Launch HN: Opstrace (YC S19) – open-source Datadog

Launch HN: Opstrace (YC S19) – open-source Datadog
6 by spahl | 0 comments on Hacker News.
Hi HN! Seb here, with my co-founder Mat. We are building an open-source observability platform aimed at the end user. We assemble what we consider the best open source APIs and interfaces such as Prometheus and Grafana, but make them as easy to use and featureful as Datadog, with for example TLS and authentication by default. It's scalable (horizontally and vertically) and upgradable without a team of experts. Check it out here: http://opstrace.com/ & https://ift.tt/33DRGgY About us: I co-founded dotCloud which became Docker, and was also an early employee at Cloudflare where I built their monitoring system back when there was no Prometheus (I had to use OpenTSDB :-). I have since been told it's all been replaced with modern stuff—thankfully! Mat and I met at Mesosphere where, after building DC/OS, we led the teams that would eventually transition the company to Kubernetes. In 2019, I was at RedHat and Mat was still at Mesosphere. A few months after IBM announced purchasing RedHat, Mat and I started brainstorming problems that we could solve in the infrastructure space. We started interviewing a lot of companies, always asking them the same questions: "How do you build and test your code? How do you deploy? What technologies do you use? How do you monitor your system? Logs? Outages?" A clear set of common problems emerged. Companies that used external vendors—such as CloudWatch, Datadog, SignalFX—grew to a certain size where cost became unpredictable and wildly excessive. As a result (one of many downsides we would come to uncover) they monitored less (i.e. just error logs, no real metrics/logs in staging/dev and turning metrics off in prod to reduce cost). Companies going the opposite route—choosing to build in-house with open source software—had different problems. Building their stack took time away from their product development, and resulted in poorly maintained, complicated messes. Those companies are usually tempted to go to SaaS but at their scale, the cost is often prohibitive. It seemed crazy to us that we are still stuck in this world where we have to choose between these two paths. As infrastructure engineers, we take pride in building good software for other engineers. So we started Opstrace to fix it. Opstrace started with a few core principles: (1) The customer should always own their data; Opstrace runs entirely in your cloud account and your data never leaves your network. (2) We don’t want to be a storage vendor—that is, we won’t bill customers by data volume because this creates the wrong incentives for us. (AWS and GCP are already pretty good at storage.) (3) Transparency and predictability of costs—you pay your cloud provider for the storage/network/compute for running Opstrace and can take advantage of any credits/discounts you negotiate with them. We are incentivized to help you understand exactly where you are spending money because you pay us for the value you get from our product with per-user pricing. (For more about costs, see our recent blog post here: https://ift.tt/2YxXjKX ). (4) It should be REAL Open Source with the Apache License, Version 2.0. To get started, you install Opstrace into your AWS or GCP account with one command: `opstrace create`. This installs Opstrace in your account, creates a domain name and sets up authentication for you for free. Once logged in you can create tenants that each contain APIs for Prometheus, Fluentd/Loki and more. Each tenant has a Grafana instance you can use. A tenant can be used to logically separate domains, for example, things like prod, test, staging or teams. Whatever you prefer. At the heart of Opstrace runs a Cortex ( https://ift.tt/2ztx3ph ) cluster to provide the above-mentioned scalable Prometheus API, and a Loki ( https://ift.tt/2C6TKkt ) cluster for the logs. We front those with authenticated endpoints (all public in our repo). All the data ends up stored only in S3 thanks to the amazing work of the developers on those projects. An "open source Datadog" requires more than just metrics and logs. We are actively working on a new UI for managing, querying and visualizing your data and many more features, like automatic ingestion of logs/metrics from cloud services (CloudWatch/Stackdriver), Datadog compatible API endpoints to ease migrations and side by side comparisons and synthetics (e.g. Pingdom). You can follow along on our public roadmap: https://ift.tt/2NXl5hl . We will always be open source, and we make money by charging a per-user subscription for our commercial version which will contain fine-grained authz, bring-your-own OIDC and custom domains. Check out our repo ( https://ift.tt/33DRGgY ) and give it a spin ( https://ift.tt/3cxlVvo ). We’d love to hear what your perspective is. What are your experiences related to the problems discussed here? Are you all happy with the tools you’re using today?