From left: Mikk Kirstein, Taavi Rehemägi, Annika Helendi
Dashbird, the fastest growing AWS Lambda monitoring and debugging platform, has secured an $800 000 seed round from Passion Capital, Icebreaker.vc and angel investors with the aim of dominating the serverless monitoring and debugging market.
Dashbird currently has a proven track record for AWS Lambda users, reporting 154% growth in paying customers since the start of the year. With the seed round investment, they aim to widen the product offering by building out integrations for event sources and covering all major cloud providers (Microsoft Azure Functions and Google Cloud Functions).
Dashbird prides itself on having the easiest setup that doesn't require any code changes and takes only a few minutes to get up and running. Mikk Kirstein, CTO says: “The main technical edge of Dashbird is the low friction and high granularity we can offer through analyzing CloudWatch logs. You don’t have to make any code changes to get an overview of all of your lambda functions and at the same time, you get really detailed error alerts via email or Slack."
Dashbird was founded at the beginning of 2017, looking to solve the problem Mikk Kirstein and Taavi Rehemägi themselves saw emerging for them as serverless early adopters - there weren’t any suitable monitoring or debugging tools available.
Dashbird's CEO Taavi Rehemägi believes in this market's potential though, saying: “I'm convinced that serverless will be the next big paradigm shift in cloud computing because of its cost efficiency, scalability and quick go-to-market time for new products which will lead to an overall boost in tech innovation”
The third co-founder Annika Helendi, joined the team at the beginning of 2018 with a mission to elevate its marketing efforts and to push the serverless community forward.
The founding trio has previously worked on multiple globally successful startups from Estonia like Starship Technologies, Toggl, Teamweek, and Testlio.
What is serverless?
Serverless computing is a cloud-computing execution model in which the cloud provider dynamically manages the allocation of machine resources. Pricing is based on the actual amount of resources consumed by an application, rather than on pre-purchased units of capacity.