This was originally posted on The New Stack. Once upon a time, log management was relatively straightforward. The volume, types, and structures of logs were simple and manageable. However, over the past few years, all of this simplicity has gone out the window. Thanks to the shift toward cloud native technologies—such as loosely coupled services, microservices architectures, and technologies like containers and Kubernetes—the log management strategies of the past no longer suffice.

It’s a day for celebration! Our migration is complete, and our applications are now running in the cloud environment best suited to their needs. The rest of our application inventory, the ones not cut out for the cloud, remain on-premises where they belong. Actually…we’re not done yet. We still have some work to do to make sure our hybrid environment runs smoothly and delivers the business value we expect. Fortunately, we aren’t the first ones to travel this path.

As we discussed in our previous post on cloud cost analysis and optimization, with over 60% of all cloud costs attributable to compute, focusing on compute infrastructure spend should be of the utmost priority. In this post we will focus on the public cloud vendors’ pricing models and how to best leverage them all for maximum cost optimization.

While we know the many benefits of going serverless - reduced costs via pay-per-use pricing models, less operational burden/overhead, instant scalability, increased automation - the challenges are often not addressed as comprehensively. The understandable concerns over migrating can stop any architectural decisions and actions being made for fear of getting it wrong and not having the right resources.

Information security (infosec) is a broad field. Its practitioners behave more like artists than engineers. After all, the mandate for security is not “do X”, but instead “ensure no one can do X, Y, Z, ɑ, β, ɣ, etc.”. The array of possibilities leading to infosec failure are vast. It’s like trying to prove a negative, thus making the task near impossible. On one hand we have an impossible task, on the other we have the affordance of time.

Hi folks! Éamon here. I’m a recent-ish addition to the Solutions Engineering team and just getting my feet wet on the blogging side so bear with me. :) Back in March, we introduced the Grafana Cloud Agent, a remote_write-focused Prometheus agent. The Grafana Cloud Agent is a subset of Prometheus without any querying or local storage, using the same service discovery, relabeling, WAL, and remote_write code found in Prometheus.

Applications have traditionally been run on servers, which have to be closely monitored to ensure there are no hindrances in production. In the case of serverless computing, you no longer have to manage servers, as the onus falls on cloud service providers. Read along to discover more about serverless computing, including some of its advantages and disadvantages. So what actually is serverless computing?

Cloud is no longer a differentiator – it’s a strategic requirement for long-term success. So says Forrester’s Benchmark Your Enterprise Cloud Adoption report, and so say our customers, 90 percent of whom choose our cloud products over hosting Server or Data Center versions on-premise (on-prem). Ten years ago, moving to the cloud was about staying ahead of the curve – no longer.