Hyperdrive has been approved for FedRAMP Authorization and is now available in the FedRAMP Marketplace ↗.
FedRAMP is a U.S. government program that provides standardized assessment and authorization for cloud products and services. As a result of this product update,
Hyperdrive has been approved as an authorized service to be used by U.S. federal agencies at the Moderate Impact level.
We are adding source origin restrictions to
the Media Transformations beta. This allows customers to restrict what sources
can be used to fetch images and video for transformations. This feature is the
same as --- and uses the same settings as ---
Image Transformations sources.
When transformations is first enabled, the default setting only allows
transformations on images and media from the same website or domain being used to make
the transformation request. In other words, by default, requests to
example.com/cdn-cgi/media can only reference originals on example.com.
Adding access to other sources, or allowing any source,
is easy to do
in the Transformations tab under Stream. Click each domain enabled for
Transformations and set its sources list to match the needs of your content. The
user making this change will need permission to edit zone settings.
You can now publish messages to Cloudflare Queues directly via HTTP from any service or programming language that supports sending HTTP requests. Previously, publishing to queues was only possible from within Cloudflare Workers. You can already consume from queues via Workers or HTTP pull consumers, and now publishing is just as flexible.
Publishing via HTTP requires a Cloudflare API token with Queues Edit permissions for authentication. Here's a simple example:
This can reduce memory leaks when using WebAssembly-based Workers, which includes Python Workers and Rust Workers. The FinalizationRegistry works by enabling toolchains such as Emscripten ↗ and wasm-bindgen ↗ to automatically free WebAssembly heap allocations. If you are using WASM and seeing Exceeded Memory errors and cannot determine a cause using memory profiling, you may want to enable the FinalizationRegistry.
For more information refer to the enable_weak_ref compatibility flag documentation.
Earlier this year, we announced the launch of the new Terraform v5 Provider. Unlike the earlier Terraform providers, v5 is automatically generated based on the OpenAPI Schemas for our REST APIs. Since launch, we have seen an unexpectedly high number of issues ↗ reported by customers. These issues currently impact about 15% of resources. We have been working diligently to address these issues across the company, and have released the v5.4.0 release which includes a number of bug fixes. Please keep an eye on this changelog for more information about upcoming releases.
Changes
Removes the worker_platforms_script_secret resource from the provider (see migration guide ↗ for alternatives—applicable to both Workers and Workers for Platforms)
Removes duplicated fields in cloudflare_cloud_connector_rules resource
If you are evaluating a move from v4 to v5, please make use of the migration guide ↗. We have provided automated migration scripts using Grit which simplify the transition, although these do not support implementations which use Terraform modules, so customers making use of modules need to migrate manually. Please make use of terraform plan to test your changes before applying, and let us know if you encounter any additional issues either by reporting to our GitHub repository ↗, or by opening a support ticket ↗.
We're excited to announce several improvements to the Cloudflare R2 dashboard experience that make managing your object storage easier and more intuitive:
All-new settings page
We've redesigned the bucket settings page, giving you a centralized location to manage all your bucket configurations in one place.
Improved navigation and sharing
Deeplink support for prefix directories: Navigate through your bucket hierarchy without losing your state. Your browser's back button now works as expected, and you can share direct links to specific prefix directories with teammates.
Objects as clickable links: Objects are now proper links that you can copy or CMD + Click to open in a new tab.
Clearer public access controls
Renamed "r2.dev domain" to "Public Development URL" for better clarity when exposing bucket contents for non-production workloads.
Public Access status now clearly displays "Enabled" when your bucket is exposed to the internet (via Public Development URL or Custom Domains).
We've also made numerous other usability improvements across the board to make your R2 experience smoother and more productive.
You can now create Python Workers which are executed via a cron trigger.
This is similar to how it's done in JavaScript Workers, simply define a scheduled event
listener in your Worker:
from workers import handler@handlerasync def on_scheduled(event, env, ctx): print("cron processed")
Define a cron trigger configuration in your Wrangler configuration file:
{ "triggers": { // Schedule cron triggers: // - At every 3rd minute // - At 15:00 (UTC) on first day of the month // - At 23:59 (UTC) on the last weekday of the month "crons": [ "*/3 * * * *", "0 15 1 * *", "59 23 LW * *" ] }}
You can now filter AutoRAG search results by folder and timestamp using metadata filtering to narrow down the scope of your query.
