Pricing that scales with you

Your monthly bill adds up data volume and connector instances:

• Data is counted each time it’s captured from a source and each time it’s delivered to a destination, including data produced by transformations.
• Each active source or destination is one connector instance, prorated by the hour.

On the Cloud plan, data costs $0.50 per GB, and connector instances cost $100 per month each for your first six and $50 per month for each one after that. For example, moving 100GB a month from Postgres to Snowflake counts as 200GB of data ($100) and 2 connector instances ($200), for a total of $300 a month. Enterprise plans apply discounted rates to the same measurement, and add private and BYOC deployment options for teams running at larger scale.

A connector instance is one configured source (a capture) or one configured destination (a materialization). A single capture can pull from many tables and still counts as one instance, so a pipeline from a Postgres database with 500 tables to Snowflake uses 2 connector instances. Transformations (derivations) don’t count as connector instances, but the data they produce does count toward your data volume.

Data volume includes data captured from sources, data produced by transformations, and data delivered to destinations. If you’re on the Cloud trial, all usage is free for 30 days, which makes it a good time to run your initial load. Planning a large historical load after your trial ends? Get in touch before you start and we’ll help you plan it.

Yes. Estuary’s free tier lets you run up to 10 GB per month across 2 connector instances indefinitely, with no credit card. If you go over those limits, a 30-day free trial of the Cloud plan starts automatically, giving you access to every Cloud feature. We’ll email you before your trial ends. To keep your pipelines running on Cloud, just add a payment method. If you don’t plan to continue, disable the connector instances you no longer need so they don’t generate charges.

With pay-as-you-go, you pay monthly for what you actually use, by card or bank account through Stripe. There’s no minimum commitment, and you can stop anytime. With an annual plan, you commit to a set amount of monthly usage upfront at a discounted rate. Usage above that amount is billed as overage at your contracted rate. Larger commitments and longer terms get deeper discounts.

Enterprise pricing is custom and based on your data volume, number of connector instances, deployment model, contract length, and support needs. Volume-based discounts are available for committed usage. Private and Bring Your Own Cloud (BYOC) deployments are available on annual or multi-year Enterprise contracts. Contact sales for a quote.

Fivetran bills by monthly active rows (MAR), so your costs depend on how many rows change and can be hard to predict. Estuary bills by GB moved plus a flat fee per connector instance, so costs scale with data volume and are easy to estimate upfront. Estuary also stores captured data in your own cloud storage, which means adding a new destination doesn’t require re-extracting data from the source. For a full breakdown, see Estuary vs. Fivetran.

You can sign up for free and build your first pipeline in minutes using our no-code interface. Simply connect a source and destination, and Estuary will guide you through capturing and syncing your data in real time. Learn here to create a basic data flow.

Estuary is a Right-Time Data Platform that unifies real-time streaming and batch movement in one system. Unlike ETL tools that focus only on batch jobs, Estuary lets you choose when data moves, instantly or on schedule, with predictable costs and enterprise reliability. See the difference in our detailed comparisons.

For side-by-side breakdowns with popular vendors, check out:

• Estuary vs Fivetran
• Estuary vs Airbyte

Yes. Estuary offers a free tier that includes a generous data volume limit so you can try real-time pipelines without entering a credit card. If you exceed the free tier limits, you can continue to use Estuary for a 30-day free trial. If you need more time or volume during evaluation, just contact us and we’ll help.

Estuary offers 200+ source and destination connectors across databases, cloud storage, APIs, SaaS apps, and streaming platforms. You can view the full connector list here. Don’t see what you need? Reach out — we prioritize connectors based on customer demand.

If we don’t currently support a specific source, you can request it through our support form. You can also use our webhook, file drop (CSV, JSON), or custom HTTP connector to ingest data from virtually any source.

Estuary uses change data capture (CDC) to stream inserts, updates, and deletes from supported databases. For APIs, we pull data incrementally or via webhooks. All changes are written to a real-time collection and delivered downstream continuously.

Estuary syncs data according to a sync schedule you set for your destination. This can be real-time millisecond latency or a longer interval to balance data freshness with compute costs in your destination systems.

Yes. When a pipeline starts, Estuary performs a historical backfill to capture your existing data, then seamlessly switches to real-time CDC. Backfilling is configurable and can be disabled or scoped for large datasets.

