Robin Vasan

During yesterday's LibreQoS APNIC webinar, I posed the question: won't more bandwidth solve these problems? As Herbert Wolverson said, yes, more bandwidth is good, but, still these problems remain if queueing is done incorrectly. Here's my take on why bandwidth alone is not as good as bandwidth + good queueing policies: Generally it was believed that 'data is important, so don't throw it away; hang on to it and send it later'. In practice, this has proven to be suboptimal as two issues may emerge: 1) latency increases for some flows due to heavy demand from other flows using the same bottleneck link, 2) even a single flow can have excessive latency due to aspects of typical TCP behavior (referred to bufferbloat) when the buffers grew large enough that the data being buffered was retransmitted anyway. It turns out that not all data is equally important. Active Queue Management is the art of deciding priorities, both in deciding what data to throw away, but also in allowing some later arriving data to be transmitted ahead of data in another connection that arrived before it. These problems have been studied, and good solutions have been found by using certain queueing policies in routers and switches, referred to as “fq_codel’ and “cake”. These track the different flows not by classifying the data, but by watching the behavior. Flows that send relatively little data (DNS lookups), or at a measured pace (video chat) have priority over flows that send a lot of data as quickly as possible (App and System updates, Video and ISO downloads).

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Some additional charts on the aggregate hyperscaler data from Q1. The charts below show 1) The aggregate net new ARR added on q quarterly basis. Q1 is always a seasonal low 2) The YoY growth in net new ARR in Q1. Q1 '23 was the "bottom" with AWS actually adding negative net new ARR (ie they shrunk), so the YoY comp is the "easiest" for the aggregate bucket.

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➰ APIContext's latest insights on cloud-based API performance have been featured on Nordic APIs! In the 2024 report, we delve into crucial aspects affecting API efficiency: 🔐 Authentication is key. Ensuring robust authentication mechanisms in APIs is crucial, yet offloading this function can introduce complex challenges. 📖 DNS configurations matter. How you handle DNS settings can significantly impact API performance, particularly when tied to authentication processes. 🌐 The role of third parties. Critical third-party services play a significant role in 🔗 API transactions, underscoring the need for careful integration and monitoring. ⛅ Not all clouds are created equal. Differences in cloud services can drastically affect API performance, an essential consideration as APIs account for over 80% of all internet traffic. #APIs #CloudComputing #TechInsights #APIContext

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At #OffsetSymposium2024, and couldn’t agree more with Corey Jaskolski that wide-spread #cloud adoption across #DOD allows startups to scale more broadly, lowers their costs, allows them to move faster, and ultimately delivers more, saving the limited taxpayer dollars we have for #nationaldefense.

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Instacart is a market leader when it comes to leveraging AI tools to boost productivity at work. I had an amazing conversation with JJ Zhuang, Instacart’s Chief Architect, who has shepherded over 50 AI-driven initiatives to market across the org. We explored what catapulted Instacart to the forefront of AI integration, supporting radically diverse use cases across various functional areas. One striking insight (echoed by Ethan Mollick and others elsewhere) is that you often don't need specialized tools for specialized work. Frontier models like GPT-4 can seamlessly adapt to a wide range of functions and use cases. They're not just for engineers; they're incredibly versatile tools used by marketing, comms, and legal teams. From generating code to proofreading and ideation, GPT-4 empowers every department to leverage AI for enhanced productivity and innovation. By integrating LLM’s like GPT-4 into their workflows, teams can streamline processes, automate repetitive tasks, and unlock bandwidth for new creative possibilities. How can you harness the power of AI in your own work to boost productivity? Share your thoughts below 👇 Want to hear more insights from JJ? Check out the full podcast episode by clicking the link in the comments.

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In vertical SaaS, the very best companies landed with a product solving a specific, burning pain point that horizontal solutions solved badly and then became broad product suites over time (ServiceTitan, Veeva Systems, Doximity, etc). My partner Gordon Ritter calls this “layering the cake”. In vertical AI, the landing wedges are much more brittle. While the painpoints they solve are very real (and often unaddressable by SaaS alone), they’re also much more likely to commodotize in the near term as the tech advances. 👀 Therefore, the need to layer the cake in vertical AI is much more urgent than in vertical SaaS. 👀 The best vertical AI CEOs not only recognize the dynamic, but talk about it openly. Helps those around them keep up the speed of innovation and their eyes on the bigger prize.

