Airbyte Cloud Reviews

Airbyte Cloud's main use case for our organization is enabling our data analytics team to collect all datasets from multiple data sources into a single warehouse or data lake.

A specific example of how my data analytics team uses Airbyte Cloud is by obtaining datasets from various sources, such as logs and metrics from multiple sources. These sources need to be managed centrally through a system that functions as a data warehouse or lake, which Airbyte accomplishes. Airbyte Cloud extracts data from applications and databases, such as Salesforce, Stripe, and APIs, and loads them into destinations such as Snowflake, BigQuery, and Redshift. Airbyte Cloud collects our data and keeps it synced automatically to multiple destination sources.

Airbyte Cloud functions as a data pipeline engine for a modern data stack.

The best features Airbyte Cloud offers include the centralization of data for analytics by combining data from SaaS tools, databases, and APIs into one place. It enables production models, BI reporting, and ML use cases. Airbyte Cloud has a huge connector ecosystem with over 600 connectors across SaaS tools, databases, apps, and platforms that can be integrated. Faster integration requires less engineering work. Automation and reliability are significant features, allowing me to schedule syncs daily with auto-retry, logging, and alerting capabilities. Incremental updates such as CDC for efficiency are available. Airbyte Cloud is a managed cloud solution with no infrastructure worries, as everything is managed. It is customizable, and the significant difference is that we have full control. It is also more cost-effective than competitors.

Airbyte Cloud has positively impacted my organization by reducing the manpower required for managing the underlying resources of a data sync. It directly performs the job that a database engineer would do by managing a huge connector ecosystem with over 600 connectors across SaaS tools and databases, enabling faster integration. Whenever new data arrives, it automatically syncs to the destination source without requiring any engineer to manually copy or replicate the data. This approach helps our organization significantly.

Airbyte Cloud could be improved because the documentation and setup manual are quite limited. Every time we need to implement features, we must conduct research to understand some of the functionality. Comprehensive video tutorials, demonstrations, or proper documentation would be beneficial.

There are some bugs in the user interface that could be improved.

I have been working in my current domain for beyond five years.

Airbyte Cloud is much more stable than Fivetran.

Airbyte Cloud's scalability is good, and the functionality is also good. We can run multiple syncs in parallel at the same time.

Airbyte Cloud's customer support is professional and quite responsive, with response times that are faster than expected.

We used Fivetran earlier, which offers similar features but resulted in significantly higher costs. We observed a 30 to 70 percent cost reduction compared to Fivetran. We have achieved much greater efficiency from a financial perspective, and the sync frequency is higher. Many manual or human resources have been reduced as a result.

We switched from Fivetran due to price constraints.

I have observed a return on investment with 30 to 70 percent of costs saved. Due to faster sync capabilities, there is a reduced need for data engineer resources. Nearly 20 percent of human manual work has also been saved.

Regarding pricing and licensing, the process is a FinOps task. Airbyte Cloud is more effective than Fivetran, which we used earlier. Its price is 30 to 70 percent lower compared to competitor tools in the market.

Before choosing Airbyte Cloud, I did not have other options on my evaluation list, so I did not evaluate alternative solutions.

My advice for others considering Airbyte Cloud is that it is cost-effective and syncs faster than traditional engineering approaches.

I rate Airbyte Cloud nine out of ten because I generally do not give perfect scores, as the technology is still evolving and still has some bugs in the user interface. Additionally, there is a lack of documentation for new users to understand the product quickly and utilize its functionality and features properly. My overall rating for this review is nine out of ten.

Public Cloud

["Google", "Amazon Web Services (AWS)"]

Our main use case for Airbyte Cloud is consolidating data from multiple sources: drone flight logs, RTs, soil sensors, weather APIs, mobile app backends, and CRM tools, all into one central data warehouse. As a product team, we use the unified data to track product usage patterns, monitor field performance, and make better decisions about future priorities.

We had a specific challenge where our drone data was stored in one database, farm engagement data was in another system, and weather data was coming from a third-party API. Our data analysts were manually downloading and combining this data every week, which was error-prone and slow. I helped implement Airbyte Cloud to automate all three data pipelines in our BigQuery warehouse within a two-week setup. Our analysts had a single source of trust, updating automatically every hour, and the weekly manual data merge process was completely eliminated.

The best features Airbyte Cloud offers are the huge connector library, automatic schema change detection, and scheduling and synchronized frequency control. The transformation support with dbt integration, and the clear monitoring dashboards that show sync status and error every time are also notable.

