| Deployment Model | Deployed entirely in the customer’s cloud (AWS/Azure/GCP). Full control, internal network connectivity, and native integration with existing security policies | Runs only on Confluent-managed infrastructure. BYOC and private connectivity options remain limited |
| Dev Experience | Integrated, AI-assisted IDE to build, test, and deploy stream logic; outputs reusable transforms and connectors with single-click publishing | Requires external IDEs or KSQL/Flink setup; no unified development or deployment workflow |
| NCLC Utilities | Provides built-in low-code components—split, filter, alerting, windowing, and aggregation—for composing logic directly in pipelines | Relies on KSQL or Flink for stream operations; higher setup and code overhead. |
| Industry Ecosystem | Ships with domain-optimized connectors and transforms for verticals such as Mobility, Industrial IoT, and FinTech. End-to-end verticalized ecosystem aligned with domain | Offers generic Kafka connectors; lacks industry-specific prebuilt assets. Broad horizontal architecture; users must customize domain logic manually |
| Observability | Native observability layer for metrics, logs, and traces; extensible to external APM tools (Prometheus, Grafana, Datadog) | Provides Control Center monitoring; deeper observability requires external integrations. |
| Security | Enterprise-grade: OAuth2, fine-grained RBAC | OAuth and API key-based access; RBAC and directory integration |
| Pricing Model | Simple vCPU/hr billing. No per-connector, partition, or throughput cost; simple and predictable, pay purely for compute consumed | Multi-dimensional pricing based on data volume, partitions, connectors, and storage; opaque for large-scale workloads |
| Latency & Performance | Consistent millisecond-level Kafka-standard latency (10–20 ms typical) | Kafka-standard latency (10–20 ms typical); network and connector overhead add variability |
| Ops Overhead | Unified streaming platform. Operations include Kafka plus transforms, deployment, and observability, all managed from one control plane. Simplifies end-to-end lifecycle management. | Focused on Kafka infrastructure; managing transforms, scaling, and monitoring requires separate tools and configuration |