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Founderville believes one of the most significant opportunities is emerging in legacy system applications. We look for manual tasks to leverage AI for efficient automation. Rather than chasing massive TAM, which will be addressed by larger VCs, we focus on smaller market domains that won't be chased as hard. Think of "boring" processes that require human intervention or manualized tasks. Those are opportunities. Keep your eyes open if you have deep domain expertise in a non-automated and fragmented industry. #entrepreneurs #venturecapital #startups #saas

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How @IronMountain built its InSight DXP on @MongoDB https://buff.ly/3zD1gUu We caught up with Adam Williams, Vice President of Global Platforms at Iron Mountain, to talk about use of MongoDB and scaling. Here's a look at some of the key takeaways.

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Thrilled to announce the launch of our inaugural edition of Beyond Benchmarks at Emergence Capital. This comprehensive report dives deep into the metrics and trends shaping the early-stage enterprise cloud market. A huge thank you to our VC partners and contributors for making this possible! Here's a sneak peek of our findings: --> 60% of companies have already integrated GenAI into their service offerings, with another 20% planning to do so this year. --> While most companies use OpenAI as their primary LLM, many are experimenting with multiple models. We’re seeing a trend toward intelligently routing GenAI inference requests based on cost, performance, and security. --> Companies that have implemented GenAI are showing promising results, with a 7% higher NDR compared to those that haven’t. Beyond Benchmarks goes further with more GenAI trends, insights on the current fundraising environment, and key performance metrics. Our goal is to provide founders and their teams with valuable benchmarks to help them make better-informed decisions. At Emergence Capital, we're committed to helping founders build iconic companies. Dive into the full report here: https://lnkd.in/g6bnvAZM

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Multi cloud GenAI needs, with all the building blocks for modern applications, from MongoDB...

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As we stand on the brink of 2025, the world's technology, infrastructure, AI, Data, Cloud, Crypto, venture and everything else in tech are poised for a transformative year. I am excited to share my predictions for 2025 — insights that I believe will guide us through the next wave of innovations as important as the internet and smartphones. Together, let’s explore the trends that will not only guide and influence our thinking and investment strategies but also inspire us to create a better world through entrepreneurship. Here are a few predictions 💡💡💡: -  Imagine the creation of HealthGPT, BioGPT, NuclearGPT, LogisticsGPT, GovGPT and more - Establishment of a US Bitcoin Reserve by the end of 2025 - SpaceX, OpenAI, Stripe, SHEIN, Databricks, Canvas, Chime, Anduril, and Scale AI are anticipated to go public - 50% of the Department of Government Efficiency (DOGE) new hires are from Silicon Valley. #prediction #2025 #venture #funding #M&A #DOGE #bitcoin #AI #GPT #IPOs #exits #startups #venturecapital #USmarket #databricks #chime #anduril #scaleAI #OpenAI #stripe #spaceX #bytedance #canvas Read more on my Substack  👉 👉 👉

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The Forbes 2024 Cloud 100 List is live ☁ Congrats to all the honorees – the world's top 100 private cloud companies –including the 3 healthcare and life sciences companies featured this year: • Benchling - biotech R&D platform • Komodo Health - healthcare data analytics • Abridge - AI for clinical documentation For more on how AI is colliding with cloud computing, check out Bessemer Venture Partners's 2024 Cloud 100 report. Personally I'm focused on advancements in multi-modal AI, as applied to biomedical applications (Trend 3!): https://lnkd.in/euzurvQV

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Worth reading: How the Apache Arrow Format Accelerates Query Result Transfer by Ian Cook One of the hidden problems of intersystem communication is transcription of data formats. Ian does a wonderful job of explaining the "why" of Apache Arrow as well as the "how" of its advantages. Read on...

