Informatica VS Meltano

Informatica started as one of the first ETL vendors with Powercenter, in 1993. It then released one of the first cloud integration products, Informatica Cloud, in 2006. Informatica Cloud was originally built based on an older version of Informatica PowerCenter. After PowerCenter started to get replaced by a new Hadoop (Spark)-based framework, Informatica Cloud eventually moved over as well. After being taken private by Permira in 2015 and having a long period as a private company, Informatica became publicly traded again in 2021. In May 2025, Salesforce signed a definitive agreement to acquire Informatica, aiming to enhance its enterprise data and AI capabilities.

Informatica is perhaps the best example of a mature data integration platform. While it was one of the first to make the transition to the cloud and has one of the strongest and broadest data integration feature sets, it is harder to use, more expensive, and not as DataOps-native. But it has great enterprise features and one of the better private cloud architectures.

• A comprehensive data management platform: Informatica Intelligent Data Management Cloud is much more than ETL-based data integration. It includes dozens of options including replication, data quality and master data management.
• Rich data integration functionality: Informatica has developed a rich library of capabilities over the years for data integration. 
• Great connectors: Over 300 connectors, including proven connectors to high-performance on premises and cloud data warehouses.
• Performance and scalability: Informatica is built to support large deployments and deliver low latency data pipelines at scale. Informatica has supported serverless compute, pipeline partitioning, push-down optimization, and other features for years.
• Private cloud: Informatica is one of the few vendors that supports a private data plane deployment managed by a shared SaaS control plane.
• Now backed by Salesforce: The acquisition by Salesforce positions Informatica to be more tightly integrated with the Salesforce Data Cloud and Einstein AI ecosystem.

• Harder to learn: While Informatica Cloud is easier than Powercenter was, it still has a significant learning curve compared to most SaaS ELT services. This makes it more suitable for larger, specialized data integration teams.
• Doesn’t support DataOps as well: Informatica Cloud was built pre-CI/CD and DataOps. While you can use its CLI and API to automate deployment, it’s not as simple as a more modern platform. Schema evolution is supported, but there are some limitations depending on the source and destination. Versioning and other tasks are harder than some of the more modern ELT tools.
• Higher vendor costs: Informatica is more expensive than most other ELT and ETL vendors.
• Salesforce ecosystem lock-in: Now that Informatica is being integrated into Salesforce, platform neutrality may diminish over time, especially for organizations not already using Salesforce products.

Informatica’s consumption-based pricing is complicated and requires a quote. You can read the Informatica Cloud and Product Description Schedule here. Cloud is mostly based on hourly pricing per compute units, with some other pricing like row-based pricing for CDC-based replication. In general, you can expect a higher cost compared to most other vendors. With Salesforce’s acquisition, Informatica pricing may increasingly reflect bundled Salesforce ecosystem offerings and enterprise packaging.

Meltano was founded in 2018 as an open source project within GitLab to support their data and analytics team. It’s a Python framework built on the Singer protocol. The Singer framework was originally created by the founders of Stitch, but their contribution slowly declined following the acquisition of Stitch by Talend (which in turn was later acquired by Qlik).

Meltano is focused on configuration-based ELT using YAML and the CLI.

• Open source ELT: Meltano is the main successor to Stitch if you’re looking for a Singer-based framework.
• Configuration-driven: If you are looking for a configure-driven approach to ELT, Meltano may be a great option for you.
• Connectivity: Meltano and Airbyte collectively have the most connectors, which makes sense given their open source history with Singer. Meltano supports Singer and has an SDK wrapper for Airbyte, giving it 600+ open source connectors in total. Open source connectors have their limits, so it’s important to test out carefully based on your needs.

• Not low-code: If you’re looking for a more graphical, low-code approach to integration, Meltano is not a good choice.
• Latency: Meltano is batch-only. It does not support streaming. While you can reduce polling intervals down to seconds, there is no staging area. The extract and load intervals need to be the same. Meltano is best suited for supporting historical analytics for this reason.
• Reliability: Some will say Meltano has less issues when compared to Airybte. But it is open source. Connectors may not be maintained and if you have issues you can only rely on the open source community for support.
• Scalability: There isn’t as much documentation to help with scaling Meltano, and it’s not generally known for scalability, especially if you need low latency. Various benchmarks show that larger batch sizes deliver much better throughput. But it’s still not the level of throughput of Estuary or Fivetran. It’s generally minutes even in batch mode for 100K rows.
• ELT only: Meltano supports open source dbt and can import existing dbt projects. Its support for dbt is considered good. It also has the ability to extract data from dbt cloud. Meltano does not support ETL.
• Deployment options: Meltano is deployed as self-hosted open source. There is no Meltano Cloud, though Arch is offering a broader service with consulting.
• DataOps: Data engineers generally automate using the CLI or the Meltano API. While it is straightforward to automate pipelines, there isn’t much support for schema evolution and automating responses to schema changes.

