Monitoring platform for keeping systems up and running at all times.
Full stack visibility across the entire stack.
Detect and resolve any incident in record time.
Conform to industry best practices.
This past year has been a crazy roller coaster with Dashbird and 2018 isn’t showing signs of slowing down either, so I took some time to write down what we have achieved so far …for history’s sake.
The idea for creating a product like Dashbird came to us in the end of 2016 and it was really out of necessity. We were working with AWS Lambda and loved the whole Serverless framework and where it’s going, but we saw that there were virtually no monitoring or debugging tools for it out there. We wanted to build a Serverless computing monitoring platform that would start out by solving our own problems with AWS Lambda and then cover other Serverless services too.
So, after countless hours of shower-thinking sessions and heavy discussions over beers, we decided to take the leap into the unknown and go all-in with Dashbird. There is a saying that entrepreneurship is about jumping into the water and then learning to swim. It sure felt like that back in March!
After a few months of heavy hacking, we were ready to launch private beta. It was a proud moment filled with hope and unicorn fantasies. Thanks to BetaList and Producthunt, we managed to get the first users on the service and collected a ton of valuable feedback.
We went public with Dashbird and almost instantly got our first paying customers thanks to – this article in Serverless blog. This was the moment that we realized: “Oh shit, I think we’re on to something”.
After the launch and first paying customers, we honestly have just been hustling and working towards building Dashbird into a company it deserves to be. We concluded our first year (it has actually been less than 6 months since the product launch) with 13 paying accounts, 0% churn rate and accelerating signup growth.
We feel that this is going to be the ride of our lives and we have so much exciting stuff to announce in coming weeks. Keep an eye on these hungry birds!
Follow us in Twitter – @thedashbird
In this article, we’re covering 4 tips for AWS Lambda optimization for production. Covering error handling, memory provisioning, monitoring, performance, and more.
In this article we’ll go through the ins and outs of AWS Lambda pricing model, how it works, what additional charges you might be looking at and what’s in the fine print.
Dashbird was born out of our own need for an enhanced serverless debugging and monitoring tool, and we take pride in being developers.
Dashbird gives us a simple and easy to use tool to have peace of mind and know that all of our Serverless functions are running correctly. We are instantly aware now if there’s a problem. We love the fact that we have enough information in the Slack notification itself to take appropriate action immediately and know exactly where the issue occurred.
Thanks to Dashbird the time to discover the occurrence of an issue reduced from 2-4 hours to a matter of seconds or minutes. It also means that hundreds of dollars are saved every month.
Great onboarding: it takes just a couple of minutes to connect an AWS account to an organization in Dashbird. The UI is clean and gives a good overview of what is happening with the Lambdas and API Gateways in the account.
I mean, it is just extremely time-saving. It’s so efficient! I don’t think it’s an exaggeration or dramatic to say that Dashbird has been a lifesaver for us.
Dashbird provides an easier interface to monitor and debug problems with our Lambdas. Relevant logs are simple to find and view. Dashbird’s support has been good, and they take product suggestions with grace.
Great UI. Easy to navigate through CloudWatch logs. Simple setup.
Dashbird helped us refine the size of our Lambdas, resulting in significantly reduced costs. We have Dashbird alert us in seconds via email when any of our functions behaves abnormally. Their app immediately makes the cause and severity of errors obvious.
End-to-end observability and real-time error tracking for AWS applications.