This makes it easy to build multitenant experiences where each user can only access their own data. By organizing your content into per-tenant folders and applying a folder filter at query time, you ensure that each tenant retrieves only their own documents.
const response = await env.AI.autorag("my-autorag").search({ query: "When did I sign my agreement contract?", filters: { type: "eq", key: "folder", value: "customer-a/contracts/", },});
You can use metadata filtering by creating a new AutoRAG or reindexing existing data. To reindex all content in an existing AutoRAG, update any chunking setting and select Sync index. Metadata filtering is available for all data indexed on or after April 21, 2025.
Queues pull consumers can now pull and acknowledge up to 5,000 messages / second per queue. Previously, pull consumers were rate limited to 1,200 requests / 5 minutes, aggregated across all queues.
Pull consumers allow you to consume messages over HTTP from any environment—including outside of Cloudflare Workers. They’re also useful when you need fine-grained control over how quickly messages are consumed.
To setup a new queue with a pull based consumer using Wrangler, run:
You can also configure a pull consumer using the REST API or the Queues dashboard.
Once configured, you can pull messages from the queue using any HTTP client. You'll need a Cloudflare API Token with queues_read and queues_write permissions. For example:
To learn more about how to acknowledge messages, pull batches at once, and setup multiple consumers, refer to the pull consumer documentation.
As always, Queues doesn't charge for data egress. Pull operations continue to be billed at the existing rate, of $0.40 / million operations. The increased limits are available now, on all new and existing queues. If you're new to Queues, get started with the Cloudflare Queues guide.
You can now retrieve up to 100 keys in a single bulk read request made to Workers KV using the binding.
This makes it easier to request multiple KV pairs within a single Worker invocation. Retrieving many key-value pairs using the bulk read operation is more performant than making individual requests since bulk read operations are not affected by Workers simultaneous connection limits.
// Read single keyconst key = "key-a";const value = await env.NAMESPACE.get(key);// Read multiple keysconst keys = ["key-a", "key-b", "key-c", ...] // up to 100 keysconst values : Map<string, string?> = await env.NAMESPACE.get(keys);// Print the value of "key-a" to the console.console.log(`The first key is ${values.get("key-a")}.`)
Previously, a request to the Workers Create Route API always returned null for "script" and an empty string for "pattern" even if the request was successful.
The Workers and Workers for Platforms secrets APIs are now properly documented in the Cloudflare OpenAPI docs. Previously, these endpoints were not publicly documented, leaving users confused on how to directly manage their secrets via the API. Now, you can find the proper endpoints in our public documentation, as well as in our API Library SDKs such as cloudflare-typescript ↗ (>4.2.0) and cloudflare-python ↗ (>4.1.0).
Note the cloudflare_workers_secret and cloudflare_workers_for_platforms_script_secretTerraform resources ↗ are being removed in a future release. This resource is not recommended for managing secrets. Users should instead use the:
Secrets Store with the "Secrets Store Secret" binding on Workers and Workers for Platforms Script Upload
Cloudflare Stream has completed an infrastructure upgrade for our Live WebRTC beta support which brings increased scalability and improved playback performance to all customers. WebRTC allows broadcasting directly from a browser (or supported WHIP client) with ultra-low latency to tens of thousands of concurrent viewers across the globe.
Additionally, as part of this upgrade, the WebRTC beta now supports Signed URLs to protect playback, just like our standard live stream options (HLS/DASH).
Happy Developer Week 2025! Workers AI is excited to announce a couple of new features and improvements available today. Check out our blog ↗ for all the announcement details.
Faster inference + New models
We’re rolling out some in-place improvements to our models that can help speed up inference by 2-4x! Users of the models below will enjoy an automatic speed boost starting today:
@cf/meta/llama-3.3-70b-instruct-fp8-fast gets a speed boost of 2-4x, leveraging techniques like speculative decoding, prefix caching, and an updated inference backend.