You can view pipeline status, latency, and metrics in Estuary’s dashboard. For advanced monitoring, we expose metrics via an OpenMetrics API, which integrates with Prometheus, Datadog, and other observability tools.

Yes, Estuary supports Snowpipe Streaming as a destination, allowing you to continuously load real-time data into Snowflake with low latency. It handles schema evolution, exactly-once semantics, and backfill logic automatically so you don’t have to manage Snowpipe infrastructure yourself.

No. Estuary uses log-based CDC, which reads from database logs rather than querying tables, minimizing impact on production systems.

Yes. Estuary supports real-time transformations using Python, SQL, or TypeScript. You can filter, enrich, join, and reshape data in motion. Or ditch the code and rename or remove fields directly through the dashboard.

Yes. Estuary offers a dbt Cloud integration. Trigger dbt jobs automatically whenever there’s new data available.

A derivation is a transformation task that creates a new collection based on one or more input collections. It runs continuously and updates automatically as source data changes, supporting real-time use cases.

Yes. You can preview and validate derivations in the UI or CLI using sample data. This makes it easy to catch issues and ensure accuracy before publishing to production.

Estuary provides intelligent schema evolution for durable pipelines with auto-discovery. You can automatically discover new fields, changed data types, and even entirely new data streams. Or you can choose to manually make updates for tight control over your data ecosystem.

Estuary is SOC 2 Type II certified with zero exceptions and HIPAA-compliant. This means our infrastructure and operational controls are independently audited and validated to meet strict industry standards. Learn more here.

Data is stored in your own cloud account. Whether you’re using SaaS or a Private Deployment, you can configure your own cloud storage bucket. The platform supports zero data egress from your network.

Private Deployments let you run the data plane inside your own VPC or cloud region, while the SaaS control plane manages configuration. Your data never leaves your network, and you retain full control over infrastructure, security, and governance.

Yes. Estuary supports AWS PrivateLink and similar services (Azure Private Link, Google Private Service Connect), VPC peering, and SSH tunnels, enabling secure connections to firewalled databases and private cloud services. You can deploy the data plane in any region while maintaining full network isolation.

All communications use TLS, including within cloud environments. Internal services authenticate with mutual TLS (mTLS) using a custom certificate authority per deployment. Server-facing services use Let’s Encrypt, and all data at rest is encrypted with AES-256.

Yes. Estuary uses RBAC (Role-Based Access Control) and tokenized cross-deployment access. The control plane handles authorization and generates traceable, time-limited tokens, ensuring secure access and full auditability across data planes.

Yes. Platform-level logs are shipped from each data plane to a centralized logging system, and task-level logs are accessible via the Estuary interface. This enables both internal security monitoring and user visibility.

Yes. Estuary’s architecture supports multi-region data plane mobility without relying on specific VPC constructs. You can maintain consistent security policies across geographies while ensuring data residency compliance.

Estuary pricing is volume and connector-based. You’re billed for the amount of data synced across your pipelines, measured in GBs, plus a flat rate per month based on your number of active connector instances. This makes pricing predictable and scalable. View pricing here.

You will automatically start your 30-day free trial and can upgrade your account at any time. If you go over the free tier limits, you’ll receive a warning and can choose to continue with usage-based billing or pause pipelines.

You can reach out through our Slack, or email us at support@estuary.dev. We also provide enterprise-grade support with SLAs on paid plans.

All product documentation is available at docs.estuary.dev, including setup guides, pipeline examples, and API references.

Estuary delivers data in under 100 milliseconds end-to-end, allowing downstream systems like analytics dashboards, operational databases, and AI models to act on fresh data instantly.

Traditional streaming requires managing infrastructure like Kafka and custom pipelines. Estuary provides a fully managed platform that handles ingestion, delivery, and recovery without operational overhead.

Yes. Estuary lets you configure delivery latency per destination, so you can run sub-second pipelines for critical systems and slower delivery for cost-sensitive workloads.

No. Estuary guarantees exactly-once delivery and deterministic recovery, ensuring accuracy and consistency even in real-time pipelines.

Real-time analytics dashboards, fraud detection systems, operational monitoring, and AI inference pipelines all benefit from continuously fresh data.

