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Edge Runtime

14 posts · Jun 2022 – Apr 2026

What this trail covers

Vercel's Edge Network provides a globally distributed infrastructure for caching and computing user requests close to the user. This enables both high performance for static assets and dynamic personalization by caching the results of dynamic requests. Edge Functions can now be regionally executed, allowing developers to colocate compute resources near data sources to avoid latency waterfalls. This complements the existing global distribution of Edge Functions for low-latency user experiences. The Edge Runtime is a subset of Node.js APIs, giving compatibility and interoperability between multiple web environments, compliant with WinterCG standards. It uses the JavaScript V8 engine in production, not Node.js, and is designed for integration into frameworks like Next.js.

All Vercel trails
  1. What is Edge RuntimeIntroduces the Vercel Edge Runtime, a lightweight, TypeScript-based runtime compliant with WinterCG standards. It provides a subset of Node.js APIs for compatibility across web environments and is designed for framework integration, not direct application use. In production, it utilizes the V8 engine, excluding Node.js APIs. Local development polyfills Web APIs for Node.js compatibility.
  2. Scale to one: How Fluid solves cold startsThis post introduces 'scale to one' as a strategy to keep at least one function instance warm, preventing first-visitor cold starts. It also details how Fluid compute reuses existing instances for multiple requests, reducing the number of cold starts. Predictive scaling is also mentioned as a method to warm instances ahead of demand. The post quantifies the reduction in cold starts to less than 1% of requests.
  3. Introducing Active CPU pricing for Fluid computeIntroduces Active CPU pricing for Fluid compute, a new pricing model that charges for CPU only when code is actively executing. This model aims to further optimize costs for I/O bound workloads with high idle times, such as AI inference. The pricing is based on three metrics: Active CPU (milliseconds of vCPU usage), Provisioned Memory (GB-hours), and Invocations. This builds upon the existing Fluid compute model which intelligently orchestrates compute across invocations to eliminate cold starts and reuse idle time.
  4. Update regarding Vercel service disruption on August 7, 2024Details an incident where an upstream provider for Edge Middleware and Edge Functions caused a 26-minute outage due to an erroneous configuration state. Vercel mitigated the issue via a config override and is implementing future mitigations including improved configuration management with the provider, developing a backup option for Edge Middleware/Functions, and unifying the compute subsystem for Edge, Edge Middleware, and Serverless Functions.
  5. Introducing bytecode caching for Vercel FunctionsIntroduced experimental bytecode caching for Vercel Functions to reduce cold start latency. This involves caching the compiled JavaScript bytecode generated by the V8 engine, which is then re-used on subsequent cold starts. The implementation overcomes limitations of ephemeral file systems in serverless platforms by developing a custom solution that continuously improves the cache with traffic. Tested with Next.js applications, showing significant improvements in TTFB and billed duration. Opt-in via `USE_BYTECODE_CACHING=1` environment variable for Node.js 20 and CommonJS. Future support for ES Modules is planned.
  7. Streaming from serverless Node.js and Edge Runtime on VercelThis post details Vercel's custom solution for enabling HTTP response streaming on AWS Lambda, which lacks native support. It explains the request flow through the Edge Location and Serverless Function Invocation Service, the modification of requests with callback URLs, and the use of a bridge code within Lambda to establish a secure socket connection for transferring the response stream back to the invocation service for client delivery. This significantly reduces TTFB and FCP by allowing incremental data processing.
  8. Build a GPT-3 app with Next.js and Vercel Edge FunctionsThis post details the implementation of a GPT-3 application using Next.js for the frontend and a backend API route. It explains the frontend components, state management, and the API call to OpenAI. The backend API route is shown to construct a payload for OpenAI's GPT-3 model and send a POST request. The post also provides historical context, recommending Vercel Functions with Fluid compute and streaming for new projects instead of the Edge Functions approach demonstrated.
