Fivetran VS Informatica

Fivetran was founded in 2012 by data scientists who wanted an integrated stack to capture and analyze data. The name was a play on Fortran and meant to refer to a programming language for big data. After a few years the focus shifted to providing just the data integration part because that’s what so many prospects wanted. Fivetran was designed as an ELT (Extract, Load, and Transform) architecture because in data science you might not yet know how you want to process your data, and so would want to store the raw data. 

In 2018, Fivetran raised their series A, and then added more transformation capabilities in 2020 when it released Data Build Tool (dbt) support. That year Fivetran also started to support CDC. Fivetran has since continued to invest more in CDC with its HVR acquisition.

Fivetran’s design worked well for many companies adopting cloud data warehouses starting a decade ago. While all ETL vendors also supported “EL” and it was occasionally used that way, Fivetran was cloud-native, which helped make it much easier to use. The “EL” is mostly configured, not coded, and users can choose between dbt Core, dbt Cloud, and Coalesce for transformations.

When deciding whether to use Fivetran, special consideration should be made for the desired deployment option. Fivetran’s cloud-based SaaS and self-hosted HVR can be very different products, with diverging capabilities requiring separate documentation tabs. HVR, unlike Fivetran’s standard batch model, can handle real-time streaming data, but must be managed and maintained, incurring higher engineering effort. HVR also only lists ~30 supported source and target locations.

• Ease of Use: Fivetran is a modern SaaS ELT platform with an easy-to-use UI, especially in comparison to more traditional ETL tools. It allows you to set up a data pipeline without coding.
• Pre-built Connectors: Fivetran offers over 700 connectors, more than 450 of which are API-based “lite” connectors built for specific use cases. Lite connectors tend to offer limited endpoints and generally don’t support custom data.
• Scalability: Fivetran is known for scaling better than many of its competitors.
• Integration with dbt: Fivetran supports dbt Core and dbt Cloud as well as an integration with Coalesce.
• Focus on replication: Fivetran is good at data extraction and loading (EL), even if it is batch only, making it a strong choice if your primary goal is to efficiently move data into your warehouse for analysis.
• Advanced schema evolution: Fivetran and Estuary are the two leading vendors with support for automating how changes in sources are passed through to destinations.

• Latency: While Fivetran uses change data capture at the source, its primary focus is on batch data rather than streaming. Fivetran’s Standard tier guarantees 15 minutes of latency. Enterprise and Business Critical is 1 minute of latency, but costs more than 2x the standard edition. Its ELT architecture can also be slowed down by the target load and transformation times. 
  Fivetran’s HVR option supports real-time data, but as it is self-hosted rather than managed, it comes with its own complexity around setup and maintenance.
• Costs: Fivetran’s high vendor costs can become an issue, as they have been 5x the cost of Estuary as stated by customers. Fivetran costs are based on monthly active rows (MAR), or the number of rows updated or added within a month. This may seem like a transparent pricing model, but for several reasons (see below and the pricing section) it can quickly add up.
• Unpredictable costs: One major reason for high costs is that MARs are based on Fivetranʼs internal representation of rows, not rows as you see them in the source. This can make the MAR model opaque and difficult to properly budget. 
  Fivetran has also been known to change their pricing with little notice, causing companies to swallow sudden price hikes or scramble to find different vendors.
• Reliability: Another complaint against Fivetran is reliability. Their status page commonly reports 15 or more incidents per month, a combination of API outages, connector failures, and other degraded functionality. In the past, Fivetran has also been known to experience outages lasting more than 2 days, breaking listed SLAs.
  Make sure you understand Fivetran’s current SLA in detail. Fivetran has had an “allowed downtime interval” of 12 hours before downtime SLAs start to go into effect on the downtime of connectors. They also do not include any downtime from their cloud provider.
• Deployment options: Besides the public SaaS option, self-hosted HVR has some serious limitations. It requires installation effort and only supports a handful of sources and destinations.
• Support: Customers also complain about Fivetran support being slow to respond. Combined with reliability issues, this can lead to a substantial amount of data engineering time being lost to troubleshooting and administration.
• DataOps: Fivetran does not provide much control or transparency into what they do with data and schema: they alter field names and change data structures and do not allow you to rename columns. This can make it harder to migrate to other technologies.
• Roadmap: Future features and their timelines can be somewhat opaque. While Fivetran provides an SLA for some Lite connectors through their By Request program, wait time on requested features can otherwise be long. For example, Amazon S3 support was only released in 2023 after 2+ years of development and many user requests.