Definitely the pre-built connectors have been the most valuable feature for my team, and it has made my workflow easier. As a product manager intern, I don't have deep engineering resources to build custom data pipelines from scratch. Having a ready-made connector for tools such as Google Sheets, PostgreSQL, HubSpot, and various API tools means I can set up a new data pipeline in under one hour without writing a single line of code. The self-service capability has been incredibly empowering for the product team specifically.

Airbyte Cloud has positively impacted our organization by directly improving our product decision-making speed. Before, we were making feature decisions based on gut feelings or out-of-date weekly reports. Now we have nearly real-time data flowing into our dashboards, and we can see exactly how farmers are using our app, which drone features are being used the most, and where the drop-offs happen. This has made our product roadmap more evidence-based.

I give it an eight because of error messages. If they solve some error messages, that would help significantly. Sync failures can be technical and hard to understand for a non-engineer. A more user-friendly error explanation would be beneficial.

We have been using Airbyte Cloud for approximately eight months now during a phase where our data is scattered across too many disconnected systems, and we need a reliable way to bring everything together.

Regarding accuracy and reliability, Airbyte Cloud's sync accuracy has been reliable in our experience. Data arrives complete and correctly structured almost every time. We have had very few incidents of data loss or corruption. The incremental sync feature is particularly very accurate as it only moves new or changed records, which keeps our warehouse clean and our data cost-controlled.

Airbyte Cloud scales well as our data needs grow to a scale of ten.

Airbyte Cloud compares favorably to other data integration tools I have used or evaluated, as it is more smooth and manageable, and you can set it up on your own without a developer.

The experience of integrating Airbyte Cloud into our existing tech stack was much smoother than I expected, especially considering how complex our tech stack is at Adarsh Human. We have a fairly diverse setup, using PostgreSQL for our core application database.

Since using Airbyte Cloud, we save approximately seventy to seventy-five percent of the time our data team was spending on manual data preparation. That is roughly six to eight hours per week recovered. For a lean startup team, that is significant. We also avoid hiring a dedicated data engineer for pipeline maintenance, which has saved us a significant salary. Airbyte Cloud essentially covers that function at a fraction of the cost.

Airbyte Cloud is already a good application and does not need improvement.

The learning curve for new users on our team is very easy to understand. It does not require coding skills to implement it, and users can use it very easily.

I would describe the documentation and resources provided by Airbyte Cloud as awesome. Their connectivity and core scale are good, and the complex parts, such as connectivity to IoT and APIs, are well documented. For a product intern such as myself who needs coordination and does not have deep developer skills, Airbyte Cloud made everything very manageable.

My advice for others looking into using Airbyte Cloud is that if they have multiple data flows, this is a great application and a great product for connectivity and all types of data in one system. Airbyte Cloud provides more complex customized IoT and API solutions, and I believe everyone should use Airbyte Cloud. I rate this product an eight overall.

Public Cloud

Google

My main use case for Airbyte Cloud is ELT data ingestion into Snowflake. I use Airbyte Cloud to extract data from Postgres and a few SaaS sources, then load it into Snowflake for analytics and reporting. The Postgres to Snowflake sync is the core pipeline, mostly for operational data replication. For SaaS sources, it is more for consolidating business data into a single warehouse so downstream data dashboards and queries can run consistently.

A typical example is our Postgres to Snowflake sync. We configure Airbyte Cloud to run scheduled syncs for specific tables that contain transactional data. Once set up, it runs automatically in batch mode, and I mainly monitor sync status and handle occasional schema changes or failed sync retries. On a day-to-day basis, it is not heavy manual work. Most of the time it involves checking that syncs are healthy, reviewing logs when something fails, and adjusting sync configurations if there are changes in source schema.

One significant benefit for us is reducing the amount of custom pipeline maintenance. Once the sync is configured in Airbyte Cloud, we do not have to maintain scripts or connectors ourselves, simplifying the process of keeping Postgres and Snowflake in sync reliably. It also helps with onboarding new data sources faster; instead of writing custom ingestion logic, we can set up a connector and validate the schema in Snowflake, which speeds up getting data into analytics.

The best features I found most useful were the large number of pre-built connectors, the managed scheduling for syncs, and the ability to monitor sync status and failures through the UI without needing to maintain infrastructure. The connector ecosystem is probably the biggest advantage, especially for Postgres and common SaaS tools. The scheduling and retry handling reduce the operational work, and the UI makes it easy to see failed syncs and debug issues without digging into manual logs.

The connector ecosystem has helped mainly by reducing engineering time when adding new sources. For example, when we needed to bring in additional Postgres tables and a couple of SaaS sources, we did not have to write custom ingestion logic. We could just configure existing connectors and focus on schema mapping in Snowflake. The UI made it easier to quickly see sync status and failures without digging into logs or infrastructure. If a sync fails, we can immediately see which connector has failed and roughly why, such as authentication issues or schema changes, and then decide whether to retry or adjust configuration.