  9. Near-zero latency using Vercel Edge Functions | Customers | VercelThis post details how Read.cv utilized Vercel's Edge Functions to achieve near-zero latency for personalized content delivery, significantly improving their global customer base's user experience. It highlights the benefits of edge deployment for speed and the flexibility of serving some pages from the edge while others are pre-generated static assets within the same Next.js and Vercel toolkit. The post also mentions Vercel's support for wildcard domains, automatic SSL cert generation, and DNS management for multi-tenant platforms.
  10. Sanity balances experimentation and performance with Vercel Edge Middleware | Customers | VercelThis post details how Sanity uses Vercel's Edge Middleware to run A/B tests and personalization experiments on their marketing site. It highlights the benefits of reduced overhead and complexity compared to their previous SSR React Framework, Docker, and GCP stack. The seamless switch to Vercel and Next.js, along with features like Preview Deployments and out-of-the-box CI/CD, are emphasized. The use of Edge Middleware for stress-free experimentation is a key takeaway.
  11. Vercel Edge FunctionsThis post announces the General Availability (GA) of Vercel Edge Functions. It details performance optimizations, support for `cache-control` headers, regional execution preferences, WebAssembly support, and increased function size limits (2MB for Pro, 4MB for Enterprise). It also highlights cost-effectiveness compared to traditional serverless functions and mentions future work on API compatibility between Edge and Vercel Functions, and improved observability.
  12. Edge Config: Ultra-low latency data at the edgeIntroduced Edge Config, a new ultra-low latency data store optimized for configuration data at the edge. It functions as a push-based CDN, replicating data to all regions before requests are made, ensuring instant availability. The system is designed for fast, inexpensive reads and infrequent writes, with sub-millisecond reads for experiment and feature flag configurations. It supports querying single keys or multiple values via a client SDK. The post highlights its use for A/B testing, feature flags, and dynamic redirects, citing a case study with Speedway Motors showing improved Core Web Vitals and SEO.
  13. Colocate your edge to your data on VercelIntroduces regional execution for Vercel Edge Functions, allowing developers to specify the execution region. This feature aims to reduce latency by colocating compute resources near data sources, thereby avoiding "waterfalls" caused by multiple database queries over long distances. The post contrasts this with global Edge Function deployment and highlights the benefits for data fetching scenarios.
  14. Introducing OG Image Generation: Fast, dynamic social card images at the EdgeIntroduced Vercel OG Image Generation, a new library (`@vercel/og`) for generating dynamic social card images using Edge Functions and WebAssembly. This approach is 5x faster than previous Serverless Function-based solutions by avoiding headless browsers and leveraging a lightweight core engine (Satori). It supports HTML/CSS, Tailwind CSS, and dynamic asset fetching, with automatic Edge caching. The post details the technical challenges of the previous approach (size, speed, cost) and the benefits of the new Edge-based solution, including performance metrics and code examples.
  15. WebAssembly at the EdgeIntroduced support for WebAssembly (Wasm) in Vercel Edge Functions, allowing developers to compile and run code written in languages like Rust, Go, and C directly on the edge. This enables the use of existing libraries and computationally heavy workloads that might be slower or require workarounds in JavaScript. The post highlights performance benefits (2-3x faster for computationally heavy workloads) and provides an example of a maze-solving application demonstrating these improvements.
  16. Vercel Edge Middleware: Dynamic at the speed of static (historical)This post announces the General Availability (GA) of Vercel Edge Middleware for Next.js, following a successful public beta with significant growth and adoption. It highlights the ability of Edge Middleware to run before the Edge Cache, enabling fast rewrites, redirects, and dynamic routing. The post details use cases for experimentation (A/B testing, personalization) and localization at the edge, leveraging built-in geolocation data. It also covers authentication at the edge for improved security and performance. The post notes that for new projects, Vercel Functions with Node.js runtime and Fluid compute are recommended, and Routing Middleware for request-time routing.