Meltano is open source. There is no pricing. But it’s not really free. You’ll need to spend more on data engineering resources to stand up, build, and maintain Meltano. If you need scalability, there isn’t a lot of documentation on how to scale. Make sure you evaluate carefully and find some Meltano expertise.

Estuary is the right time data platform that replaces fragmented data stacks with one dependable system for data movement. Instead of juggling separate tools for CDC, batch ELT, streaming, and app syncs, teams use Estuary 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, near real time, or scheduled.

The company was founded in 2019, built on Gazette, a battle tested streaming storage layer that has powered high volume event workloads for years. That foundation lets Estuary mix CDC, streaming, and batch in a single catalog and gives customers exactly once delivery, deterministic recovery, and targeted backfills across all of their pipelines.

Unlike traditional ELT tools that focus on batch loads into a warehouse, Estuary stores every event in collections that can be reused for multiple destinations and use cases. Once a change is captured, it is written once to durable storage and then fanned out to any number of targets without reloading the source. This reduces load on primary systems, provides consistent history for analytics and AI, and makes it easy to replay or reprocess data when schemas or downstream models change.

Estuary can run as a multi tenant cloud service, as a private data plane inside the customer’s cloud, or in a BYOC model where the customer owns the infrastructure and Estuary manages the control plane. This gives security and compliance teams the control they expect from in house systems with the convenience of a managed platform.

Estuary also has broad packaged and custom connectivity, making it one of the top ETL tools. The platform ships with a growing set of high quality native connectors for databases, warehouses, lakes, queues, SaaS tools, and AI targets. Estuary also supports many open source connectors where needed, so teams can consolidate around one system while still covering niche sources and destinations. Customers consistently highlight predictable pricing, strong reliability, and partner level support as key reasons they choose Estuary instead of Fivetran, Airbyte, or DIY stacks.

Estuary Flow is highly rated on G2, with users highlighting its real-time capabilities and ease of use.

• Right time pipelines: Estuary lets you choose the cadence of each pipeline, from sub second streaming to periodic batch, so cost and freshness match the workload.
• One platform for all data movement: Handles CDC, batch loads, and streaming in one product, which reduces tool sprawl and simplifies operations.
• Dependable replication: Exactly once delivery, deterministic recovery, and targeted backfills keep pipelines stable even when sources or schemas change.
• Efficient CDC: Log based CDC captures inserts, updates, and deletes once and reuses them for many destinations, reducing load on operational databases.
• High scale architecture: Gazette and collections support large, continuous data streams with reliable throughput across multiple targets.
• Modern transforms: Supports SQL and TypeScript based transformations in motion, and integrates cleanly with dbt for warehouse side ELT.
• Flexible deployment choices: Available as cloud SaaS, private data plane, or BYOC, giving enterprises strong control over data residency and security.
• Predictable total cost of ownership: Transparent pricing based on data volume and connector instances avoids MAR based surprises and is easy to forecast.
• Fast time to value: A guided UI, CLI, and templates help most teams build their first dependable pipelines in hours instead of weeks.
• Partner level support: Customers report quick connector delivery, responsive troubleshooting, and SLAs that make Estuary feel like an extension of their team.

• On premises connectors: Estuary has 200+ native connectors and supports 500+ Airbyte, Meltano, and Stitch open source connectors. But if you need on-premises app or data warehouse connectivity, make sure you have all the connectivity you need.
• Graphical ETL: Estuary has been more focused on SQL and dbt than graphical transformations. While it does infer data types and convert between sources and targets, there is currently no graphical transformation UI.

Of the various ELT and ETL vendors, Estuary is the lowest total cost option. Estuary only charges $0.50 per GB of data moved from each source or to each target, and $100 per connector per month. Rivery, the next lowest cost option, is the only other vendor that publishes pricing of 1 RPU per 100MB, which is $7.50 to $12.50 per GB depending on the plan you choose. Estuary becomes the lowest cost option by the time you reach the 10s of GB/month. By the time you reach 1TB a month Estuary is 10x lower cost than the rest.