You probably know that we have a generous free plan that allows you to send 20 million events per month. This is enough for many of our customers. In fact, some have developed neat techniques to keep themselves underneath the event limit. I’m going to share one way here—hopefully no one at Honeycomb notices!
Large Language Models (LLMs) are all the rage in software development, and for good reason: they provide crucial opportunities to positively enhance our software. At Honeycomb, we saw an opportunity in the form of Query Assistant, a feature that can help engineers ask questions of their systems in plain English.
Massdriver is a cloud operations platform that makes it easier for engineering teams to build, deploy, and scale cloud-native applications. While many companies use this lofty language to make similar promises, Dave Williams, CTO and co-founder at Massdriver, means it. Before Massdriver, Dave worked in product engineering where he was constantly bogged down with DevOps toil. He spent his time doing everything except what he was hired to do: write software.
Who is software for? It’s an interesting question, because there’s an obvious answer. It’s for the users, right? If your job is to write software, then it’s implied that the most important thing you should care about is the experience people have when they use your software.
Kubernetes has been around for nearly 10 years now. In the past five years, we’ve seen a drastic increase in adoption by engineering teams of all sizes. The promise of standardization of deployments and scaling across different types of applications, from static websites to full-blown microservice solutions, has fueled this sharp increase.
Understanding production has historically been reserved for software developers and engineers. After all, those folks are the ones building, maintaining, and fixing everything they deliver into production. However, the value of software doesn't stop the moment it makes it to production. Software systems have users, and there are often teams dedicated to their support.
Like many companies, earlier this year we saw an opportunity with LLMs and quickly (but thoughtfully) started building a capability. About a month later, we released Query Assistant to all customers as an experimental feature. We then iterated on it, using data from production to inform a multitude of additional enhancements, and ultimately took Query Assistant out of experimentation and turned it into a core product offering.
