News & Updates
The latest news and updates from companies in the WLTH portfolio.
Vercel Acquires Tekedia Capital Portfolio, Better Auth
Good People, it is with great excitement that I announce the acquisition of one of our portfolio companies, Better Auth, by the industry-leading decacorn, Vercel. Founded by a self-taught tech prodigy from Ethiopia, Better Auth has ascended to become a preeminent force in open-source authentication. Tekedia Capital congratulates the Better Auth team and looks forward to Vercel's stewardship of this innovative platform. You can read the full details here: https://vercel.com/blog/vercel-acquires-better-auth. This acquisition follows the recent purchase of another portfolio company by OpenAI last month, with a public announcement to follow shortly. May the harvest season be bigger for Tekedia Capital community. Connect via my LinkedIn | Facebook | X | YouTube
Globant and Vercel Forge Strategic Alliance to Take Enterprises from Agentic AI to Full Production with AI Pods
Globant is also launching Vercel-powered AI Pods -- specialized agentic service units that design, develop, and modernize enterprise digital products on Next.js, offering record-time migration of legacy frontends. Globant, a global company that delivers AI-native services to turn AI ambition into measurable business performance, announced a multi-year strategic alliance with Vercel, the leading platform for building and running modern websites, applications, and agents, and the creator of Next.js. Enterprises can now benefit from Globant's signature AI Pods, specialized agent-orchestrated service units, and AI-built applications deployed directly to Vercel, streamlining the path beyond agentic workflow to production without the friction of traditional deployment cycles. This collaboration addresses a recurring bottleneck for enterprise technology leaders: the gap between building something and actually getting it live, secure, and ready to scale for real users. With this new offering, the same AI process that creates the solution also launches it -- returning a secure, live web address in minutes. For enterprise customers, this offering turns multi-month projects into same-week experiences, without leaving the platform, making AI ROI visible almost immediately. Marketing Technology News: MarTech Interview with Haley Trost, Group Product Marketing Manager @ Braze Globant is introducing a new line of AI Pods dedicated to modernizing older websites and applications. Built on Vercel, they redesign and rebuild enterprise digital products to be faster, more reliable, and ready for AI -- in a fraction of the usual time. Through this alliance, enterprise customers will benefit from: * AI-Powered Modern Web Development: Specialized AI Pods that design, build, and optimize digital products on Vercel -- fast, reliable, and ready to integrate AI from day one. * Record-Time Modernization: AI-accelerated upgrades that move outdated websites and apps onto modern technology, reducing time-to-market and ongoing maintenance costs. * One-Click Go-Live: From idea to a live, production-ready application on Vercel's platform -- purpose-built for AI and optimized to load fast for users anywhere in the world. Marketing Technology News: From Data to Impact: How AI is Transforming Interactive CTV Ads "At Globant, our AI Pods model is built on the principle that the best outcomes come from combining world-class technology with expert human supervision. Partnering with Vercel gives our clients access to the gold standard in front-end infrastructure -- and the agility to build, migrate, and scale AI-native digital products without compromise," said Martín Migoya, CEO and co-founder of Globant. "Globant's AI Pods model brings exactly the kind of structured, expert-led deployment that Vercel's platform is built to power intelligently, at scale. Together, we're enabling enterprises to modernize their stack and ship AI-native applications faster, with the reliability and performance their users expect," said Guillermo Rauch, Founder and CEO of Vercel.
