I’ve been using Lenses Community Edition for some time and our Kafka adoption has grown. We’re now looking to deploy an enterprise solution. Conduktor.io would be the alternative option but seems a bit different. What’s the consensus on the difference between the two solutions?
Hi James, I’m glad you are enjoying the Community Edition!
I will try my best to answer honestly, as I am on the Lenses team and, hence - as is only normal - a little biased.
The two products compete a little on the surface, but they have very different philosophies.
We have been working with Apache Kafka since 2016, and to our experience, the biggest problem with Kafka today is the complexity of building streaming apps in a multi-Kafka, multi-vendor environment; dozens of Kafka clusters, perhaps some in the cloud, some on-prem. The more apps there are, the more complex it becomes for engineers, as more things need to be locked down, and the developer experience across different clusters may differ. Confluent, AWS, Redpanda, and the other Kafka infrastructure providers have done an amazing job of addressing the infrastructure and platform engineering problems with Kafka. Yet the Developer Experience problem exists.
Lenses is highly focused on giving as much simplicity, self-service, and autonomy for developers (Software, AI, Data, …) to build and operate streaming apps in an enterprise with strong security and compliance requirements and with large Kafka estates. Avoiding dependence on Platform Engineers.
Lenses is also highly focused on AI-assisted developer experience. Its MCP and in-built AI Agents are designed to be integrated into IDEs, Agents, and copilots such as Cursor.
Our focus has always been on developers, and Lenses continues to evolve to solve current developer problems.
Conduktor, as I see it, is more targeted on the data infrastructure requirements, those addressed by AWS, Confluent, Redpanda that I talked about and without AI-assistance. They also focus on installing their gateway within the architecture between clients and brokers, which may add complexity and risk to the project and create lock-in.
A key principle of Lenses is “bring your own infra” (e.g., Kafka, Kubernetes, LLMs); it doesn’t disrupt or impose data infrastructure architectures or components.
A good example of this is the SQL Snapshot engine, which brings SQL data exploration in topics without having to move that data out of a cluster. Solutions such as Conduktor will move the data into ClickHouse or a similar system, which adds cost and risk and makes compliance more challenging.
Finally, because of Lenses’ historical background in the stream-reactor project, the team is very strong in data movement, processing & replication. Stream-reactor, if you haven’t come across it, is a collection of open-source, Apache-licensed connectors that has become an industry standard. These are key to a developer workflow for building and testing a streaming app, migrating an app to another cluster, or sharing data. That’s why you’ll notice Lenses embedding its SQL Processing engine, K2K Data Replicator, and, of course, all of the Stream Reactor Kafka Connectors into the Developer Experience.
In any case, don’t just take my word for it, try both and see which suits you.