AWS Kinesis excels as a tightly integrated, serverless streaming service for AWS-native workloads, while Confluent delivers a more complete data streaming platform with broader deployment flexibility, richer governance, and a massive connector ecosystem built on Apache Kafka.
| Feature | AWS Kinesis | Confluent |
|---|---|---|
| Architecture | Proprietary AWS service with shard-based throughput model and serverless infrastructure | Cloud-native platform built on Apache Kafka with Kora engine and autoscaling clusters |
| Pricing Model | Usage-based pricing starting at $0.08 per GB of data ingested, with example monthly charges of $593.04 for 7,413.12 GB of data ingested. Additional pricing tiers include $0.04, $0.03, and $0.01 per GB/month as shown in the sources. | Basic $0/mo, Standard $385/mo, Enterprise $895/mo, Freight $2,300/mo with usage-based rates starting at $0.01 |
| Ecosystem & Integrations | Deep AWS-native integrations with Lambda, S3, Redshift, Firehose, and CloudWatch monitoring | 120+ pre-built connectors, Schema Registry, Apache Flink, ksqlDB, and multi-cloud deployment options |
| Scalability | Scales by adding shards; each shard handles 1 MB/s ingestion and 2 MB/s reads | Autoscaling clusters up to 9,120/27,360 MBps ingress/egress on Freight tier with infinite storage |
| Deployment Flexibility | AWS-only fully managed service with no self-hosted or multi-cloud deployment option | Available as Confluent Cloud, self-managed Confluent Platform, or on Kubernetes across any environment |
| Data Governance | Server-side encryption via AWS KMS, IAM-based access control, and CloudWatch metrics | Schema Registry for data contracts, role-based access control, OAuth/OIDC, and TLS/mTLS encryption |
| Feature | AWS Kinesis | Confluent |
|---|---|---|
| Core Streaming | ||
| Real-Time Data Ingestion | — | — |
| Stream Processing Engine | — | — |
| Record Aggregation & Batching | — | — |
| Scalability & Performance | ||
| Autoscaling Capacity | — | — |
| Multi-Region Replication | — | — |
| Tiered Storage | — | — |
| Integration & Connectivity | ||
| 120+ Pre-Built Connectors | — | — |
| Native Cloud Provider Integrations | — | — |
| Apache Flink Support | — | — |
| Security & Governance | ||
| Schema Registry | — | — |
| Server-Side Encryption | — | — |
| Role-Based Access Control | — | — |
| Deployment & Operations | ||
| Self-Managed Deployment Option | — | — |
| Kubernetes Orchestration | — | — |
| Serverless Operation | — | — |
Real-Time Data Ingestion
Stream Processing Engine
Record Aggregation & Batching
Autoscaling Capacity
Multi-Region Replication
Tiered Storage
120+ Pre-Built Connectors
Native Cloud Provider Integrations
Apache Flink Support
Schema Registry
Server-Side Encryption
Role-Based Access Control
Self-Managed Deployment Option
Kubernetes Orchestration
Serverless Operation
AWS Kinesis excels as a tightly integrated, serverless streaming service for AWS-native workloads, while Confluent delivers a more complete data streaming platform with broader deployment flexibility, richer governance, and a massive connector ecosystem built on Apache Kafka.
Choose AWS Kinesis if:
Choose AWS Kinesis if your organization is fully committed to the AWS ecosystem and needs a serverless, low-maintenance streaming solution. Kinesis eliminates operational overhead with its fully managed shard-based model, integrates natively with Lambda, S3, Redshift, and Firehose, and works well for use cases like log analytics, IoT data ingestion, and real-time application monitoring where deep AWS integration matters more than multi-cloud portability.
Choose Confluent if:
Choose Confluent if you need a comprehensive data streaming platform that works across hybrid and multi-cloud environments. Confluent provides 120+ pre-built connectors, Schema Registry for data governance, Apache Flink for stream processing, and deployment options spanning fully managed cloud, self-managed on-premises, and Kubernetes. It is the stronger choice for organizations building event-driven architectures, requiring data contracts and schema evolution, or operating across multiple cloud providers.
This verdict is based on general use cases. Your specific requirements, existing tech stack, and team expertise should guide your final decision.
AWS Kinesis uses pure usage-based pricing with two capacity modes: provisioned at $0.015/shard-hour (roughly $10.95/mo per shard) and on-demand at $0.04/GB ingested. Additional costs include PUT payload units at $0.014 per million, enhanced fan-out at $0.013/GB, and extended retention fees. Confluent Cloud offers tiered cluster types starting with Basic at no monthly cost, Standard at $385/Mo, Enterprise at $895/Mo, and Freight at $2,300/Mo, with separate usage-based charges for data ingress, egress, storage, and connectors. For predictable workloads under 500 GB/day, Kinesis provisioned mode tends to be cheaper; for large-scale streaming with complex integration needs, Confluent pricing depends heavily on the cluster tier and connector usage.
Migrating between these platforms requires significant architectural changes because they use fundamentally different APIs and data models. AWS Kinesis uses a proprietary API with shards, partition keys, and sequence numbers, while Confluent is built on Apache Kafka with topics, partitions, and consumer groups. There is no direct migration path or API compatibility layer between them. Teams typically need to build a bridge layer that consumes from one platform and produces to the other, update all producer and consumer applications to use the new SDK, and reconfigure downstream integrations. Confluent does offer Kafka Connect connectors for AWS services, which can ease the integration but not eliminate the migration effort.
Both platforms support real-time AI and ML pipelines, but they approach it differently. AWS Kinesis integrates natively with SageMaker, Lambda, and Kinesis Data Analytics (powered by Apache Flink) for building ML inference pipelines within the AWS ecosystem. Confluent positions itself as an AI-ready data foundation, streaming live operational events to AI models and agents with governance controls. Following IBM's acquisition of Confluent in March 2026, the platform now integrates with IBM watsonx.data for enterprise AI workflows. Confluent's Schema Registry ensures data quality and schema evolution for ML feature stores, while its 120+ connectors make it easier to feed diverse data sources into training and inference pipelines across multiple cloud environments.
IBM completed its acquisition of Confluent on March 17, 2026 in an all-cash transaction valued at $31.00 per share, representing an enterprise value of approximately $11 billion. Confluent was delisted from the Nasdaq Stock Market and absorbed into IBM's Data and AI division. IBM has announced immediate integrations with watsonx.data, IBM MQ, IBM webMethods Hybrid Integration, and IBM Z. The acquisition positions Confluent as a core component of IBM's strategy for agentic AI and real-time enterprise data. For existing Confluent users, IBM has committed to maintaining the platform's open-ecosystem approach, though deeper integrations with IBM's proprietary software are expected to be prioritized over time.