> ## Documentation Index
> Fetch the complete documentation index at: https://docs.condense.io/llms.txt
> Use this file to discover all available pages before exploring further.
# Redpanda
##### **Condense and Redpanda both handle GBps-level throughput, but Condense provides the complete application environment that Redpanda lacks. Redpanda is a storage engine that requires you to manage separate clusters like Flink or Spark to actually use your data. Condense solves the entire lifecycle integrating high-performance brokers with a native execution layer for your Java, Python, or Go code. It autonomously scales your brokers and your logic in lockstep, eliminating the need for separate processing infrastructure and the manual work of managing it.**
## **How does Condense compare against Redpanda?**
| **Feature** | **Condense** | **Redpanda** |
| :--------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| System Design | A unified environment that bundles Kafka, Kubernetes, and the App Runtime into one integrated stack. | Designed as a native C++ engine that consolidates the broker, proxy, and storage into a high-performance, JVM-free execution model. |
| Unified Platform | Unified Application Fabric: Merges the Kafka engine with a native event-driven microservice runtime. It is an "Execution Fabric." | Broker-Centric: A high-performance storage engine; requires external tools for logic. |
| Scaling Scope | Full-Stack Autonomy: Automatically scales brokers, connectors, and custom-code transforms (Java/Python/Go) based on real-time consumer lag. | Infrastructure-Centric and Broker-Level Scaling: Efficiently scales cluster nodes, but scaling the processing power for your code remains a manual or external engineering task |
| Cloud Storage | Native Object Storage: Direct offloading to S3/GCS/Azure Blob; data stays in your buckets for infinite retention. | Tiered Storage: Integrated S3 offloading; Requires Enterprise License for production. |
| Enterprise BYOC | 100% BYOC Native: Specifically engineered to run in your VPC to eliminate "SaaS" networking taxes and maintain 100% data sovereignty. | Flexible Managed/BYOC: Offers SaaS, Serverless, and BYOC; however, Azure BYOC typically requires more manual configuration. |
| Performance | Serverless Scale and Zero-Hop: Running GBps+ enterprise data workload and autonomous scale for spikes and variations. Application logic runs "local" to the broker, eliminating the network latency of external processors. | Yes (Ultra-Low Latency): C++ engine optimized for sub-millisecond tail latencies. |
| **Feature** | **Condense** | **Redpanda** |
| :----------------- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| Operational Effort | Zero-Ops Application: Eliminates "Microservice Sprawl." You simply write the logic; the platform autonomously runs, scales, and monitors the entire application pipeline. | Simplified Infrastructure: Removes Kafka complexity (no JVM/KRaft), but leaves the "Application Toil" like managing the external apps that process the data to your team. |
| Industry Readiness | Dual-Layer Ecosystem: Access a vast library of generic connectors alongside industry specific domain-aware transforms for Mobility, IoT, and FinTech (e.g., VIN parsing, Geofencing) out of the box. | Horizontal Generalist: Optimized for raw byte-moving. Any industry-specific schema or logic must be engineered from scratch by your team. |
| Apache Kafka API | 100% Native: Built on upstream Kafka 4.0/KRaft. | API Compatible: C++ reimplementation of the protocol; 100% API compatible. |
| Logic Execution | Managed App Runtime: Custom transforms run in a managed, serverless-style environment. Condense handles containerization, state management, and orchestration. | External Execution: Business logic is either limited to inline WASM for simple tasks or piped to external clusters (Flink/Spark/K8s) that you must manage. |
| Dev Experience | Integrated AI IDE: Build, test, and deploy production Java/Python logic natively. Includes Agentic AI for root-cause analysis and code generation. | WASM & Console: Inline transforms via WebAssembly for simple tasks. Excellent UI (Console) for topic management, but lacks integrated logic runtimes. |
| Observability | Inbuilt Full-Stack: Trace an event from ingestion through your custom code to the final sink in a single, unified dashboard. | Broker Observability: Strong metrics for cluster health and throughput, but application-level tracing usually requires external APM tools. |
| Flink Integration | Native Runtime Alternative: Replaces the need for Flink for most use cases. | External: Heavily relies on external Flink or Spark for stateful logic. |
| **Feature** | **Condense** | **Redpanda** |
| :------------------ | :--------------------------------------------------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| Access Experts 24/7 | Direct Engineering: Built in support widget to raise ticket. Condense offers support priority based TAT for resolution | Enterprise Support): Mature support model for 24/7 cluster operations. |
| Cloud Availability | AWS, GCP, Azure (Customer Subscription) | AWS, GCP (Azure limited in BYOC) |
| Security | Own Cloud -Sovereign: Inherits your IAM and security groups; 100% data localization. | Hardened: Strong security features and enterprise RBAC. |
| SLAs | 99.95% Full-Stack: Covers the brokers, the processing logic, and the connectors under one SLA. | 99.99% Broker: Industry-leading uptime for the ingestion engine. |
| Pricing Model / TCO | Compute-Based: Simple, predictable vCPU/hr billing. Zero "Connector " or per-partition fees. | Usage/Performance: Pricing scales with throughput and volume. While hardware efficient (fewer nodes), costs can fluctuate significantly with data spikes., but often requires Enterprise licenses for Tiered Storage or BYOC support. |
## **Why Switch to Condense?**
In a typical Redpanda architecture, scaling is restricted to the infrastructure. If your processing logic (WASM transforms or external apps) starts to lag during a traffic spike, you must manually intervene to adjust compute resources.
