A fully managed data streaming platform, available on AWS, Google Cloud, and Azure, with a cloud-native Apache Kafka® engine for elastic scaling, enterprise-grade security, stream processing, and governance.
View Confluent Cloud release notes
Represent Kafka topics as open table formats like Apache Iceberg™ and Delta Lake.
Use AI functions, interfaces, and tools to build agentic workflows, query real-time data, manage Confluent Cloud resources, and build streaming applications.
Manage and monitor both self-managed Confluent Platform and fully managed Confluent Cloud clusters from a single interface.
Use connectors to stream data between Kafka and external systems.
Store and retrieve Avro®, JSON Schema, and Protobuf schemas in a centralized repository to enforce data quality, ensure compliance, and manage schema evolution.
Build and run complex, stateful, low-latency streaming applications at scale with Apache Flink.
Produce and consume Kafka messages quickly using official Confluent clients for Java, librdkafka, and derived libraries.
Control Kafka resources, security, and account settings using the Confluent command-line interface.
Use HashiCorp Terraform to deploy and manage your Confluent Cloud infrastructure.
Visualize event streams and data relationships to track data movement from source to destination.
Manage, configure, and automate your Confluent Cloud deployment using REST APIs.
Deploy a fully managed event streaming service built specifically for U.S. government workloads.
Deploy and scale a fully managed, elastic cloud Kafka service built on the Kora engine.
Stream and store real-time data using the open-source distributed event engine at the core of Confluent.
Run stateless, S3-backed Kafka workloads with zero disk footprint on brokers.
Provision clusters across flexible tiers tailored to your scaling, isolation, and pricing requirements.
Create, configure, and manage topics, partitions, and data records.
Track consumer group membership and manage committed read offsets.
Execute stateful, low-latency stream processing workloads on a fully managed Flink service.
Write and deploy real-time streaming queries and data pipelines using standard ANSI SQL.
Build stateful stream processing applications directly in Java.
Process and query streaming data in real time using SQL directly against Kafka topics.
Stream data between Kafka and external systems using managed or self-managed infrastructure.
Integrate with databases, cloud storage, SaaS applications, and messaging systems.
Upload and run custom connector plugins directly within Confluent Cloud.
Centralize, version, and enforce schema compatibility across your data streams.
Ensure data quality, maintain metadata catalogs, and trace lineage across all event streams.
Visualize end-to-end data flow across topics, connectors, and streaming applications.
Discover, search, and access event streams through a self-service portal.
Enforce schema rules, metadata standards, and quality guarantees on topic data.
Expose Kafka topics as Apache Iceberg or Delta Lake tables for open analytical processing.
Power real-time, context-aware AI applications with built-in streaming intelligence features.
Build event-driven AI agents that analyze and act on streaming data in real time.
Get assistance, execute queries, and complete guided tasks using an in-product AI assistant.
Detect sentiment, anomalies, and PII, or run forecasts directly within streaming SQL queries.
Call remote AI models and generate vector embeddings directly from streaming SQL.
Serve fresh, real-time context to AI models and downstream applications.
Query external vector and text stores directly within streaming SQL pipelines.
Expose Confluent data and actions to external AI tools using Model Context Protocol (MCP).
Mirror topics across clusters and regions without deploying extra infrastructure.
Authenticate and verify user and service identities using SSO, SAML, OAuth/OIDC, SASL, or mTLS.
Grant precise access permissions using role-based scopes across specific resources.
Define fine-grained allow rules per principal and resource.
Restrict cluster access to authorized source IP address ranges.
Encrypt all client and broker network traffic over the wire.
Encrypt stored cluster data using your own customer-managed encryption keys.
Encrypt sensitive fields on the client side before transmitting data to brokers.
Track, log, and review authentication and access events for security compliance.
Deploy FedRAMP-aligned event streaming for secure U.S. government workloads.
Connect to clusters securely over public internet endpoints.
Establish private, one-way network connections to clusters on AWS.
Establish private network connectivity to clusters on Microsoft Azure.
Establish private network connectivity to clusters on Google Cloud.
Route private network traffic between your VPC and Confluent infrastructure.
Configure custom DNS domain names and isolate network segments.
Secure and govern private access to Confluent services.
Administer Confluent Cloud and Platform resources directly from your terminal.
Manage Confluent Cloud resources, configurations, and settings through a web UI.
Manage hybrid cloud and self-managed clusters from a single control plane.
Provision and manage Confluent Cloud resources as code using Terraform.
Provision and manage Confluent Cloud resources using Pulumi infrastructure as code.
Track, allocate, and manage Confluent Cloud spend across teams and environments.
Monitor and manage resource limits and service quotas across Confluent Cloud.
Configure, route, and manage alerts and notifications for account events.
Monitor consumer lag and track processing delays across topics.
Export cluster metrics and logs to external monitoring tools like Datadog and Prometheus.
Manage Confluent Cloud infrastructure programmatically over HTTP.
Produce records and administer Kafka topics programmatically over REST.
Configure, monitor, and manage Kafka connectors programmatically over REST.
Query cluster health, performance, and resource metrics programmatically.
Fetch Confluent metadata and resource details using GraphQL queries.
Develop, test, and manage Kafka resources directly inside JetBrains IDEs.
Manage topics, schemas, and connectors directly from Visual Studio Code.
Produce and consume Kafka events in native Java applications.
Build high-performance C/C++ producers and consumers or power derived language libraries.
Produce and consume Kafka events using native Python code.
Build fast, concurrent Kafka producers and consumers in Go.
Integrate Kafka messaging directly into .NET and C# applications.
Produce and consume Kafka events in Node.js and JavaScript applications.