Batch is ideal for scheduled workflows like nightly reporting, large-scale data migrations, and cost-optimized analytics pipelines where instant updates aren’t required.

Yes. Estuary supports deterministic backfills and replay, allowing you to reprocess historical data without re-extracting from source systems.

Through exactly-once delivery and checkpointing, ensuring no duplication or data loss even during retries or failures.

Yes. Estuary is built for “right-time data,” meaning you can run both batch and real-time pipelines in the same platform without duplication or reconfiguration.

Estuary eliminates fragmented pipelines by unifying batch and real-time processing, reducing operational complexity and improving reliability.

Change data capture (CDC) captures inserts, updates, and deletes from production databases and continuously delivers those changes to downstream systems.

Estuary supports major databases including Postgres, MySQL, MongoDB, Oracle, and SQL Server, among others.

Yes. Estuary allows one-to-many replication, so a single source can feed warehouses, search systems, microservices, and AI pipelines simultaneously.

Pipelines using schema evolution will incorporate schema changes, such as new fields, updated data types, and new data streams, automatically. Or you can choose to manually make updates for tight control over your data ecosystem.

You can filter, join, aggregate, normalize, and enrich data using SQL, Python, or TypeScript before it reaches downstream systems.

Transforming data in motion ensures that every downstream system receives clean, consistent, and analytics-ready data without additional processing layers.

Yes. Estuary allows you to join live streams with historical datasets to create richer, more complete data models.

Built-in validation and QA mechanisms detect anomalies, enforce schemas, and route errors automatically as data flows through pipelines.

In many cases, yes. Estuary reduces the need for downstream transformation layers by delivering production-ready data directly to warehouses and applications.

AI-ready data is clean, structured, enriched, and continuously updated so models can operate on accurate and current inputs.

Estuary continuously captures, transforms, and delivers data to vector databases, feature stores, and model pipelines with low latency and high reliability.

Yes. Estuary keeps embeddings and underlying data sources in sync, ensuring retrieval systems always reflect the latest information.

By delivering fresh, high-quality data, Estuary reduces drift, improves inference accuracy, and enables faster iteration cycles.

No. Estuary unifies operational, analytical, and AI pipelines into a single platform, reducing duplication and complexity.

Agent Skills work with Claude Code, Cursor, OpenAI Codex, GitHub Copilot, Gemini CLI, and any other tool that supports the open SKILL.md standard. Claude Code users can install skills through the built-in plugin marketplace, and other tools can use the cross-tool Skills CLI or copy skills directly from the open-source estuary/agent-skills repository on GitHub.

All of them. Dedicated skills cover the most common sources, including PostgreSQL, MySQL, MongoDB, and SQL Server CDC, plus HTTP webhook captures for tools like Shopify, Stripe, and GitHub. On the destination side, there are dedicated skills for Snowflake, BigQuery, Redshift, Databricks, and PostgreSQL. Beyond those, generic capture and materialization skills can configure any of Estuary’s 200+ connectors through dynamic schema discovery.

They complement each other. Estuary’s MCP integration gives your assistant context about Estuary, so it can answer questions about the platform. Agent Skills take action: they provide a set of steps for the agent to run real Estuary workflows, from drafting and publishing pipeline configuration to checking task health, inspecting logs, and restarting connectors. Use MCP to learn about Estuary and Agent Skills to build and operate with it.

No. The agent drafts pipeline configuration as code that you review before anything is published. You approve the change, version it like any other piece of infrastructure, and deploy it through your normal workflow. Every change the agent suggests stays reviewable, auditable, and reversible.

Yes. Pipelines built through Agent Skills run on the same Estuary runtime as every other pipeline, with the same exactly-once delivery and schema evolution, using the same log-based CDC and optimized connectors. There is no separate agent mode with different reliability guarantees, and Estuary’s security and compliance posture applies to every pipeline regardless of how it was created.

Create a free Estuary account, install the flowctl CLI, and add the skills to your assistant. Claude Code users can run /plugin marketplace add estuary/agent-skills, and other tools can use npx skills add estuary/agent-skills. From there, ask your assistant something like “capture my Postgres database into Estuary” and it will guide you through setup.