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.
We recently wrote about why serverless applications fail and how to design resilient architectures. Being able to detect early-stage failure indicators can be invaluable.
With proper monitoring, developers move from waiting for the system to crash and adopt a more proactive attitude in managing resource allocation and architecture design to avoid bottlenecks and performance degradation. This leads to end-user satisfaction, trust among executive team members, and a healthy stream of support requests for customer care agents.
The main challenge is that, even though Serverless abstracts away most traditional infrastructure management, there are still numerous architectural complexities at our hands.
The number and variety of cloud resources we use in serverless applications are growing. Each service has its own intricacies and limitations. Interactions between services increase complexity in rapid proportions. It is difficult to track everything and keep on top of all aspects of such architecture.
Take Queues, for instance. They have to be verified constantly for latency causing high delays, an unusual accumulation of messages in the queue, etc. Compute services, such as AWS Lambda functions or ECS containers have to be monitored for a variety of possible faults, such as high resource (e.g. memory) consumption.
It is also important to have all monitoring metrics, performance and architectural insights in one place so that developers can be efficient to discover and act upon potential issues. Most monitoring platforms, though, still apply a server-based mindset, which doesn’t fit well the serverless paradigm.
Cloud resources cannot be monitored isolated, we must start thinking about our serverless backends as a whole, and almost as living organisms. Otherwise, issues arising from the interaction of services are difficult to track and detect early on.
Dashbird comes embedded with dozens of algorithms for early-detection and alerting of issues in Serverless platforms: software exceptions, infrastructure faults, platform errors. Try it for 14 days free, no credit card required.
Today we are announcing a new, updated pricing model and the end of free tier for Dashbird.
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.