Observe, Inc.

When we started Observe, Inc. customers with 10, 20, 50TB / day were unusual. Now we see AI startups approaching 10TB / day and mid-sized SaaS companies with 20-40TB / day. Most large enterprises are way beyond 100TB / day and often looking at 500TB / day in the next year or so. Petabyte scale sounds big, but for an average large enterprise that’s just a week of data. Here’s our founding engineer Ang Li talking about how we built Observe, Inc. to handle petabyte scale data volumes.

We’re committed to open data formats at Observe, and Apache Iceberg is a big part of that strategy. Why? Because data shouldn’t live in silos. With Iceberg, teams can: ✅ Share and reuse data without endless copies ✅ Query telemetry and business data together ✅ Scale analytics without vendor lock-in 👉 Jacob Leverich breaks down why Iceberg is reshaping the future of observability: https://lnkd.in/gucwNsNZ Do you see the observability + Iceberg combination making a difference for your team? #observability #apacheiceberg #iceberg #datalake

Ang Li and 🌩️ Warren Parad discuss the challenges of building an observability platform for petabyte scale. Should you build a database or use an existing one? How do you handle streaming, bursty ingest? What trade-offs do you have to make when designing for scale? 👉 Hear Ang's thoughts about these questions and more: https://lnkd.in/g5wZ9Eua #observability #devops #platformengineering

Scaling observability is one of the toughest challenges engineering leaders face — rising data volumes, runaway costs, and slowing developer velocity. That’s exactly what we unpacked in today’s Masterclass in Observability at Scale, with lessons from operating at Google and Stripe. At Observe, our vision is simple: solve the scale and context problem by unifying all telemetry in a data lake, mapping it with a knowledge graph, and powering it with AI — so teams can move faster at a fraction of the cost. 👉 If you missed it, you can catch the replay on LinkedIn Live, Friday 9/12 at 10 AM PT.

🚀 We’re launching the Observe Community Forum → community.observeinc.com It’s a shared, searchable space where customers, users, and developers can: • Share best practices • Collaborate on use cases • Build a growing library of answers This is just the beginning—we’ll be adding fresh content and growing our user base in the coming weeks. 👉 Be one of the first to shape the conversation: community.observeinc.com

🚀 NEW on We ❤️ Open Source 🚀 Observability is entering a new era. At All Things AI 2025, Wade Moore (Observe Inc.) showcased how AI Investigators, automated runbooks, multi-agent orchestration, and RAG are helping engineers move from reactive troubleshooting to proactive, insight-driven operations. With open standards like OpenTelemetry and Apache Iceberg paving the way, the observability market is primed for massive AI-driven transformation. Read the article: https://lnkd.in/evDxA64R #WeLoveOpenSource #AI #Observability #DevOps #OpenTelemetry #Automation #RAG

I learned so much from this Masterclass in Observability at Scale with Qi Jin and Jacob Leverich, where they shared personal stories and lessons learned from Google, Stripe, Splunk and . Some very interesting takeaways- 1. Observability data lake and how to make it work for profoundly large scale log analytics 2. Team structure and how to measure what matters, particularly when you want to empower each team to own their own metrics but also come together to build more resilient systems 3. Context is the new bottleneck and how teams use modern approaches like knowledge graphs to bring business context and correlate entities instead of treating logs, metrics and traces as separate signals 4. How to use AI to increase velocity not only by assisting with troubleshooting but also helping instrument your code better 5. Why open standards like OTel and Iceberg are changing the game for observability at scale and how to reason about the short term and long term impact for your team I personally learned a lot. What are some of the aha moments for you? https://lnkd.in/guYdr7gM

Qi Jin president of engineering and products at Observe, Inc., explains how artificial intelligence (AI) is transforming software engineering with the rise of the “Vibe Loop”: an emerging pattern where instrumentation, telemetry, investigation, and code changes converge within the development environment to create a continuous and intelligent loop. Thanks for a great interview Michael Vizard. https://lnkd.in/gT-YiydJ

It's not too late to join us for tomorrow's Masterclass in Observability at Scale. Our technical leaders, Qi Jin and Jacob Leverich, will share hard-earned lessons and practical recommendations from their time building at companies like Google and Stripe, including: * How to design for efficiency at scale * Why context is essential when scaling observability and AI agents * Strategies for increasing engineering velocity * Where observability and software development are headed Whether you’re building in anticipation of growth or already operating at scale, you won't want to miss this! Link to register in comments 👇

Scale breaks everything. Your systems. Your processes. Your teams. Even your ability to adapt under pressure. That’s why we’re hosting a Masterclass in Observability at Scale—featuring lessons learned from Stripe, Google, and enterprise deployments at petabyte scale. You’ll hear directly from three leaders who have done it at the highest levels: 👤 Jeremy Burton — CEO of Observe 👤 Qi Jin — President of Engineering 👤 Jacob Leverich — Chief Product Officer Together, they’ll share the hard-earned lessons every engineering org needs to know: ⚡ The dos and don’ts of scaling from Google & Stripe ⚡ How to architect for cost efficiency (without slowing down) ⚡ Why context is the secret weapon for observability & AI agents ⚡ Strategies to unlock engineering productivity ⚡ Where observability is heading in the age of AI Whether you’re bracing for growth or already running at scale, this session will give you an actionable roadmap to succeed. 📅 Save your spot now: https://lnkd.in/gfrvtvn2 #ObservabilityAtScale #DevOps #SRE #EngineeringLeadership #AIObservability