With the bge models, we’re also announcing a new parameter called pooling which can take cls or mean as options. We highly recommend using pooling: cls which will help generate more accurate embeddings. However, embeddings generated with cls pooling are not backwards compatible with mean pooling. For this to not be a breaking change, the default remains as mean pooling. Please specify pooling: cls to enjoy more accurate embeddings going forward.
We’re also excited to launch a few new models in our catalog to help round out your experience with Workers AI. We’ll be deprecating some older models in the future, so stay tuned for a deprecation announcement. Today’s new models include:
@cf/mistralai/mistral-small-3.1-24b-instruct: a 24B parameter model achieving state-of-the-art capabilities comparable to larger models, with support for vision and tool calling.
@cf/google/gemma-3-12b-it: well-suited for a variety of text generation and image understanding tasks, including question answering, summarization and reasoning, with a 128K context window, and multilingual support in over 140 languages.
@cf/qwen/qwq-32b: a medium-sized reasoning model, which is capable of achieving competitive performance against state-of-the-art reasoning models, e.g., DeepSeek-R1, o1-mini.
Introducing a new batch inference feature that allows you to send us an array of requests, which we will fulfill as fast as possible and send them back as an array. This is really helpful for large workloads such as summarization, embeddings, etc. where you don’t have a human-in-the-loop. Using the batch API will guarantee that your requests are fulfilled eventually, rather than erroring out if we don’t have enough capacity at a given time.
Check out the tutorial to get started! Models that support batch inference today include:
We’ve upgraded our LoRA experience to include 8 newer models, and can support ranks of up to 32 with a 300MB safetensors file limit (previously limited to rank of 8 and 100MB safetensors) Check out our LoRAs page to get started. Models that support LoRAs now include:
D1 read replication is available in public beta to help lower average latency and increase overall throughput for read-heavy applications like e-commerce websites or content management tools.
Workers can leverage read-only database copies, called read replicas, by using D1 Sessions API. A session encapsulates all the queries from one logical session for your application. For example, a session may correspond to all queries coming from a particular web browser session. With Sessions API, D1 queries in a session are guaranteed to be sequentially consistent to avoid data consistency pitfalls. D1 bookmarks can be used from a previous session to ensure logical consistency between sessions.
// retrieve bookmark from previous session stored in HTTP headerconst bookmark = request.headers.get("x-d1-bookmark") ?? "first-unconstrained";const session = env.DB.withSession(bookmark);const result = await session .prepare(`SELECT * FROM Customers WHERE CompanyName = 'Bs Beverages'`) .run();// store bookmark for a future sessionresponse.headers.set("x-d1-bookmark", session.getBookmark() ?? "");
Read replicas are automatically created by Cloudflare (currently one in each supported D1 region), are active/inactive based on query traffic, and are transparently routed to by Cloudflare at no additional cost.
To checkout D1 read replication, deploy the following Worker code using Sessions API, which will prompt you to create a D1 database and enable read replication on said database.
To learn more about how read replication was implemented, go to our blog post ↗.
Pipelines let you ingest high volumes of real time data, without managing the underlying infrastructure. A single pipeline can ingest up to 100 MB of data per second, via HTTP or from a Worker. Ingested data is automatically batched, written to output files, and delivered to an R2 bucket in your account. You can use Pipelines to build a data lake of clickstream data, or to store events from a Worker.
