Confluent VS Rivery

Confluent is the data streaming platform built around Apache Kafka. The company was started in 2014 by Jay Kreps, Neha Narkhede, and Jun Rao, the three LinkedIn engineers who created Kafka back in 2011. In December 2025, IBM announced an $11 billion all-cash acquisition of Confluent, and the deal closed on March 17, 2026. Confluent now operates as a wholly-owned IBM subsidiary inside IBM's software portfolio, alongside earlier IBM purchases like Red Hat and HashiCorp.

The product line is broader than most teams realize. Confluent Cloud is the fully managed SaaS offering, available on AWS, Azure, and Google Cloud. Confluent Platform is the self-managed version for teams that want to run Kafka on their own infrastructure. There is also Confluent Private Cloud, and WarpStream, an object-storage-based Kafka alternative that Confluent acquired in September 2024. WarpStream's diskless architecture is the cost-optimized path for storage-heavy workloads, while standard Confluent Cloud is the higher-performance path.

Around Kafka itself, Confluent ships a fairly complete ecosystem: Schema Registry for data governance, 120+ pre-built connectors, Confluent Cloud for Apache Flink for stream processing, and Tableflow for landing Kafka topics directly into Apache Iceberg or Delta Lake tables. ksqlDB is still available for SQL-based stream processing, though Flink has clearly become the primary engine on the platform. The customer base is large: more than 6,500 enterprises, including over 40% of the Fortune 500.

Recent product direction leans heavily into AI infrastructure. Confluent Intelligence, Streaming Agents, and the Real-Time Context Engine target agentic AI workflows where models and agents need continuously updated, governed context to operate on.

• Most complete Kafka ecosystem on the market. If you want Schema Registry, Flink, Tableflow for Iceberg, ksqlDB, and 120+ connectors from a single vendor, Confluent has the broadest set.
• Multiple deployment paths. Confluent Cloud (SaaS), Confluent Platform (self-managed), Confluent Private Cloud, and WarpStream's diskless architecture cover most preferences from fully managed to fully self-hosted.
• Managed Apache Flink. Confluent Cloud for Apache Flink supports Flink SQL plus Table API for Java and Python, with native integration to Schema Registry, Connectors, and Tableflow.
• WarpStream for high-volume retention. Object-storage architecture meaningfully reduces storage costs versus traditional Kafka brokers when retention windows are long.

• IBM acquisition uncertainty. The IBM deal only closed in March 2026, and customers are still watching how Confluent's pricing, packaging, and roadmap get integrated into IBM's broader portfolio. Worth weighing if long-term roadmap stability is critical to the buying decision.
• Pricing has a lot of dimensions. Charges land separately on data ingress, egress, storage, partitions, connectors, Schema Registry, Flink, and Tableflow. The model is transparent but not easy to forecast without modeling several dimensions at once, and bills tend to compound as throughput grows.
• Kafka itself is not simple. Even with the managed service, partitions, consumer groups, replication, schema evolution, and tuning are not things teams learn in a weekend. Teams new to Kafka usually spend real time on architecture and operational know-how.
• Lock-in once you are deep. Moving off Confluent Cloud to self-hosted Kafka or another managed provider means migrating topics, connectors, schemas, Flink jobs, and operational tooling. It is doable but it is not a small project.

Confluent Cloud uses usage-based pricing with separate dimensions for data ingress, egress, storage, partitions, connectors, and add-on services like Schema Registry, Flink, and Tableflow. There are multiple cluster types (Basic, Standard, Enterprise, Dedicated, Freight) at different price points. WarpStream uses its own pricing model based on its object-storage design, which often comes in lower for storage-heavy workloads. Confluent Platform is licensed separately for self-managed deployments. Small and mid-sized deployments are manageable, but at higher throughput or with several add-on services running, total cost tends to grow quickly and usually needs to be modeled across multiple dimensions before commit.

Rivery was founded in 2019. Since then it has grown to 100 people and 350+ customers. It’s a multi-tenant public cloud SaaS ELT platform. It has some ETL features, including inline Python transforms and reverse ETL. It supports workflows and can also load multiple destinations.

But Rivery is also similar to batch ELT. There are a few cases where Rivery is real-time at the source, such as with CDC, which is its own implementation. But even in that case it ends up being batch because it extracts to files and uses Kafka for file streaming to destinations which are then loaded in minimum intervals of 60, 15, and 5 minutes for the starter, professional, and enterprise plans.

If you’re looking for some ETL features and are OK with a public cloud-only option, Rivery is an option. It is less expensive than many ETL vendors, and also less expensive than Fivetran. But its pricing is medium-high for an ELT vendor.

Rivery's future offerings, policies, and pricing may be uncertain as they undergo an acquisition with Boomi.

