Pravir Gupta

I am excited to share the latest episode of The Frugal Architect featuring Tom Leaman, VP of Site Reliability Engineering at Warner Bros. Discovery. Tom takes us through WBD's journey of building and scaling Max globally - from establishing concrete efficiency measures to creating self-service cost allocation tools. What stands out is their pragmatic approach to balancing cost optimization with customer experience. They call it "Don't be Frupid." The results speak for themselves: ~75% reduction in pod count through smart automation, 40% decrease in DynamoDB run-rates by rethinking their architecture patterns, and a more resilient system overall. It's a masterclass in embedding cost-consciousness into engineering culture while keeping focus on what matters most - delivering content reliably to millions of viewers. Read the deep dive here: https://lnkd.in/e7pkqJee Listen to the full conversation here: https://lnkd.in/eBnFyJ4i

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WTG MIT: "In our view, America’s leadership in science and innovation depends on independent thinking and open competition for excellence. In that free marketplace of ideas, the people of MIT gladly compete with the very best, without preferences. Therefore, with respect, we cannot support the proposed approach to addressing the issues facing higher education." —Massachusetts Institute of Technology 👏 https://lnkd.in/gK87AksP

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Excited to share that MongoDB Store for LangGraph is now live! This integration empowers agents with long‑term memory, enabling them to recall, evolve, and build upon user interactions across different sessions—no more goldfish memory. - Agents can finally remember, learn, and evolve across interactions. - Context isn’t lost — it compounds, making AI more useful over time. - Builders get a scalable, secure, and production-ready foundation to power the next wave of intelligent applications. With MongoDB, agents gain - JSON-native memory for complex state and interactions - Atlas Vector Search for semantic recall, not just keywords - TTL controls to manage memory lifecycle - Enterprise-grade scale & reliability for production use At MongoDB, we’re building the data backbone for adaptive, context-aware AI. This release moves us closer to a world where agents don’t just respond — they grow with you. https://lnkd.in/eGpx-SMc

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Launch day! Continuing the drum beat on new provider integrations: today we're announcing a native integration with Anyscale! Anyscale kicks off a series of AI-specific provider integrations we're going to be announcing for the remainder of the year. When I started Vantage, I had a belief that companies were going to be using more and more third-party providers beyond the main three hyperscalers to run their infrastructure. Today, AI has materially accelerated that trend. Each of these AI providers has their own bespoke pricing structure that we need to work with to provide the single pane of glass view into total cost of ownership for an organization. As companies leverage these AI providers, they expect that their Finops platform has support accordingly. As with the ~25 other native integrations Vantage currently supports, Anyscale costs will support all the #FinOps features you know and love across cost allocation, budgeting, forecasting and beyond. Getting starting is as simple as providing an API key and we do all the heavy lifting from there. To see all the details, check out the blog post below.

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"While the project began during the pandemic as a joint effort between Seattle and Google.org, developers recognized its greater potential; the system moved to civic tech firm Exygy for broader deployment in 2023." https://lnkd.in/gNaDT8yV

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We teamed up with Stanford Institute for Human-Centered Artificial Intelligence (HAI) in their effort to train a fully open source Foundation Model. This means that not only the weights, but also the code, data, and processes are fully open sourced. To optimize efficiency and performance, the team combined the power of JAX/XLA and TPUs. Read all the gory details of the journey and the different choices made in our new blog post: https://lnkd.in/gTGdURZ5

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It's a big day at Honeycomb! We're announcing major advancements in Honeycomb Telemetry Pipeline that unlock a new era of observability—one without tradeoffs. 🔥 Over the past several years, conversations around observability have revolved around challenging tradeoffs: retention versus fidelity, availability versus speed—and always, the constant pressure of cost. With the rise of OpenTelemetry shifting telemetry ownership from vendors to engineering teams, it was no surprise to see telemetry pipelines quickly emerge as essential infrastructure. Yet—as standalone solutions—these pipelines reinforce divisions between the producers and consumes of telemetry data (but this time, more toxically, often within the same organization). Honeycomb is committed to a different approach: deeply integrating telemetry pipelines into the observability experience itself. ✨Enhance✨ is the first step toward this vision, eliminating the traditional tradeoffs in data management. Teams now retain easy, cost effective access to all their telemetry, even (especially! 💥) the bits they didn’t expect to matter. By aligning telemetry data management directly with data usage patterns, we're able to foster positive feedback loops across engineering teams and lay the groundwork for a more integrated, insightful, and collaborative future. Welcome to an era of true lossless observability. It's gonna be exciting. 🚀 Learn more from our press release: https://lnkd.in/dqgCZDyy