Chinese AI Models Gain Ground in U.S. as Lower Costs Challenge OpenAI and Anthropic
Chinese artificial intelligence models are rapidly gaining acceptance among U.S. businesses as companies seek to reduce soaring AI costs without sacrificing performance, marking a significant shift in a market long dominated by American technology firms. Developers and businesses are increasingly turning to open-source and open-weight AI models from Chinese companies such as DeepSeek, Z.ai and Alibaba's Qwen, attracted by systems that many say now deliver capabilities approaching those of leading U.S. models at a fraction of the cost. The trend is emerging at a sensitive moment for the United States, as the Trump administration weighs tighter oversight of advanced AI technologies while also grappling with the growing global influence of Chinese AI developers. Industry data suggests the shift is no longer confined to experimentation. According to OpenRouter, a platform that allows developers to access and compare AI models from multiple providers, more than 30% of tokens used by U.S. companies each week since February 8 have been processed through Chinese AI models. At one point, that share climbed to 46%. The figures represent a dramatic change from previous usage patterns. Over the preceding 12 months, Chinese models accounted for an average of just 11% of OpenRouter's token usage, while their share fell to only 4.5% during the first half of 2025. The sharp increase shows how quickly developers are reconsidering the economics of artificial intelligence as operating costs become a larger concern. Early enterprise AI adoption was largely driven by access to the most capable models available, regardless of price. Increasingly, companies are evaluating whether premium AI systems justify their significantly higher operating costs. Kyle Chan, a fellow at the John L. Thornton China Center at the Brookings Institution, said rising prices at American AI companies are changing purchasing decisions. "Chinese AI models are particularly attractive to American companies now as AI costs skyrocket," Chan told CNBC. "Where previously U.S. companies were prioritizing AI adoption regardless of model, now they're getting more cost-conscious." That shift is disrupting the status quo. Many of the newest Chinese AI systems are distributed as open-source or open-weight models, allowing developers to inspect, customize, or build applications using technology that is not fully locked behind proprietary platforms. This contrasts with many flagship models from OpenAI, Anthropic and Google, whose internal architectures, training methods and core technologies remain proprietary. The flexibility of open models has become attractive for businesses seeking greater control over their AI infrastructure while reducing dependence on commercial application programming interfaces (APIs). The cost savings can be substantial. According to Justin Summerville, who works on data and analytics at OpenRouter, leading Chinese open-source models are typically between 60% and 90% cheaper than comparable offerings from OpenAI and Anthropic. Those economics are beginning to influence real business decisions. AI startup Lindy recently migrated all of its AI workloads from Anthropic's Claude models to DeepSeek, one of China's fastest-rising AI companies. DeepSeek attracted global attention in early 2025 with a highly competitive reasoning model before introducing another major model upgrade in April. Lindy's Chief Executive Officer, Flo Crivello, said the transition immediately transformed the company's operating costs. "We did it, and you could see that cost curve go down, like, crash to the ground," Crivello told CNBC. He estimated the move would save the company millions of dollars within a matter of months. The growing adoption extends beyond DeepSeek. Developer platform Vercel reported that DeepSeek significantly increased its share of AI token usage between May and June. Even more striking has been the rapid rise of Z.ai's GLM 5.2 model. Released in June, GLM 5.2 recorded the fastest adoption of any AI model tracked by Vercel during 2026. According to Harpreet Arora, the company's Head of Agentic Infrastructure, daily token volume surged approximately 27-fold during the model's first full week after launch, while the number of customers using it increased about 80 times. Arora said economics, rather than ideology, is increasingly determining which models companies deploy. "Price is doing the work here," he said. "When a task doesn't need the best model, teams are beginning to route it to the cheapest one that's good enough, and the recent wave of models coming out of China is winning that trade." This shows that companies are now routing different tasks to different models depending on complexity, accuracy requirements and cost, rather than relying on a single AI provider. Routine customer support, document processing, and software development tasks may be assigned to lower-cost models, while more demanding reasoning or research tasks continue to use premium frontier systems. The approach allows organizations to reduce AI expenses while maintaining performance where it matters most. LaunchLemonade, an AI platform serving regulated industries, has observed the same trend. Although Anthropic's Claude and OpenAI's ChatGPT remain its most widely used models, Z.ai's GLM 5.2 has already entered the platform's five most-used AI systems. Chief Executive Officer Cien Solon said businesses are becoming increasingly pragmatic. "Chinese models like Z.ai and Alibaba's Qwen are becoming options for companies as they offer an attractive combination of performance and cost for specific workloads," Solon told CNBC. "Businesses with more mature AI strategies are increasingly willing to use them where they make technical or commercial sense." The growing interest is not driven by price alone. Researchers say Chinese AI models are closing the performance gap with the industry's leading American systems. Chan estimates that China's most advanced models now trail the top U.S. frontier models by approximately six to nine months while costing only a fraction as much to operate. "The new open-source models are performing well and prove capable for all but the most complex LLM tasks," Summerville said. Independent benchmarks increasingly support those assessments. On one closely watched benchmark measuring autonomous AI agent performance, GLM 5.2 finished within roughly one percentage point of Anthropic's Opus 4.8 while operating at around one-fifth of the cost. Some researchers have also reported that GLM 5.2 performs competitively with leading U.S. models on cybersecurity benchmarks, an area traditionally viewed as one of the most technically demanding applications of generative AI. Lindy's experience echoed those findings. Crivello said migrating to DeepSeek V4 improved performance across many of the company's core AI applications, demonstrating that lower cost did not necessarily require sacrificing capability. The rapid rise of Chinese AI is also complicating U.S. technology policy. As Washington considers tighter controls on advanced AI systems, Chinese open-source models remain widely accessible around the world. At the end of June, OpenAI delayed the rollout of a new family of models following requests from the U.S. government. During the same period, export restrictions affecting Anthropic's cybersecurity-focused Mythos and Fable models were lifted after months of negotiations between the company and the Trump administration. Those policy debates reflect broader concerns about maintaining U.S. leadership in artificial intelligence while limiting the international availability of the country's most advanced technologies. Yet some researchers warn that restricting American AI too aggressively could unintentionally strengthen overseas competitors. Yacine Jernite, Head of Machine Learning at Hugging Face, said businesses increasingly want AI systems that they can modify, deploy independently and control without relying entirely on commercial providers. "We're seeing companies increasingly motivated to turn to cheaper AI stacks they can control and adapt themselves, and given the state of open-source and open-weight models that often means leveraging Chinese options," Jernite told CNBC. He cautioned that enterprises could eventually face an uncomfortable choice. "There is a real risk that users get stuck having to choose between performant but expensive U.S. proprietary models whose price and accessibility can quickly fluctuate, or using Chinese models as the only feasible alternative whenever they want to control costs or own their AI stack." That tension highlights the next phase of the global AI race. While American companies continue to lead in developing the world's most advanced frontier models, Chinese developers are steadily narrowing the capability gap while competing aggressively on price. For businesses focused on controlling costs rather than on possessing the absolute best-performing AI, that combination is proving increasingly difficult to ignore.
