Tray.ai is a composable AI integration and automation platform that enterprises use to build smart, secure AI agents at scale. To modernize their marketing site, they partnered with Roboto Studio to migrate off their legacy solution and outdated version of Next.js. The goal: simplify the architecture, consolidate siloed repos, and bring content and form management into one unified system. After moving to Vercel, builds went from a full day to just two minutes.

The Model Context Protocol (MCP) standardizes how to build integrations for AI models. We built the MCP adapter to help developers create their own MCP servers using popular frameworks such as Next.js, Nuxt, and SvelteKit. Production apps like Zapier, Composio, Vapi, and Solana use the MCP adapter to deploy their own MCP servers on Vercel, and they've seen substantial growth in the past month. MCP has been adopted by popular clients like Cursor, Claude, and Windsurf. These now support connecting to MCP servers and calling tools. Companies create their own MCP servers to make their tools available in the ecosystem. The growing adoption of MCP shows its importance, but scaling MCP servers reveals limitations in the original design. Let's look at how the MCP specification has evolved, and how the MCP adapter can help.

Our two conferences (Vercel Ship and Next.js Conf) are our chance to show what we've been building, how we're thinking, and cast a vision of where we're going next. It's also a chance to push ourselves to create an experience that builds excitement and reflects the quality we strive for in our products. For Vercel Ship 2025, we wanted that experience to feel fluid and fast. This is a look at how we made the conference platform and visuals, from ferrofluid-inspired 3D visuals and generative AI workflows, to modular component systems and more.

Search is changing. Backlinks and keywords aren’t enough anymore. AI-first interfaces like ChatGPT and Google’s AI Overviews now answer questions before users ever click a link (if at all). Large language models (LLMs) have become a new layer in the discovery process, reshaping how, where, and when content is seen. This shift is changing how visibility works. It’s still early, and nobody has all the answers. But one pattern we're noticing is that LLMs tend to favor content that explains things clearly, deeply, and with structure. "LLM SEO" isn’t a replacement for traditional search engine optimization (SEO). It’s an adaptation. For marketers, content strategists, and product teams, this shift brings both risk and opportunity. How do you show up when AI controls the first impression, but not lose sight of traditional ranking strategies? Here’s what we’ve noticed, what we’re trying, and how we’re adapting.

An AI agent is a language model with a system prompt and a set of tools. Tools extend the model's capabilities by adding access to APIs, file systems, and external services. But they also create new paths for things to go wrong. The most critical security risk is prompt injection. Similar to SQL injection, it allows attackers to slip commands into what looks like normal input. The difference is that with LLMs, there is no standard way to isolate or escape input. Anything the model sees, including user input, search results, or retrieved documents, can override the system prompt or event trigger tool calls. If you are building an agent, you must design for worst case scenarios. The model will see everything an attacker can control. And it might do exactly what they want.

AI agents are software systems that take over tasks made up of manual, multi-step processes. These often require context, judgment, and adaptation, making them difficult to automate with simple rule-based code. While traditional automation is possible, it usually means hardcoding endless edge cases. Agents offer a more flexible approach. They use context to decide what to do next, reducing manual effort on tedious steps while keeping a review process in place for important decisions. The most effective AI agents are narrow, tightly scoped, and domain-specific. Here's how to approach building one.

We recently launched our AI models v0-1.5-md and v0-1.5-lg in v0.dev and v0-1.0-md via API. Today, we're sharing a deep dive into the composite model architecture behind those models. They combine specialized knowledge from retrieval-augmented generation (RAG), reasoning from state-of-the-art large language models (LLMs), and error fixing from a custom streaming post-processing model. While this may sound complex, it enables v0 to achieve significantly higher quality when generating code. Further, as base models improve, we can quickly upgrade to the latest frontier model while keeping the rest of the architecture stable.

AI’s rapid evolution is reshaping the tech industry and app development. Traditional serverless computing was designed for quick, stateless web app transactions. LLM interactions require a different sustained compute and continuous execution patterns.

Since February, Vercel blocked 148 billion malicious requests from 108 million unique IP addresses. Every deployment automatically inherits these protections, keeping your workloads secure by default and enabling your team to focus on shipping rather than incidents. Our real-time DDoS filtering, managed Web Application Firewall (WAF), and enhanced visibility ensure consistent, proactive security. Here's what's new since February.

Vercel recently published a Model Context Protocol (MCP) adapter that makes it easy to spin up an MCP server on most major frameworks. Vapi is building an API for building real-time voice agents. They handle orchestration, scaling, and telephony to provide a completely model-agnostic and interchangeable interface for building agents.  Vapi rebuilt their MCP server on Vercel, letting users create agents, automate testing, analyze transcripts, build workflows, and give agents access to all of Vapi’s endpoints.

Storage should be simple to set up, globally available, and built to last, without slowing you down or adding complexity. It should feel native to your app. That's why we built Vercel Blob: Amazon S3-backed storage that's deeply integrated with Vercel's global application delivery and automated caching, with predictable pricing to serve public assets cost-efficiently at scale. Vercel Blob is now generally available. It's already storing and serving over 400 million files, and powers production apps like v0 and the Vercel Dashboard.

The Vercel AI Gateway is now available for alpha testing. Built on the AI SDK 5 alpha, the Gateway lets you switch between ~100 AI models without needing to manage API keys, rate limits, or provider accounts. The Gateway handles authentication, usage tracking, and in the future, billing.