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Waymo has unlocked a surprisingly specific use case in San Francisco: parents are trusting it to drive their kids to school. They wouldn’t put their kid in an Uber, but they’ll send them solo in a fully autonomous car. That was the moment it really clicked for me. If AI can handle real-time decisions where safety is on the line, it can handle a mortgage file. Document review, condition clearing, data extraction—it’s not even close in terms of complexity. The challenge isn’t whether it’s possible. It’s whether we’re designing systems that make better use of people’s time—because the manual work isn’t just inefficient. It’s holding the entire industry back. https://lnkd.in/gFVA9Yvk #MortgageTech #Automation #AIinLending

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I was CPO at Blend before their $4 billion IPO. We rejected the “forward deployed engineer” Palantir model that everyone is copying now. Here’s why: Blend could have followed the forward-deployed engineer approach. Both our CEO and CTO were early Palantir. They'd seen it work. Instead, we built a SaaS product with core components. Move fast, iterate, use data to learn. Not custom code for every client. That Palantir model is having a resurgence. Now Sierra, Decagon, and others are hiring forward-deployed engineers. But it’s not the right approach for every company. It’s fundamentally the wrong answer for Assembled’s customers. Here's what we understand about support teams and why we’re not going the forward deployed route: Customer Support teams are used to getting no resources and no support. On the surface, you'd think they'd be excited about engineers building custom solutions. "Finally, someone's offering us developers!" The reality is support teams are terrified of getting help they can't afford to keep. Support teams are wary because they know the math. They don't have budget for expensive, scarce resources. At some point the shoe drops. The engineers disappear when priorities shift. And custom solutions break. Support is left holding the bag with something they can't maintain. They're resource-starved by design. The idea of a dependency on resources they can't control is terrifying. So instead, the goal should be to give support teams the ability to manage, change, and solve their own problems. Not dependencies on engineers they can't keep. Smart, competent people who teach them to do it themselves. Palantir wasn't even a product company for years. They were a custom development shop with reusable components. Show up, build something bespoke, maybe extract learnings. That's not scalable for any but the largest support teams who need sustainability. Forward-deployed engineers are the new shiny object. I wasn’t a fan of it at Blend. And I’m not a fan today either. What I see in the customer support space is that teams need empowerment, not dependency. I want the teams we work with to be able to say: "I love that you’re partnering with me. And I love that I can do this myself too." That's the difference between building a product and building a consulting practice.

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As I'm leaving AWS re:Invent in Vegas, I’m reminded of a counterintuitive truth: competing with the public clouds is often really good business VCs love to ask, “Why wouldn’t the incumbent do this?” But in markets this large (and evolving this quickly) that’s rarely the right question. The real questions are: Who can assemble the best team, get to market fastest and with the most focus? And where are there opportunities to partner with the biggest players rather than compete? We’ve seen this pattern repeat across the ecosystem: -People assumed Vercel and AWS were destined to compete. Today, they’re strategic partners with CEO Guillermo Rauch in the keynote -Snowflake vs. Amazon Redshift. Now, AWS is one of Snowflake’s strongest distribution channels -In Datadog’s early pitches, the concern was, “If it’s just monitoring AWS, why not use CloudWatch?” Fast forward to now, and Datadog is now $50B+ public company We’re going to see the same dynamics play out with OpenAI and Anthropic. These are becoming foundational platforms. But that doesn’t preclude incredible companies from being built on, next to, and even around them. Some will look competitive. Many will end up as partners. And the biggest outcomes often start in those gray zones

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