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🚀 In April of 2023, Vantage launched beta support for OpenAI. It's been one of the most popular and fastest-adopted providers we've added support for. However, the APIs we were leveraging for beta support were a bit shaky and only intermittently available from the OpenAI side. We've been *eagerly* awaiting OpenAI to launch more robust billing and usage APIs and as of 22 days ago, they have delivered! As a result, I'm really excited to share our General Availability support for OpenAI on Vantage. Our team was able to very quickly execute on adopting the newly available APIs and now have progressed OpenAI to General Availability on Vantage which is available to all users across all tiers, including the free tier. Read the full launch blog post with all the details here: https://lnkd.in/e7f7yNYE #finops

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Why IBM's Acquisition of HashiCorp is a Game-Changer for Investors IBM's $6.4 billion acquisition of HashiCorp significantly bolsters its hybrid and multi-cloud capabilities, positioning it as a leader in the AI era. HashiCorp’s expertise in cloud infrastructure, lifecycle management, and security automation complements IBM’s strengths, creating an end-to-end cloud platform. This strategic move enables IBM to offer enhanced solutions for managing complex cloud environments and generative AI workloads, providing investors with robust growth potential and a competitive edge in the evolving tech landscape. #IBM #HashiCorp #CloudComputing #AI #InvestmentOpportunity #PINCommunityAdvocate #IBMPartner

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And we’re live. 🚀 Today, we’re thrilled to announce we’re launching out of stealth with $10.5 million in funding to build a unified dataops platform. When co-founders Benjamin Segal & Geva Segal started Matia, they wanted to solve a real problem they saw in the data and engineering world. They were managing and scaling large teams, and relying on disparate tools to move their data to Snowflake and back to their SaaS platforms, another tool to leverage their data lineage, and another one to monitor their data pipeline quality. Their teams were full of talented engineers, but they were spending most of their time piecing together their data stack and investigating anomalies, rather than focus on their core job. So they spoke to other data and engineering leaders about how they solved this problem. Everyone agreed it was an issue, but no one had the solution. And as more companies adopted AI strategies, the importance of an observability platform that natively monitored data before it was even ingested became even more important. So that’s where the idea of Matia was born: A unified dataops platform bridging together ingestion, reverse ETL, observability and catalog. 🧠 With pre-built connectors, data and AI teams can plug in seamlessly to the tools they use, and integrate with Databricks, Snowflake and even monitor transformations natively in dbt Labs. While it’s only the beginning of our journey, we’re grateful to have amazing customers like Ramp, Obligo, & HoneyBook onboard and already seeing incredible results, and backed by investors including Leaders Fund, Secret Chord Ventures, Cerca Partners & Caffeinated Capital. The best is yet to come. Follow our journey. Link in comments for more.

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Last April I gave a talk at Palo Alto Networks about AI's impact on enterprise software. I didn't know Satyam Sinha, their VP of Engineering, was in the audience—and he'd been thinking about this for months. He soon resigned and shifted focus to building a startup that would push AI innovation forward instead of simply adapting to it. Fast forward to today: My partner Jaya and I are thrilled to announce that Acuvity, the company born from Satyam's vision, has raised a $9M seed round led by Foundation Capital, with participation from Jonathan Siddharth (CEO of FC portfolio company Turing) and other angels. Acuvity tackles one of the most pressing issues in enterprise AI adoption—security. Companies rushing to leverage AI are exposing themselves to unprecedented risks of data leaks and compliance violations. Samsung's recent AI-related data breach is not an anomaly. Satyam and his co-founder Antoine Mercadal have developed a novel approach using AI models to monitor AI usage, combining elements of classical machine learning with LLMs. It's an elegant solution to a complex problem, providing enterprises with the visibility and control they desperately need. This investment combines two of Foundation's deep convictions: the transformative power of AI and the critical importance of data security. We've been investing at this intersection for over a decade, from MobileIron to Fortanix to Skyflow. We're excited to lead this round and partner with Satyam, Antoine, and the entire Acuvity team on their journey to secure AI-powered enterprise! Read more: https://lnkd.in/gPiiutpD

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The FireHydrant acquisition of Blameless signals a major shift in observability and incident management. My Partner Jaya and I just wrote about the looming "observability crisis"—how the explosion of data and tools has paradoxically made it harder to extract meaningful insights. This deal is a direct response to that crisis. By combining forces, FireHydrant and Blameless aim to build an end-to-end incident management platform to reimagine root cause analysis from the ground up. But to *truly* address the observability crisis, it will take more than consolidation. We need AI-driven systems that: → Provide natural language interfaces to query complex systems → Automatically generate and update incident response playbooks → Offer predictive maintenance by recognizing patterns across heterogeneous data sources → Synthesize post-mortem reports, distilling insights from terabytes of data Most critically, we need to ditch volume-based pricing models that incentivize data hoarding over problem-solving. Expect more moves as the market evolves to meet the demands of increasingly complex enterprise systems.

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