Estuary is a right-time data platform that unifies log-based CDC, streaming and batch ETL in one event-driven architecture, moving data from 150+ sources to 60+ destinations with sub-second latency.
Most data stacks force a bad choice: batch ETL that is hours stale, or a Kafka-based streaming platform your team must babysit forever. Estuary’s Flow runtime takes a third path. Captures backfill your full history, then read every insert, update and delete from the source log as it happens; data lands as schema-enforced collections in your own cloud storage; and materializations sync each destination on its own schedule — real-time, hourly, or anything between — with append-only or merge semantics.
The catalog covers the systems that matter: PostgreSQL, MySQL, SQL Server, MongoDB and Oracle for databases; Salesforce, HubSpot, NetSuite and Stripe for SaaS; Kafka, Kinesis and Pub/Sub for streams; and destinations from Snowflake, BigQuery and Databricks to Elasticsearch, vector databases and reverse-ETL syncs back into operational tools. Deployment is flexible too — public cloud, a dedicated private data plane, or BYOC inside your own account. Newer capabilities target AI workloads directly: agents connected through MCP can inspect, observe and build pipelines, and in-pipeline derivations can classify, enrich and embed records as data arrives.
Estuary offers a 30-day free trial with no credit card required. Paid plans are usage-based, priced on the volume of data your pipelines move rather than per-connector or per-seat fees, and scale from a first table to petabyte workloads. Private deployment and BYOC options are quoted for teams with isolation or data-residency requirements.
Data teams that need fresh data in warehouses, apps and AI systems without operating streaming infrastructure themselves. Organizations with purely batch, once-a-day reporting needs may find a simpler scheduler-based tool cheaper.