Vercel Queues is a durable event streaming system built with Fluid compute, and is now available in public beta for all teams. Vercel Queues also powers Workflow: use Queues for direct message publishing and consumption, Workflow for ergonomic multi step orchestration.

Functions need a reliable way to defer expensive work and guarantee that tasks complete even when functions crash or new deployments roll out. Queues makes it simple to process messages asynchronously with automatic retries and delivery guarantees, providing at-least-once delivery semantics.

How it works:

• Messages are sent to a durable topic

• The queue fans messages out to subscribed consumer groups.

• Each consumer group processes messages independently.

• The queue redelivers messages to consumer groups until successfully processed or expired.

Publish messages from any route handler:

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import { send } from '@vercel/queue';
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export async function POST(request: Request) {
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  const order = await request.json();
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  const { messageId } = await send('orders', order);
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  return Response.json({ messageId });
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}

Create a consumer:

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import { handleCallback } from '@vercel/queue';
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export const POST = handleCallback(async (order, metadata) => {
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  console.log('Fulfilling order', metadata.messageId, order);
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  // await doAnythingAsync(order);
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});

Configure the consumer group:

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{
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  "functions": {
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    "app/api/queues/fulfill-order/route.ts": {
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      "experimentalTriggers": [{ "type": "queue/v2beta", "topic": "orders" }]
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    }
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  }
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}

Adding a trigger makes the route private: it has no public URL and only Vercel's queue infrastructure can invoke it.

Vercel Queues is billed per API operation, starting at $0.60 per 1M operations, and includes:

• Multiple AZ synchronous replication

• At-least-once delivery

• Customizable visibility timeout

• Delayed delivery

• Idempotency keys

• Concurrency control

• Per-deployment topic partitioning

Functions invoked by Queues in push mode are charged at existing Fluid compute rates.

Get started with the Queues documentation.

Chat SDK now supports Telegram, extending its single-codebase approach to Slack, Discord, GitHub, and Teams, with the new Telegram adapter.

Teams can build bots that support mentions, message reactions, direct messages, and typing indicators.

The adapter handles single file uploads and renders basic text cards, with buttons and link buttons that display as inline keyboard elements, allowing developers to create interactive workflows directly within Telegram chats.

Get start with Telegram adapter setup:

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import { Chat } from "chat";
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import { createTelegramAdapter } from "@chat-adapter/telegram";
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const bot = new Chat({
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  userName: "mybot",
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  adapters: {
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    telegram: createTelegramAdapter(),
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  },
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});
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bot.onNewMention(async (thread, message) => {
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  await thread.post(`You said: ${message.text}`);
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});

Telegram does not expose full historical message APIs to bots, so message history relies on adapter-level caching. Additionally, callback data is limited to 64 bytes, and the platform does not currently support modals or ephemeral messages.

Read the documentation to get started.

Pro teams can now assign the Developer role to their members. Previously only available for Enterprise teams, the Developer role gives Pro teams more granular access control.

Developers can safely deploy to projects on a team, with more limited team-wide configuration control and environment variables visibility.

Owners can assign the Developer role to any existing seat or invite new members from the team members settings.

Learn more about team level roles.

The new dashboard navigation is now the default experience for all Vercel users.

Following a successful opt-in beta release in January it has now rolled out fully as of February 26, 2026, with several improvements made based on feedback.

The redesigned navigation includes:

• New sidebar with horizontal tabs moved to a resizable sidebar that can be hidden when not needed

• Consistent tabs for unified navigations across both team and project levels

• Improved order with navigation items prioritized the most common developer workflows

• Projects as filters so you can switch between team and project versions of the same page in one click

• Optimized for mobile with floating bottom bar optimized for one-handed use

No action is required. The new navigation is available to all users automatically.

Open your dashboard to see the updated experience.

Gemini 3.1 Flash Image Preview (Nano Banana 2) is now available on AI Gateway.

This release improves visual quality while maintaining the generation speed and cost of flash-tier models.

Nano Banana 2 can use Google Image Search to ground outputs in real-world imagery. This helps with rendering lesser-known landmarks and objects by retrieving live visual data. This model also introduces configurable thinking levels (Minimal and High) to let the model reason through complex prompts before rendering. New resolutions and new aspect ratios (512p, 1:4 and 1:8) are available alongside the existing options to expand to support more types of creative assets.

To use this model, set model to google/gemini-3.1-flash-image-preview in the AI SDK. Nano Banana 2 is a multimodal model. Use `streamText` or `generateText` to generate images alongside text responses. This example shows how the model can use web search to find live data.

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import { streamText } from 'ai';
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const result = streamText({
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  model: 'google/gemini-3.1-flash-image-preview',
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  providerOptions: {
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    google: { responseModalities: ['TEXT', 'IMAGE'] },
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  },
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  prompt: 'Generate an image of the 2026 Super Bowl at golden hour',
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});

You can also change the thinking level: in this example, thinking is set to high for a more thorough response.

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import { streamText } from 'ai';
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const result = streamText({
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  model: 'google/gemini-3.1-flash-image-preview',
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  providerOptions: {
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    google: {
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      responseModalities: ['TEXT', 'IMAGE'],
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      thinkingConfig: {
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        includeThoughts: true,
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        thinkingLevel: 'high',
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      },
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    },
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  },
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  prompt:
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   `An exploded view diagram of a modern GPU, showing the die, HBM stacks, interposer,
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    and cooling solution as separate floating layers with labeled callouts.`,
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});

AI Gateway provides a unified API for calling models, tracking usage and cost, and configuring retries, failover, and performance optimizations for higher-than-provider uptime. It includes built-in observability, Bring Your Own Key support, and intelligent provider routing with automatic retries.

Learn more about AI Gateway, view the AI Gateway model leaderboard or try it in our model playground.

The activity log now captures every change made to your team and project settings, giving you complete visibility into who changed what and when.

Previously, some settings changes went untracked. With 88 new events added, activity log coverage is now 100%, so no action goes unrecorded.

Try it out or learn more about the activity log.

The bundle size limit for Vercel Functions using the Python runtime is now 500MB, increasing the maximum uncompressed deployment bundle size from 250MB.

Learn more in the functions limitations documentation, or deploy FastAPI or Flask on Vercel to get started.

GPT 5.3 Codex is now available on AI Gateway. GPT 5.3 Codex brings together the coding strengths of GPT-5.2-Codex and the reasoning depth of GPT-5.2 in a single model that's 25% faster and more token-efficient.

Built for long-running agentic work, the model handles research, tool use, and multi-step execution across the full software lifecycle, from debugging and deployment to product documents and data analysis. Additionally, you can steer it mid-task without losing context. For web development, it better understands underspecified prompts and defaults to more functional, production-ready output.

To use this model, set model to openai/gpt-5.3-codex in the AI SDK.

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import { streamText } from 'ai';
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const result = streamText({
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  model: 'openai/gpt-5.3-codex',
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  prompt:
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    `Research our current API architecture, identify performance
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     bottlenecks, refactor the slow endpoints, add monitoring,
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     and deploy the changes to staging.`,
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});

AI Gateway provides a unified API for calling models, tracking usage and cost, and configuring retries, failover, and performance optimizations for higher-than-provider uptime. It includes built-in observability, Bring Your Own Key support, and intelligent provider routing with automatic retries.

Learn more about AI Gateway, view the AI Gateway model leaderboard or try it in our model playground.