My suggestion for improvement would mainly be around areas that could be enhanced: error debugging depth in the UI, more granular visibility into why a sync failed, and better handling or guidance around schema changes when they happen frequently in source systems. In some cases, having more proactive alerts or clearer recommendations when syncs fail would reduce the time spent manually checking logs. For teams with many connectors, better organization or filtering in the UI would help manage at scale.

I have been using Airbyte Cloud for around twelve to eighteen months, mainly in production workflows for moving data from Postgres into Snowflake, along with a few other SaaS and database sources.

In my experience, using it for scheduled Postgres to Snowflake syncs and a few SaaS sources, it has been generally stable for day-to-day use. There are occasional sync failures or schema-related issues.

Airbyte Cloud has handled our workloads well for scheduled syncs between Postgres and a few SaaS sources and Snowflake. We did not hit scaling limits in our usage pattern.

Most of the issues we encountered were handled internally through configuration adjustments, so we did not escalate cases to the support team.

Previously, we did not formally migrate from another dedicated ELT tool. Before Airbyte Cloud, the ingestion workflows were custom script-based. Airbyte Cloud replaced a set of in-house or script-driven pipelines instead of a commercial tool.

From my perspective as a user, the main benefit was reducing engineering time spent maintaining custom ingestion pipelines and lowering operational overhead around data syncs, which indirectly contributes to efficiency. I cannot quantify this in terms of financials, but the time saved on pipeline maintenance and troubleshooting was the most noticeable practical benefit.

I was not directly involved in the structured evaluation of multiple ELT tools. The move towards Airbyte Cloud was mainly driven by the need to reduce maintenance on custom ingestion pipelines and move to a managed solution.

My main advice would be to start small with well-defined use cases, such as a single Postgres to Snowflake pipeline, and validate reliability and schema handling before expanding to more sources. I would rate this solution an eight out of ten.

Public Cloud

Amazon Web Services (AWS)

I use Airbyte Cloud primarily for data integration and ETL workflows between different systems and destination databases.

My setup is straightforward with Airbyte Cloud, and I use it for creating new pipelines such as a pipeline for SFTP to MinIO, MinIO to PG Sequel, and a pipeline between PG Sequel systems.

One challenge I have faced recently in Airbyte Cloud is occasional connector sync failure that requires troubleshooting through logs.

I use Airbyte Cloud as a managed SaaS platform for orchestrating data integration workflows with a cloud-hosted approach. This helps especially to reduce the operational overhead associated with infrastructure maintenance. I have connected various source systems and databases to destinations through Airbyte Cloud, and I have used it to schedule and monitor data synchronization jobs. Since this platform is managed by Airbyte, my team personally focuses more on data pipeline and business requirements rather than infrastructure management.

I have been using Airbyte Cloud for three years.

I use Airbyte Cloud because it has a large number of prebuilt connectors, which makes my work easy to connect applications and databases without extensive custom development. I have been working in ETL processes mainly.

I found the user interface very easy and simple to navigate. Even when managing multiple data syncs, I can easily switch between them. The best feature that I have liked about it is the scheduling and automation features that help reduce manual effort significantly in moving data between systems.

I also appreciate the monitoring capabilities of Airbyte Cloud. I can track sync stats and identify failures quickly if there are any. The logging information is very helpful when I have to troubleshoot connection or synchronization issues. This platform provides flexibility to integrate with modern data stack components and cloud environments.

The areas for improvement could be more detailed error logs. The error logs that are coming are not very comprehensive, but they could provide more detailed error messages. Additionally, an enhanced monitoring dashboard would be beneficial.

I have been using Airbyte Cloud for three years.

I would rate Airbyte Cloud nine out of ten because it has really helped me a lot. A perfect ten would require addressing the areas for improvement that I have already discussed: more detailed error messages and enhanced monitoring dashboards, as well as resolving the occasional connector sync failure that requires troubleshooting through logs.

The governance and security of Airbyte Cloud is very good. From other applications, I have seen that this is a trustworthy application that I can use for my sensitive projects, especially when dealing with banks. I definitely recommend and use this product because it has vast security capabilities.

From my experience, Airbyte Cloud's AI-related capabilities are helpful, especially for accelerating connector setup and providing configuration suggestions and troubleshooting. However, I view them more as productivity aids rather than something to rely on without validation. The recommendations and configurations usually serve as a good starting point and reduce manual effort. However, I personally prefer to review the generated settings and mappings before moving them into production environments. The reliability of the output is consistent for common integration scenarios, but in more complex use cases involving custom transformations, schema changes, or uncommon connectors, some manual adjustments are occasionally required.