Condense introduces Autonomous Scaling for the entire pipeline. It monitors real-time consumer lag and throughput at the event level. When data volume surges, the platform automatically provisions compute for your custom Java, Python, or Go transforms. It ensures your processing power stays in sync with your data volume and automatically scales back down to optimize costs once the surge passes.
While Redpanda supports WebAssembly (WASM) for simple tasks like data filtering, complex stateful business logic often requires moving code to an external system.
Condense embeds a Full-Code IDE directly into the platform. Developers can write, test, and deploy production transforms in standard languages with built-in state management and version control. This environment is supported by an AI-assisted layer that handles rebalancing and root-cause analysis, significantly reducing the manual effort required to keep complex pipelines healthy.
Many managed services require data to leave your environment to reside in a vendor-owned account, which can complicate security audits and increase network costs.
Condense is built for Bring Your Own Cloud (BYOC). It deploys directly into your private VPC (AWS, Azure, or GCP), keeping 100% of your data within your control. This allows you to inherit your existing security policies and encryption keys while utilizing your existing cloud enterprise credits for the underlying compute and storage.
Redpanda is a horizontal tool designed for raw speed across any use case, meaning all industry-specific logic must be built from scratch.
Condense provides a Verticalized Ecosystem. It includes pre-built, domain-aware transforms for industries like Mobility (e.g., VIN parsing, trip decoding), IoT, and FinTech. By using these pre-tuned assets, teams can bypass months of custom development and move from prototype to production significantly faster.
Redpanda simplifies the broker by removing the JVM, but it remains a storage and messaging layer. To actually use the data, teams still face the overhead of managing external processing clusters, such as Flink or Kubernetes microservices.
Condense merges these layers. It provides a managed environment that hosts both the data and the business logic as a single entity. This allows developers to ship production-grade code directly on the stream without the burden of building and maintaining a separate processing tier.
## **Why is Condense the Best Way to Enable Agentic AI and Real-Time Data**
| **WITHOUT CONDENSE** | **THE CONDENSE WAY** |
| :----------------------------------------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Coding Connectors Requires specialized Java/Scala skills to write and maintain industry specific connectors | Universal & Industry-Ready Connectors Deploy universal or specialized connectors (e.g., Telematics for Mobility) that come with built-in parsing for complex schemas |
| Complex Management Development and maintenance of ever changing industry connectors becomes difficult | Configurable Output Sinks Configure and deploy pre-built sink/source connectors and through UI into the data pipeline |
| Maintenance & Scalability Managing scale and failover of connectors become a challenge as the load increases | |
| **WITHOUT CONDENSE** | **THE CONDENSE WAY** |
| :---------------------------------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Research & Glue Code Weeks spent finding libraries and writing boilerplate just to connect components | In-Built AI IDE & Git Sync Use purpose built AI agents to create, test, and build your custom transforms with GIT support. Publish them to be used in data pipeline directly |
| Maintenance & Scalability Multiple workflows and automations are required to manage availability and failover of stream processing applications | Native Stream Processing No external engine needed. Deploy your custom logic as reusable connectors or transforms that runs as containerized services |
| Disjointed Lifecycle Constant context switching between IDE, Git, Cloud Console, and CI/CD tools | Management & Scalability Complete lifecycle, versioning and scalability of services managed by Custom Transform Framework |
| **WITHOUT CONDENSE** | **THE CONDENSE WAY** |
| :--------------------------------------------------------------------------------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------- |
| Absence of Insights Creation of observability layer based on disjointed CLI tools, multiple available monitoring stacks and manual log aggregation | Native Dashboard Built-in visual pipeline view to see data moving in real-time, Check and act based on states of services, logs and configurations |
| Manual Tracking Manually monitoring usage, over-
provisioning, and under-utilization risks that impacts availability and increases operational cost | Comprehensive Observability Seamless integration with external tools industry accepted observability stacks for platform and cloud Ops |
| |
Purpose Built AI Agents
Autonomously checks system to generate actionable insights |
| **WITHOUT CONDENSE** | **THE CONDENSE WAY** |
| :------------------------------------------------------------------------------------------------------------------------------------------ | :----------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Complex Setup** Manual provisioning of Cloud compute resources and networking. Deployment of platform architecture for streaming usecases | Automated Provisioning Automated deployment of cloud resources and platform tailor-made for data streaming on your cloud subscription |
| Maintenance Nightmare Difficult to manage uptime between Infra and other system upgrades and cross dependencies | Fully Managed Maintenance All upgrades, patches, and downtime recovery are handled by the Condense team. User stays on a stable interface with 99.95% availability |
| Security Considerations Maintaining custom build governance workflows, cloud security and compliances becomes difficult over time | Security & Compliance Enterprise grade governance, audits, Information security and compliance certifications out-of-the-box |