• Modern data pipelines: Rivery is the one other modern data pipeline platform in this comparison along with Estuary.
• Transforms: You have an option of running Python (ETL) or SQL (ELT). You do need to make sure you use destination-specific SQL.
• Orchestration: Rivery lets you build workflows graphically.
• Reverse ETL: Rivery also supports reverse ETL.
• Load options: Rivery supports soft deletes (append only) and several update-in-place options including switch-merge (to merge updates from an existing table and switch), delete-merge (to delete older versions of rows), and a regular merge.
• Costs: Rivery is lower cost compared to other ETL vendors and Fivetran, though it is still higher than several ELT vendors.

• Batch only: While Rivery does extract from its CDC sources in real-time, which is the best approach, it does not support messaging sources or destinations, and only loads destinations in minimum intervals of 60 (Starter), 15 (Professional), or 5 (Enterprise) minutes.
• Data warehouse focus: While Rivery supports Postgres, Azure SQL, email, cloud storage, and a few other non data warehouse destinations, Rivery’s focus is data warehousing. It doesn’t support the other use cases as well.
• Public SaaS: Rivery is public cloud only. There is no private cloud or self-hosted option.
• Limited schema evolution: Rivery had good schema evolution support for its database sources. But the vast majority of its connectors are API-based, and those do not have good schema evolution support.

Rivery charges per credit, which is $0.75 for Starter, $1.25 for Professional, and negotiated for Enterprise. You pay 1 credit per 100MB of moved data from databases, and 1 credit per API call. There is no charge for connectors. If you have low data volumes this will work well. But by the time you’re moving 20GB per month it starts to get more expensive than some others.

Estuary is a managed data integration platform for CDC, streaming, and batch pipelines. It supports database CDC, SaaS and API ingestion, event streams, files, warehouses, data lakes, operational systems, and AI destinations, with delivery ranging from real-time streaming to scheduled batch intervals.

At the center of Estuary is its runtime, where captured data is written to durable collections and can be independently read by multiple destinations and transformations. This separates source capture from downstream delivery and allows data to be reused for additional destinations, backfills, and reprocessing without repeatedly extracting it from the source.

Estuary offers 200+ fully managed connectors, no-code capture and materialization setup through the web application, and streaming transformations using SQL, Python, and TypeScript. It also supports dbt Cloud, materialization triggers, Agent Skills for AI-assisted pipeline development, and Public, Private, and Bring Your Own Cloud (BYOC) deployment options.

• CDC, streaming, and batch in one platform: Estuary supports log-based CDC, SaaS and API ingestion, event streams, files, and scheduled batch movement for analytical and operational use cases.
• Real-time runtime with durable collections: Estuary's runtime separates captures from materializations through durable collections, allowing data to be reused for multiple destinations, transformations, backfills, and replay without re-extracting it from the source.
• Low-latency delivery: Supported real-time pipelines can deliver data with sub-second latency, while delivery can also be scheduled when lower freshness is sufficient.
• No-code setup with advanced transformation options: Pipelines can be configured through the web application, while SQL, Python, and TypeScript transformations, dbt Cloud, APIs, and flowctl support more advanced workflows.
• Built-in monitoring and AI-assisted operations: Estuary includes logs, latency and usage metrics, pipeline alerts, OpenMetrics integrations, and Agent Skills for AI-assisted pipeline development and troubleshooting.
• Flexible deployment and predictable pricing: Public, Private, and BYOC deployments provide different levels of infrastructure control, while pricing is based on data volume and prorated connector task hours rather than Monthly Active Rows.

• Smaller managed connector catalog than some large ELT platforms: Estuary offers 200+ fully managed connectors, but some larger platforms have broader packaged coverage, particularly for long-tail SaaS and legacy enterprise applications. Custom connector development is available with Enterprise.
• No visual transformation canvas: Pipeline setup is no-code, and fields can be selected or renamed in the UI, but more complex streaming transformations use SQL, Python, or TypeScript rather than a drag-and-drop transformation canvas.
• Advanced workflows have a learning curve: Basic capture and materialization pipelines can be built through the web application, but advanced derivations, schemas, CLI workflows, and GitOps-style deployments require more data engineering familiarity. Some Estuary users on G2 also mention a learning curve for more advanced configurations.

Estuary pricing is based on data volume moved and connector instance usage. Data movement is priced at $0.50 per GB. The first six connector instances are priced at $100 per month each, while additional connector instances are $50 per month each, with usage prorated based on active connector instances.

The permanent free tier includes up to 10 GB of data movement per month and two concurrent connector instances, while the 30-day trial provides access to the full Cloud plan.

Enterprise plans can include volume discounts, Private or BYOC deployments, custom SLAs, private networking, dedicated support, and custom connector development.

Because pricing is based on GB moved plus connector usage rather than row-change metrics such as MAR, costs are relatively predictable from data volume and connector usage.