OpenObserve stood out for its simplicity, cost efficiency, and unified observability in a single, easy-to-deploy platform.
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Thank you @Walter Duan, Your support is the driving force behind our continuous improvement, and we will continue to provide everyone with better products.
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This looks like a solid alternative to the big players! I’ve been looking for an observability tool that doesn't feel like a second job to manage. The 2-minute setup claim is bold—I’m going to spin up a cloud instance today and see how it handles my logs. Excited to see where the AI SRE agent goes!
@new_user___07720267fad0ea729d0e1af You can try to turn OpenObserve on a cloud instance. However to start, just try following this doc on your laptop, and in under 30 seconds, you will have OpenObserve up and running with complete GUI, logs, metrics, traces, dashboards, and more - https://openobserve.ai/docs/getting-started/
You can use self-hosted or cloud - Either way, you will be up and running in 30 seconds.
The SQL-based querying is a smart move — one of the biggest pain points with Datadog was learning their proprietary query syntax just to search logs. Being able to use standard SQL lowers the barrier for the whole team, not just the SRE on call.
Curious about the AI SRE agent — does it work well with lower-volume setups? Most of the AI-powered monitoring I've seen needs a lot of historical data to be useful. Would love to know the minimum scale where it starts adding value.
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Maker
@nao_lore_ AI is always more powerful when placed on top of massive datasets- this how LLMs work, tho when it comes observability data ( L/M/T).. data quality is far more important then just volume. AI SRE is capable of deep semantic analysis across your entire observability stack and not just sampled fragments.
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Maker
Hey everyone. We built OpenObserve to fix the headaches of complex observability, but the community is what actually shaped it. The GitHub stars, the feedback, the bug reports, the honest conversations.. we owe our continued success to you.
I'll be hanging out here - happy to answer any questions!
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Maker
Hi everyone! 🙋 It's been a wild journey moving from our first lines of Rust code to this launch. OpenObserve was really shaped by our early open-source contributors who pushed us to make it API-compatible with things like Prometheus and OpenTelemetry.
We’re just getting started with our AI-native features, and I’d love to hear from this community: how can we make your on-call rotations or debugging sessions less painful? Drop your questions below!
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Expanding on our AI-Powered Insights: We’re big believers that AI should be a co-pilot, not a black box. While our automated SRE agent handles the heavy lifting of the analysis, we make sure to present every correlated signal. It’s all about keeping the human in the loop—giving SREs the full context they need to make fast, informed decisions with total confidence.
We’ve struggled with Elastic costs before, so this definitely sounds interesting. Wondering what the trade-offs are, where do you compromise to get that level of cost reduction?
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What kind of storage savings are teams seeing compared to Datadog or similar tools? Congrats on the launch!
Thank you @Walter Duan, Your support is the driving force behind our continuous improvement, and we will continue to provide everyone with better products.