Senior Software Engineer - Source Control and Governance

Modal Labs closing $750M round at $15.75B valuation as AI inference demand soars. Tl;dr. * Modal Labs is reportedly closing a $750M round at a $15.75B valuation, more than tripling its valuation from just four months ago amid explosive demand for AI inference. * The surge reflects a broader market shift from model training to inference, where developers are paying a premium for serverless, on-demand GPU infrastructure that scales instantly. * The raise would position Modal as one of the most valuable AI clouds, putting it in direct competition with CoreWeave, Nebius, Lambda, Together AI, and Fireworks AI. A whale-sized round for serverless GPUs. Modal Labs, the New York-based AI infrastructure startup known for its developer-first serverless cloud, is reportedly nearing a massive $750 million funding round that would value the company at $15.75 billion. If finalized, the deal would mark one of the largest private raises for an AI infrastructure company this year and cement Modal as a breakout winner in the second wave of the AI cloud boom. The new valuation represents a stunning leap - more than 3x higher than where the company was valued just four months ago - underscoring how quickly investor appetite for inference infrastructure has accelerated. Sources familiar with the matter say the round is expected to close in the coming weeks, though terms could still shift. Modal has not publicly commented on the raise. From developer darling to decacorn contender. Founded in 2021 by Erik Bernhardsson, former CTO at Better.com and early Spotify engineer behind its music recommendation system, Modal started as a radically simpler alternative to AWS and Kubernetes for running code in the cloud. Its pitch: developers can run any AI workload - from batch jobs to large language model inference - with a single line of Python, no DevOps required. Containers spin up in seconds, scale to thousands of GPUs, and spin down to zero, with customers paying only by the second. That serverless model has struck a chord with AI startups and enterprises drowning in GPU complexity and costs. The company says it now powers workloads for thousands of teams in generative media, voice AI, biotech, and autonomous agents, with usage and revenue growing multiple-fold year-over-year. The reported $15.75 billion valuation is a dramatic jump from Modal's prior valuation earlier this summer, when the company was said to be worth around $5 billion. That prior round itself was already a sharp step-up from its $1 billion-plus valuation in late 2025, charting one of the steepest valuation curves in enterprise tech. Why inference providers are commanding premium valuations. Modal's meteoric rise isn't happening in a vacuum. Investors are pouring tens of billions into what many now call the inference economy. For the past three years, most AI infrastructure capital went toward training - massive clusters of Nvidia H100s and GB200s to build foundation models. Now, the money is shifting to serving those models to hundreds of millions of users. Inference is recurring, high-volume, and increasingly where the margins are. Several forces are driving the premium: First, demand is exploding. The rise of AI agents, real-time voice, video generation, and reasoning models that use 10x to 100x more compute per query has created insatiable need for low-latency GPU capacity. Second, developers want simplicity. Unlike hyperscalers that sell raw capacity by the hour, serverless inference platforms like Modal abstract away autoscaling, cold starts, and GPU orchestration. That ease-of-use commands higher gross margins and fierce loyalty. Third, scarcity equals pricing power. Despite easing shortages, access to latest-generation Nvidia Blackwell chips remains constrained. Neoclouds that secured supply early and can deliver it efficiently are able to grow revenue at triple-digit rates. As one venture investor put it recently, training was a one-time capex boom, inference is a forever software margin. How Modal stacks up against rivals. The AI cloud market has bifurcated into two camps, and Modal is trying to bridge both. On one side are the heavy-asset neoclouds like CoreWeave, now public and valued at over $40 billion, Nebius, Crusoe, and Lambda. These companies raise billions in debt to build massive data centers and lease GPU capacity to hyperscalers and labs on multi-year contracts. On the other side are developer-centric inference platforms like Together AI, last valued at over $3.3 billion, Fireworks AI, valued at $4 billion, and Baseten. These companies compete on speed, model library, and API experience rather than raw megawatts. Modal sits somewhat apart. Unlike pure model-API providers, it lets customers bring any custom model, container, or workflow - not just open-source LLMs. And unlike CoreWeave, it owns no massive data centers itself, instead orchestrating capacity across partners with a software-first layer that optimizes utilization to the second. That asset-light approach has been both its superpower and the bear case against it. Bulls argue Modal can scale faster with far less debt and achieve software-like 70%-plus margins. Skeptics question whether it can guarantee supply of cutting-edge chips at scale without owning the underlying iron, especially as rivals lock up power and Nvidia allocations for years ahead. With $750 million in fresh capital, Modal would have the war chest to answer those doubts - pre-purchasing compute, expanding global regions, and potentially striking dedicated capacity deals while continuing to pour into engineering talent. What comes next. If the round closes as reported, Modal will join an elite tier of private AI infrastructure companies worth over $10 billion, alongside Databricks, OpenAI's infrastructure affiliates, and CoreWeave before its IPO. The key questions now are burn versus growth, path to profitability, and whether an IPO is on the horizon. At $15.75 billion, public-market expectations will be unforgiving - investors will want to see sustained triple-digit growth, enterprise traction beyond startups, and defensibility against both AWS and Nvidia-backed rivals. For now, though, the message from the market is clear: in 2026, inference is king, and Modal Labs is one of its crown princes. AndroGuider Team Articles written by the AndroGuider team. Androguider try to make them thorough and informational while being easy to read.

Akamai locks in $11.6B as Anthropic bets on CPUs. Akamai signed its largest deal ever, an $11.6B seven-year CPU cloud commitment with Anthropic that could reach $20B. Akamai announced an $11.6 billion, seven-year commitment from Anthropic to power cloud infrastructure for rising demand around its models, according to the announcement. This deal transforms Akamai from a pure delivery network into a main AI compute provider. The agreement leans on CPUs rather than GPUs and could climb toward $20 billion if Anthropic taps extra capacity, per TechCrunch, which also notes Akamai will award Anthropic warrants covering up to 5% of its stock as part of an unusual equity kickback linked to spending. This structure reshapes how teams building large-scale Claude models allocate resources. Workloads that don't need accelerators can move to Akamai Cloud to chase lower prices and better availability, while GPU clusters remain dedicated to training and cutting-edge inference. Operators will need to split deployments, rework autoscaling and routing rules to treat Akamai as a core region instead of an edge cache. Since acquiring Linode and expanding distributed compute, Akamai has pushed its cloud platform hard, yet it still battles AWS, Google Cloud and Azure for enterprise contracts and faces competitors like CoreWeave and Lambda for AI-native capacity. A flagship customer paying around $1.65 billion annually provides steady utilization, justifying data center builds and giving Akamai a foothold convincing buyers who've already tried its delivery and security services. The published release omits details on pricing, prepayment or early termination, and Akamai's own press page didn't go beyond the headline figure at launch. If the CPU focus proves right, cost-conscious inference operations will thrive. Otherwise, Akamai keeps its seven-year commitment intact. (C) 2026 StartupHub.ai. All rights reserved. You may not republish this article in full without a license. Search engines and AI research tools may crawl and summarize for reference. Bulk reproduction or model training requires a license. See our terms. Discussion. Join the conversation below Editor, StartupHub.ai Daniel Singer is the editor of StartupHub.ai, a technology expert and thought leader on AI and its applications across sectors, from fintech and healthcare to developer tooling and consumer software. He writes and tests the tools covered here thoroughly and regularly, and built StartupHub.ai to give founders, operators and buyers a clearer read on what they are actually being sold.