My overall rating for Airbyte Cloud is nine out of ten.

Airbyte Cloud's main use case for our organization is to optimize our cost. A specific example of how we're using Airbyte Cloud to optimize costs is that we have some tools, and they also help us to speed up the things we do.

The best feature Airbyte Cloud offers is data transformation.

Airbyte Cloud has positively impacted our organization by being very helpful and speeding up our work environment.

An example of how it has helped speed things up is that we have data here and there, which helps us to organize and speed up our migration.

Data tasks help our workflow and organization because most of the things we do manually, so that helps organize.

Airbyte Cloud can be improved because it looks good so far, but based on the AI coming, they have to catch up with AI agents. Everything seems to be going through AI, so that is a consideration for future improvements.

I have been using Airbyte Cloud for a few months since we are starting.

Airbyte Cloud is stable, as it looks good to me so far.

The customer support is actually good because we have an agent first, and then connect through the support with the tickets, so that is also very fast.

Positive

I did not previously use a different solution.

I'm not sure if we purchased Airbyte Cloud through the AWS Marketplace because that is purchased by our manager. My experience with pricing, setup cost, and licensing is that so far it is straightforward, and I do not feel any difficulty.

I have seen a return on investment with time saving and also reduced cost as well.

Before choosing Airbyte Cloud, we went through the market and looked around, and then we chose Airbyte.

We are integrating with Airbyte Cloud by moving an on-premise monolithic application to the cloud and moving around data. So far, it is good regarding our main use case for Airbyte Cloud, and we are still exploring. Airbyte Cloud looks good to me. My advice to others looking into using Airbyte Cloud is that based on solving the issue, it is very recommendable. I would rate this product a 9.

Public Cloud

Amazon Web Services (AWS)

My main use case for Airbyte Cloud is managing an OLTP database. We had our OLTP database in Oracle, and we were moving daily data from Oracle to Redshift, which is our OLAP database, for our dashboarding and analytics requirement.

The best features Airbyte Cloud offers are the pipelines we are building, which are seamless and just a drag and drop, no-code, low-code platform. This is the most liked feature I have seen in Airbyte.

This definitely saves time and it is also easy for anyone to pick up the skill and develop the pipelines, impacting my daily workflow positively.

Airbyte Cloud has positively impacted our organization by making our daily tasks much easier. It helped with faster reporting and it saved the team's time to develop and build the ETL pipelines, which are specific outcomes and metrics I can share.

Currently, I do not have much feedback on which things Airbyte can improve. I do not have any needed improvements to add.

I have been using Airbyte Cloud for the past three years.

Airbyte Cloud is stable.

Airbyte Cloud is highly scalable, and we can scale it up whenever required on demand.

Customer support is decent.

Previously, I did not use any other solution; I was using Airbyte Cloud as the first thing.

My experience with pricing, setup cost, and licensing was a hassle-free experience.

Definitely, time is saved, indicating I have seen a return on investment.

We did not purchase Airbyte Cloud through AWS Marketplace.

I have explored Fivetran before choosing Airbyte Cloud.

I recommend Airbyte Cloud's self-hosted solution if needed, and only if you require the features of Airbyte Cloud, you can opt in for Airbyte Cloud. I gave this review a rating of eight.

Public Cloud

Amazon Web Services (AWS)

My main use case for Airbyte Cloud is for ETL. I tend to use Airbyte Cloud to extract data from one data source and put it into another data source for VTN. We extracted data from Postgres and dumped it into Redshift, which is an example of a real workflow I have set up.

I think the various connectors out of the box are the best features Airbyte Cloud offers. I don't need to create custom code to do this kind of work, so it is easy for me to use out-of-the-box connectors in my day-to-day work.

Airbyte Cloud has positively impacted our organization because we were looking for multiple products and ended up choosing Airbyte because it is easy to use and set up. It reduced the development effort, and we did not have to build anything by ourselves, so it was easy to get into Airbyte and build the workflows.

I think Airbyte Cloud can be improved by adding more connectors and more customizable connectors.

I have been using Airbyte Cloud for the last one year.

Airbyte Cloud is stable.

Airbyte Cloud's scalability is good since it is on the cloud.

Customer support for Airbyte Cloud is all good.

We used Fivetran, and it was costly, which is why we switched to Airbyte Cloud.

I think it required fewer employees, indicating that I have seen a return on investment.

My experience with pricing, setup cost, and licensing was good.

Before choosing Airbyte Cloud, I evaluated other options, specifically Fivetran.

My advice to others looking into using Airbyte Cloud is that since it is reliable and easy to use, you can use it. I would rate Airbyte Cloud overall as seven because it has room for improvement.

Public Cloud

Amazon Web Services (AWS)