Create your first pipeline with a single command:
Create a pipelinebash
$ npx wrangler@latest pipelines create my-clickstream-pipeline --r2-bucket my-bucket🌀 Authorizing R2 bucket "my-bucket"🌀 Creating pipeline named "my-clickstream-pipeline"✅ Successfully created pipeline my-clickstream-pipelineId: 0e00c5ff09b34d018152af98d06f5a1xvcName: my-clickstream-pipelineSources: HTTP: Endpoint: https://0e00c5ff09b34d018152af98d06f5a1xvc.pipelines.cloudflare.com/ Authentication: off Format: JSON Worker: Format: JSONDestination: Type: R2 Bucket: my-bucket Format: newline-delimited JSON Compression: GZIPBatch hints: Max bytes: 100 MB Max duration: 300 seconds Max records: 100,000🎉 You can now send data to your pipeline!Send data to your pipeline's HTTP endpoint:curl "https://0e00c5ff09b34d018152af98d06f5a1xvc.pipelines.cloudflare.com/" -d '[{ ...JSON_DATA... }]'To send data to your pipeline from a Worker, add the following configuration to your config file:{ "pipelines": [ { "pipeline": "my-clickstream-pipeline", "binding": "PIPELINE" } ]}
Head over to our getting started guide for an in-depth tutorial to building with Pipelines.
Today, we are launching R2 Data Catalog in open beta, a managed Apache Iceberg catalog built directly into your Cloudflare R2 bucket.
If you are not already familiar with it, Apache Iceberg ↗ is an open table format designed to handle large-scale analytics datasets stored in object storage, offering ACID transactions and schema evolution. R2 Data Catalog exposes a standard Iceberg REST catalog interface, so you can connect engines like Spark, Snowflake, and PyIceberg to start querying your tables using the tools you already know.
To enable a data catalog on your R2 bucket, find R2 Data Catalog in your buckets settings in the dashboard, or run:
npx wrangler r2 bucket catalog enable my-bucket
And that's it. You'll get a catalog URI and warehouse you can plug into your favorite Iceberg engines.
Visit our getting started guide for step-by-step instructions on enabling R2 Data Catalog, creating tables, and running your first queries.
Hyperdrive now supports more SSL/TLS security options for your database connections:
Configure Hyperdrive to verify server certificates with verify-ca or verify-full SSL modes and protect against man-in-the-middle attacks
Configure Hyperdrive to provide client certificates to the database server to authenticate itself (mTLS) for stronger security beyond username and password
Use the new wrangler cert commands to create certificate authority (CA) certificate bundles or client certificate pairs:
Cloudflare Secrets Store is available today in Beta. You can now store, manage, and deploy account level secrets from a secure, centralized platform to your Workers.
To spin up your Cloudflare Secrets Store, simply click the new Secrets Store tab in the dashboard ↗ or use this Wrangler command:
wrangler secrets-store store create <name> --remote
The following are supported in the Secrets Store beta:
Secrets Store UI & API: create your store & create, duplicate, update, scope, and delete a secret
Workers UI: bind a new or existing account level secret to a Worker and deploy in code
Wrangler: create your store & create, duplicate, update, scope, and delete a secret
Account Management UI & API: assign Secrets Store permissions roles & view audit logs for actions taken in Secrets Store core platform
The Overview tab shows logs from all your Workers in one place. The Invocations view groups logs together by invocation, which refers to the specific trigger that started the execution of the Worker (i.e. fetch). The Events view shows logs in the order they were produced, based on timestamp. Previously, you could only view logs for a single Worker.
The Investigate tab presents a Query Builder, which helps you write structured queries to investigate and visualize your logs. The Query Builder can help answer questions such as:
Which paths are experiencing the most 5XX errors?
What is the wall time distribution by status code for my Worker?
What are the slowest requests, and where are they coming from?
Who are my top N users?
The Query Builder can use any field that you store in your logs as a key to visualize, filter, and group by. Use the Query Builder to quickly access your data, build visualizations, save queries, and share them with your team.
Workers Logs is now Generally Available
Workers Logs is now Generally Available. With a small change to your Wrangler configuration, Workers Logs ingests, indexes, and stores all logs emitted from your Workers for up to 7 days.
We've introduced a number of changes during our beta period, including:
Dashboard enhancements with customizable fields as columns in the Logs view and support for invocation-based grouping
Performance improvements to ensure no adverse impact
The API documents three endpoints: list the keys in the telemetry dataset, run a query, and list the unique values for a key. For more, visit our REST API documentation ↗.
Visit the docs to learn more about the capabilities and methods exposed by the Query Builder. Start using Workers Logs and the Query Builder today by enabling observability for your Workers:
For Workers Logpush, CPU and Wall time are surfaced at the top level of the Workers Trace Events object. All new jobs will have these new fields included by default. Existing jobs need to be updated to include CPU time and Wall time.
