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Why Glassflow
Zero Infrastructure
You define the Python functions and we run the infra. Simple.
Native Python
End-to-end native Python for your pipelines. Forget the Python wrappers and use any Py library you want.
Build in 15 minutes.
Set up your pipeline in just minutes. Get auto scaling of your infra, low latency and optimal data retention. Our templates and connectors will get you ready super quick.
See what you can build with GlassFlow
GlassFlows strengths are in the simplicity of creating event-driven data pipelines. Due to the agnostic technology you can build use cases in several categories.
Frequently asked questions
Feel free to contact us if you have any questions after reviewing our FAQs.
After your free trial finishes and you do not purchase a business or enterprise plan, your account will be downgraded to the free plan. Your first 3 created pipelines will keep running.
We want our users to experience our product before committing to it. Other managed services do not offer a free plan, which we believe is not user-centric enough.
GlassFlow is a cost-efficient solution for event-based data pipelines. Usually, when building those pipelines, you need a message broker (go-to solution is a managed Apache Kafka) and a transformation engine (go-to solution is a managed Apache Flink). With GlassFlow, you save ca. 30% on costs compared to managed services like AWS MSK. Additionally, you already have a transformation engine included, which you usually would need to buy in addition to the message broker.
They are annual subscriptions, with a minimum monthly price (see starting at). We charge you at the end of the month based on your usage. Our free package has no costs for our users.
Yes, we have a CLI that you can download and try on your local machine.
GlassFlow is using NATS Jetstream for the message broker part of the product. That allows us to process up to 3 million messages per second with the same latency as Kafka services.
Supported by our users
Do not listen to us. Listen to them.
“GlassFlow team managed to turn my scattered thoughts regarding the data streaming into a Python masterpiece.”
Anurag Roy@Doorlabs 
“Creating a data pipeline with GlassFlow is incredibly straightforward”
Swapnil Gupta@Deloitte “I am fed up with overcomplicated event-based pipelines. Finally a team that will make my life easier.”
Philippe Brule@aboutphilippe “Love the way they team tackles the complexity of building and sharing streaming pipelines.”
Rakesh Kaswan@KaswanRakesh 
“As a Python dev starting with data streaming, you should absolutely give Glassflow a try.”
Dinesh Marimuthu@Bilthouse “Something is cooking here! Super excited to test your solution.”
Joash Xu@joashxu
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