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Stop Wrestling with Kafka Clusters. Get a Fully Managed Kafka‑Native Data Streaming Platform in your own cloud

Instead of learning Strimzi, sizing clusters, tuning replication, and wiring Kafka Streams topologies, use Condense to deploy managed Kafka via BYOC, design pipelines visually, and scale streaming safely in AWS, Azure, or GCP

Condense gives you managed Kafka in your own cloud, a low‑code pipeline builder, connectors, and built‑in security so you ship real‑time apps faster

Condense gives you managed Kafka in your own
cloud, a low‑code pipeline builder, connectors, and built‑in security so you ship real‑time apps faster

No Payment Details Required!

Your Real-Time Setup, Done Right

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Condense on AWS : Managed Kafka Platform in Your Account

Condense on AWS (w/ Managed Kafka)Amazon MSK (Managed Kafka Service)
LayerKafka brokers + full streaming platformManaged Kafka brokers.​
FocusPipelines, transforms, governance, observabilityCluster availability, scaling, encryption
DeploymentAWS Marketplace BYOC into your AWS accountAWS Console / APIs in your AWS account
Who Builds PlatformCondense provides the platform out of the boxYour team (schema, connectors, UIs, tooling)

Get Started with Condense in 3 Simple Steps

Get Started with
Condense in 3 Simple Steps

Deploy Condense from Your Cloud Marketplace

01

Deploy

Deploy Condense from Your Cloud Marketplace

01

Deploy

Deploy Condense from Your Cloud Marketplace

01

Deploy

Create Your First Workspace

02

Create

Create Your First Workspace

02

Create

Create Your First Workspace

02

Create

Build Your First Real‑Time Pipeline

03

Build

Build Your First Real‑Time Pipeline

03

Build

Build Your First Real‑Time Pipeline

03

Build

Speak Your Industry's Language

  • Connected Mobility

  • Travel & Hospitality

  • IoT & Telemetry

  • Healthcare

Everything You’re Trying to Solve with Kafka DIY

All of these searches point to one reality: you’re being pushed into running and integrating a complex Kafka stack yourself. Condense exists so you don’t have to

Kafka Cluster Setup, Scaling,
and Performance Testing

Kafka on Kubernetes with
Strimzi and Operators

Kafka Streams, Topologies, and
Stream vs Consumer Confusion

Ecosystem complexity: Schema
Registry, Queues, and Alternatives

Frequently Asked Questions (FAQs)

Apache Flink vs Kafka Streams: Which does Condense support?
Condense abstracts the complexity of Kafka Streams vs Kafka Consumer groups. You write logic in standard languages; we handle state management and guarantees without a separate Flink cluster
Apache Flink vs Kafka Streams: Which does Condense support?
Condense abstracts the complexity of Kafka Streams vs Kafka Consumer groups. You write logic in standard languages; we handle state management and guarantees without a separate Flink cluster
Apache Flink vs Kafka Streams: Which does Condense support?
Condense abstracts the complexity of Kafka Streams vs Kafka Consumer groups. You write logic in standard languages; we handle state management and guarantees without a separate Flink cluster
Do I need to manage the Strimzi Kafka Operator?
No. Condense abstracts Strimzi entirely. You get the benefits of Apache Kafka in Kubernetes without the need for a dedicated K8s engineering team
Do I need to manage the Strimzi Kafka Operator?
No. Condense abstracts Strimzi entirely. You get the benefits of Apache Kafka in Kubernetes without the need for a dedicated K8s engineering team
Do I need to manage the Strimzi Kafka Operator?
No. Condense abstracts Strimzi entirely. You get the benefits of Apache Kafka in Kubernetes without the need for a dedicated K8s engineering team
How does Condense handle Tiered Storage?
Tiered storage is built-in. We automatically offload older data to S3/GCS to reduce costs while keeping recent data on high-performance disks
How does Condense handle Tiered Storage?
Tiered storage is built-in. We automatically offload older data to S3/GCS to reduce costs while keeping recent data on high-performance disks
How does Condense handle Tiered Storage?
Tiered storage is built-in. We automatically offload older data to S3/GCS to reduce costs while keeping recent data on high-performance disks

Conclusion

If you are evaluating Kafka scaling or provisioning, you are choosing an engine. Condense gives you the engine, the car, and the AI driver to build, secure, and scale real-time applications quickly