You can use a Workers Logs filter to search for logs where Wall time exceeds 100ms.
You can also use the Workers Observability Query Builder ↗ to find the median CPU time and median Wall time for all of your Workers.
Email Workers enables developers to programmatically take action on anything that hits their email inbox. If you're building with Email Workers, you can now test the behavior of an Email Worker script, receiving, replying and sending emails in your local environment using wrangler dev.
Below is an example that shows you how you can receive messages using the email() handler and parse them using postal-mime ↗:
import * as PostalMime from "postal-mime";export default { async email(message, env, ctx) { const parser = new PostalMime.default(); const rawEmail = new Response(message.raw); const email = await parser.parse(await rawEmail.arrayBuffer()); console.log(email); },};
Now when you run npx wrangler dev, wrangler will expose a local /cdn-cgi/handler/email endpoint that you can POST email messages to and trigger your Worker's email() handler:
curl -X POST 'http://localhost:8787/cdn-cgi/handler/email' \ --url-query 'from=sender@example.com' \ --url-query 'to=recipient@example.com' \ --header 'Content-Type: application/json' \ --data-raw 'Received: from smtp.example.com (127.0.0.1) by cloudflare-email.com (unknown) id 4fwwffRXOpyR for <recipient@example.com>; Tue, 27 Aug 2024 15:50:20 +0000From: "John" <sender@example.com>Reply-To: sender@example.comTo: recipient@example.comSubject: Testing Email Workers Local DevContent-Type: text/html; charset="windows-1252"X-Mailer: CurlDate: Tue, 27 Aug 2024 08:49:44 -0700Message-ID: <6114391943504294873000@ZSH-GHOSTTY>Hi there'
Local development is a critical part of the development flow, and also works for sending, replying and forwarding emails. See our documentation for more information.
Hyperdrive is now available on the Free plan of Cloudflare Workers, enabling you to build Workers that connect to PostgreSQL or MySQL databases without compromise.
Low-latency access to SQL databases is critical to building full-stack Workers applications. We want you to be able to build on fast, global apps on Workers,
regardless of the tools you use. So we made Hyperdrive available for all, to make it easier to build Workers that connect to PostgreSQL and MySQL.
If you want to learn more about how Hyperdrive works, read the deep dive ↗ on how Hyperdrive can make your database queries up to 4x faster.
Visit the docs to get started with Hyperdrive for PostgreSQL or MySQL.
Hyperdrive now supports connecting to MySQL and MySQL-compatible databases, including Amazon RDS and Aurora MySQL, Google Cloud SQL for MySQL, Azure Database for MySQL, PlanetScale and MariaDB.
Hyperdrive makes your regional, MySQL databases fast when connecting from Cloudflare Workers. It eliminates unnecessary network roundtrips during connection setup, pools database connections globally, and can cache query results to provide the fastest possible response times.
Best of all, you can connect using your existing drivers, ORMs, and query builders with Hyperdrive's secure credentials, no code changes required.
You can now add a Deploy to Cloudflare button to the README of your Git repository containing a Workers application — making it simple for other developers to quickly set up and deploy your project!
The Deploy to Cloudflare button:
Creates a new Git repository on your GitHub/ GitLab account: Cloudflare will automatically clone and create a new repository on your account, so you can continue developing.
Automatically provisions resources the app needs: If your repository requires Cloudflare primitives like a Workers KV namespace, a D1 database, or an R2 bucket, Cloudflare will automatically provision them on your account and bind them to your Worker upon deployment.
Configures Workers Builds (CI/CD): Every new push to your production branch on your newly created repository will automatically build and deploy courtesy of Workers Builds.
To create a Deploy to Cloudflare button in your README, you can add the following snippet, including your Git repository URL:
[](https://deploy.workers.cloudflare.com/?url=<YOUR_GIT_REPO_URL>)
Check out our documentation for more information on how to set up a deploy button for your application and best practices to ensure a successful deployment for other developers.
