score every option from 0 to 10. They are listed here in order of weight; the weights add up to 10, so the total is out of 100.

1. Out of the data path. A management tool that runs in the retailer’s own environment, connects as an ordinary Kafka client and keeps no data outside the retailer’s clusters gives the shortest answer when the security team asks which third parties can see order and customer data, and leaves nothing extra on the order path during peak trading. 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. Many teams, shared clusters. Ecommerce, store, supply chain and payments squads 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 a squad sees only what it owns. This criterion uses the same scores as the banking page; best tools for Kafka role-based access control (RBAC) compares the permission models in more detail.

3. Directory and Kafka sign-in. People should sign in through the retailer’s identity provider, usually Okta or Microsoft Entra ID over SAML or OIDC, or LDAP, with directory groups mapped to roles, so offshore QA teams, contractors and analysts come in the same way as developers. On the cluster side the tool has to connect the way the retailer’s clusters already authenticate clients. Kafka SSO tools covers the protocol detail. Scores are the same as on the banking page.

4. Production access on request. Can an engineer be given extra rights for a task, approved and time-boxed, without a standing grant? Can production changes such as deleting a topic or resetting offsets be held for a second person’s approval? And are customer fields masked for the people who do get in? The wider set of controls over deletes and offset resets is compared in best tools to control destructive Kafka operations. Scores are the same as on the banking page.

5. Audit trail per person. When people work through a shared tool, the broker only sees the tool’s own service account, so only the tool’s log can name the person. That log should include data reads as well as changes, and be readable without building a consumer first. best tools for Kafka audit logging compares the layers in detail. Scores are the same as on the banking page.

6. Inspecting topic data. Tracing an order, a delivery or a stock update means finding specific messages by key, value or header across several topics, on the server, without writing a consumer against production. SQL over topics scores highest, filtered search across topics next, and browsing or filtering one topic at a time a point lower. Scores are the same as on the insurers page, and best Kafka message search tools covers search in detail.

Running several distributions at once, as the retail platform teams Factor House’s retail page names do across Confluent, MSK and self-hosted clusters, is not scored separately on this page; the banking page scores it, and the general comparison is best tools to manage multiple Kafka clusters from one place. Klaw, which the banking page scores highest of the open-source options on many teams sharing clusters, is not ranked here: it is a portal for requesting and approving topics and ACLs rather than an operations console, so a retailer using it would still need a second tool to inspect messages, reset offsets and restart connectors.

The cost figures model a retailer running 4 clusters (development, staging, and production clusters for ecommerce and for stores and supply chain) for 100 engineers at $120 an engineer-hour, and each card prints its own assumptions. The general listicle view, without this weighting, is in best Kafka management tools for 2026.

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

What happens at a retailer What the Kafka tool has to do

Many squads Ecommerce, stores, supply chain, payments and marketing teams share a few clusters Scope each team to its own topics, consumer groups and connectors, by name or prefix

Sign-in for everyone Developers, QA teams in other countries and business users all need a way in Sign people in through the company's identity provider and map its groups to roles

Finding one order An order, a delivery or a stock update looks wrong and nobody knows which service broke it Search topics by key, value or header on the server, without writing a consumer

Peak trading Consumer lag climbs on order and inventory pipelines during a sale Show lag per group and partition, and export it to the monitoring the team already uses

Changes in production Deleting a topic or resetting offsets during trading can stop orders Keep those actions to a few people, and hold the rest for a second person's approval

Mixed clusters Self-managed Kafka runs beside Confluent Cloud or Amazon MSK Manage every cluster from one deployment, whatever the distribution

Customer data Names, addresses and payment details travel in order topics Run inside the retailer's own environment as a Kafka client, not in front of the brokers as a proxy

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, Many teams, shared clusters counts twice, Directory and Kafka sign-in counts twice, Production access on request counts once, Audit trail per person counts once and Inspecting topic data counts once, for a total out of 100. Out of the data path counts three times because order, payment and customer events are what a retailer's Kafka carries, and a tool that sits between the applications and the brokers puts a vendor's proxy on the order path during peak trading, while a tool with a database of its own is one more system to patch and keep available. Many teams on shared clusters and directory sign-in count twice, because a retailer's Kafka is used by many squads and by people outside engineering, so the tool has to scope each team to its own resources and let everyone in through the identity provider the company already runs. Production access on request, the per-person audit trail and inspecting topic data 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 90 out of 100. The other options follow by total. Conduktor is listed last whatever its total; on its total of 59 it would place fourth.

Related reading

• Kafka: the complete guide
• How US Foods gave 130 developers self-service Kafka access with Kpow
• How Gmarket cut Kafka licensing costs and gave non-engineers self-service operations with Kpow
• Best Kafka management tools for banks
• Best Kafka management tools for supermarkets and food distributors
• Best tools to manage multiple Kafka clusters from one place
• Best tools to monitor Kafka consumer lag
• Best Kafka management tools for ecommerce companies and marketplaces