Run any Dockerfile on Vercel
You have a server in a container. Maybe it's a Go service, a Rails app, a Spring Boot API, or a web server behind nginx. It speaks HTTP. It listens on a port. It just needs somewhere to run. Add a file to your project, and Vercel builds, stores, deploys, and autoscales the image on Fluid compute, so you pay only for the CPU your code uses. No daemon to run locally, registry to set up, or cluster to babysit. Link to headingHow it works Here is a small HTTP server in Go, listening on : Add a file that builds it into a small image and runs it: Then deploy: That is it. Two files, and you are live. Every rebuilds the image and hands you a fresh preview URL. Or run to deploy without committing. We used Go in this example, but any stack works. Rails, Spring Boot, Express, Laravel, ASP.NET, FastAPI, and a web server behind nginx all deploy the same way. The only rule is that your server listens on , which defaults to . If it speaks HTTP, it deploys. Yes, even Java. And yes, even PHP. Link to headingWhat you get A container on Vercel is a first-class citizen. It runs on the same platform, and the same compute, as your frontend and the rest of your services on Vercel. * A preview deployment for every push: Every commit gets its own immutable URL you can open, share, and roll back to. * Autoscaling, in both directions: Traffic arrives and you scale out. Traffic stops and your instances wind down. You never size a fleet or guess a concurrency number. * Active CPU pricing: Fluid compute bills for the time your code is actually running, so an idle server, parked on a slow query or an upstream API, isn't burning CPU while it waits. You pay for execution time, not wall time. * Observability, included: Logs, traces, and metrics for your container live in the same dashboard as everything else you ship. * One project, one domain: Your container sits beside your frontend and your other services and talks to them privately over the Vercel network. Your full stack ships as one deploy. Link to headingBuilt to start fast A container is only as good as the time it takes to answer its first request. When Vercel builds your image, it stores it as an optimized boot image, a compressed snapshot of the container's disk tuned for fast startup. When a container boots, we stream that snapshot and decompress it on demand, rather than downloading the whole image before anything runs. Your server can start handling requests before the full image is in place, so a larger image does not have to finish downloading first. Once an instance is running, Fluid compute keeps it warm and serves many requests from it, rather than starting a fresh copy for each one. You get the responsiveness of a warm server and the bill of one that sleeps when idle. Each container is a stateless process: it takes a request, returns a response, and keeps nothing in between. Persistent state lives in a backing service you attach, like a database or cache from the Vercel Marketplace. Because an instance holds nothing that has to survive, Vercel can add instances when traffic arrives and retire them when it stops. We're also working on shipping durable storage attached to containers soon. Link to headingWhy now? Our first platform let you deploy a Dockerfile with a single command. That was a decade ago, and the idea was right, but the infrastructure to make it great didn't exist yet. We've spent the years since building the primitives to handle it well. They power everything you run on Vercel: Builds, Functions, Sandboxes, and now containers. It all scales with traffic, and you only pay for the CPU you use. A container is now a first-class citizen, running on the same system as everything else. Framework detection is our front door. When we recognize your framework, we read your code and derive the infrastructure your app needs, because the code already describes what it should do. For most apps it's the fastest way to ship. A Dockerfile is for everything else: a service that needs a system library like FFmpeg or Chromium, a framework we do not auto-detect yet, or an app you want to bring exactly as it already runs. It is the universal way to say how a program should be built, so when there is no framework to read, we meet it directly. Everything around your Dockerfile is zero configuration. You point at the image, and the build, the registry, the rollout, the scaling, and the URL all just happen. Link to headingBackends are back Your backend now ships the way your frontend does: one push, one preview, one platform. We can't wait to see what you build. Read the docs or deploy an example to get started.