Fivetran's pricing is based on monthly active rows (MAR). This can be very unpredictable because MARs are based on Fivetran’s internal representation of data, not yours. Any non-relational or nested data gets turned into highly normalized rows that raise costs.

Lower latency is also very expensive. To reduce latency from 1 hour to 15 minutes can cost you 33-50% more (1.5x) per million MAR, and 100% (2x) or more to reduce latency to 1 minute, which is rarely deployed. Some connectors require all data to be extracted each time, which also becomes more expensive as you lower latency and increase the number of extracts.

Even then, you still have the latency of the data warehouse load and transformations. The additional costs of frequent ingestions and transformations in the data warehouse can also be expensive and take time. Companies often keep latency high to save money.

While a small deployment (2M MARs/month) can cost $700-$2667, 10M MARs/month get you into $10K a month. It is not unheard of for Fivetran costs to reach 6 digits annually, especially with certain high-cost connectors that end up having many more MARs.

For those looking for Fivetran alternatives, it's worth considering solutions that offer lower costs, real-time streaming, or more flexibility in schema control.

Informatica is one of the oldest names in data integration. The company was founded in 1993 and built its early reputation around PowerCenter, which became the default enterprise ETL platform for two decades. Over time, Informatica expanded well beyond ETL into a much broader portfolio covering data quality, MDM, data governance, and security.

Informatica is now part of Salesforce. Salesforce announced an $8 billion acquisition in May 2025 and closed it on November 18, 2025. Today Informatica operates inside Salesforce as the data foundation underneath Salesforce Data Cloud and the Agentforce agentic AI platform, with the Intelligent Data Management Cloud (IDMC) as the current flagship product.

Informatica is the textbook example of a mature, enterprise-grade data integration platform. It has one of the broadest data integration feature sets in the market and one of the better private cloud architectures, but it is also harder to use and more expensive than most modern SaaS ELT tools, and it was not built around DataOps the way newer platforms were. The trade-off is well understood: customers who pick Informatica are usually larger enterprises with dedicated data integration teams, complex governance and quality requirements, and a strong preference for a single vendor across data integration, MDM, quality, privacy, and cataloging.

• A full data management platform, not just ETL. IDMC covers data integration, replication, data quality, master data management, data cataloging, data privacy, and data governance under one platform. CLAIRE, Informatica's AI engine, runs across these to automate matching, classification, and lineage.
• Rich data integration capabilities built over 30+ years. Decades of work has gone into the data integration runtime, with deep support for complex transformations, push-down optimization, pipeline partitioning, and large enterprise patterns that newer vendors are still building toward.
• 300+ connectors. Strong coverage across cloud and on-premises data warehouses, enterprise applications (SAP, Oracle, Workday, Salesforce), mainframe sources, and modern lakehouse engines.
• Performance and scalability at the high end. Informatica is engineered for large-volume, low-latency pipelines and has supported serverless compute, pipeline partitioning, and push-down optimization for years.
• Private cloud architecture. Informatica is one of the few vendors that supports a private data plane managed by a shared SaaS control plane, which is meaningful for regulated industries with data residency constraints.
• Now part of Salesforce. Since the acquisition closed in November 2025, Informatica has been positioned as the data foundation underneath Salesforce Data Cloud and Agentforce. Customers already standardized on Salesforce can expect tighter native integration over time.

• Steep learning curve. Even IDMC is significantly harder to pick up than modern SaaS ELT tools. Realistically a fit for larger organizations with dedicated data integration teams rather than small or mid-market teams.
• Weaker on DataOps and modern developer workflows. IDMC was built before CI/CD-first DataOps became standard. CLI and API automation exist, but the experience is not as native as it is in newer platforms. Schema evolution is supported but has limitations depending on source and destination, and versioning is more cumbersome.
• Higher vendor costs. Informatica is consistently among the more expensive ETL and ELT vendors, both in list pricing and in implementation effort.
• Salesforce ecosystem lock-in is now active. With the acquisition closed, Informatica's roadmap, packaging, and pricing are increasingly tied to Salesforce Data Cloud and Agentforce. Organizations not already standardized on Salesforce should weigh how much platform neutrality they expect to keep over the next two to three years.

Informatica uses consumption-based pricing that is not published in a simple price list and typically requires a quote. The official Informatica Cloud and Product Description Schedule documents the model. Cloud pricing is mostly hourly per compute unit (Informatica Processing Units, or IPUs), with separate models for some workloads like row-based pricing for CDC replication. In general, expect higher total cost compared to most other ELT and ETL vendors, especially when CLAIRE, data quality, MDM, or privacy modules are added on. After the Salesforce acquisition, pricing is expected to increasingly reflect bundled Salesforce ecosystem packaging and enterprise-wide agreements.

Estuary is the right-time data platform that replaces fragmented data stacks with one dependable system for data movement. Teams use it to move data from databases, SaaS apps, files, and streams into warehouses, lakes, operational stores, and AI systems at the cadence they choose: sub-second streaming, near real-time, or scheduled batch. Founded in 2019, Estuary is built on Gazette, an open-source streaming broker developed by the same founding team that lets Estuary mix CDC, streaming, and batch in a single catalog with exactly-once delivery, deterministic recovery, and targeted backfills.

Unlike traditional ELT tools that focus on batch loads, Estuary stores every event in collections that can be reused for multiple destinations. Captured changes are written once to durable storage and fanned out to any number of targets without reloading the source, which reduces load on primary systems and makes replay easy when schemas change. Estuary runs as a multi-tenant cloud service, private data plane, or BYOC, and ships with 200+ fully-managed native connectors plus support for open-source Airbyte, Meltano, and Stitch connectors.

For AI-native workflows, Estuary ships Agent Skills that work with Claude Code, Cursor, OpenAI Codex, GitHub Copilot, and Gemini CLI, letting developers create captures, materialize into Snowflake, BigQuery, Redshift, or Databricks, and troubleshoot pipelines through natural-language prompts. A separate MCP server handles docs-aware Q&A inside the same assistants.

Customers include Glossier, which cut data costs by 50%; Xometry, which reduced integration costs by 60% with private deployment; Headset, which cut Snowflake ingestion costs by 40% after replacing Airbyte; and Prodege, which built Apache Iceberg pipelines.

• Right-time pipelines from millisecond streaming to scheduled batch. Choose cadence per pipeline so cost and freshness match each workload. Most ELT tools default to 15-minute or hourly intervals.
• One platform for CDC, batch, and streaming. Replaces the typical 3-4 tool stack of Debezium plus Kafka plus Airbyte plus dbt with a single system, reducing tool sprawl and operational overhead.
• Dependable replication built on Gazette. Exactly-once delivery, deterministic recovery, and targeted backfills keep pipelines stable through schema changes and source failures.
• Efficient log-based CDC with collection reuse. Captures inserts, updates, and deletes once, then fans out to any number of destinations without re-reading the source database, reducing load on production systems.
• Predictable usage-based pricing. $0.50 per GB moved plus $100 per connector instance per month for the first 6 instances, then $50 per instance for additional ones. No MAR-based surprises and no per-row charges.
• Agent-native developer experience. Open-source Agent Skills let Claude Code, Cursor, OpenAI Codex, GitHub Copilot, and Gemini CLI build and operate Estuary pipelines from natural language, with an MCP server for docs-aware Q&A in the same tools.

• No graphical transformation UI. Estuary focuses on SQL and TypeScript transformations alongside dbt integration. Teams that need point-and-click visual ETL like Matillion or Informatica PowerCenter will find this a gap, though dbt covers most warehouse-side needs.
• On-premises connectivity is narrower than legacy ETL vendors. For mainframe, SAP ECC on-premises, or other proprietary on-premises systems, vendors like Informatica or Talend may have broader native coverage. Verify legacy on-premises coverage during evaluation.
• Smaller market presence than category incumbents. Fivetran, Informatica (now Salesforce), and Talend (now Qlik) have larger enterprise customer bases and longer procurement track records. Estuary fits teams able to evaluate on technical merit, but buyers requiring a Gartner Magic Quadrant leader may need to factor this in.

Estuary uses a straightforward usage-based pricing model. Data movement is charged at $0.50 per GB sourced or delivered. Connector instances are $100 per month for the first 6 instances, then $50 per month for each additional instance. A Developer tier is free indefinitely up to 10 GB per month and 2 concurrent connector instances, and Cloud-tier customers can request a 30-day free trial.

Use the Estuary pricing calculator to model your specific workload. For larger deployments, Enterprise plans add volume-based discounts, SOC 2 and HIPAA compliance reports, SSO, custom SLA terms, private deployments, and dedicated support.