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  1. Custom fields raw and transformed values support

    Logs

    Custom Fields now support logging both raw and transformed values for request and response headers in the HTTP requests dataset.

    These fields are configured per zone and apply to all Logpush jobs in that zone that include request headers, response headers. Each header can be logged in only one format—either raw or transformed—not both.

    By default:

    • Request headers are logged as raw values
    • Response headers are logged as transformed values

    These defaults can be overidden to suit your logging needs.

    For more information refer to Custom fields documentation

  1. Read multiple keys from Workers KV with bulk reads

    KV

    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 key
    const key = "key-a";
    const value = await env.NAMESPACE.get(key);
    

    // Read multiple keys
    const keys = ["key-a", "key-b", "key-c", ...] // up to 100 keys
    const 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")}.`)

    Consult the Workers KV Read key-value pairs API for full details on Workers KV's new bulk reads support.

  1. Increased limits for Queues pull consumers

    Queues

    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:

    Create a queue with a pull based consumer
    npx wrangler queues create my-queue
    npx wrangler queues consumer http add my-queue

    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:

    Pull messages from a queue
    curl "https://api.cloudflare.com/client/v4/accounts/${CF_ACCOUNT_ID}/queues/${QUEUE_ID}/messages/pull" \
    --header "Authorization: Bearer ${API_TOKEN}" \
    --header "Content-Type: application/json" \
    --data '{ "visibility_timeout": 10000, "batch_size": 2 }'

    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.

  1. Workers AI for Developer Week - faster inference, new models, async batch API, expanded LoRA support

    Workers AI

    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.
    • @cf/baai/bge-small-en-v1.5, @cf/baai/bge-base-en-v1.5, @cf/baai/bge-large-en-v1.5 get an updated back end, which should improve inference times by 2x.
      • 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.
    • @cf/qwen/qwen2.5-coder-32b-instruct: the current state-of-the-art open-source code LLM, with its coding abilities matching those of GPT-4o.

    Batch Inference

    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:

    Expanded LoRA support

    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:

  1. HTTP redirect and custom block page redirect

    Gateway

    You can now use more flexible redirect capabilities in Cloudflare One with Gateway.

    • A new Redirect action is available in the HTTP policy builder, allowing admins to redirect users to any URL when their request matches a policy. You can choose to preserve the original URL and query string, and optionally include policy context via query parameters.
    • For Block actions, admins can now configure a custom URL to display when access is denied. This block page redirect is set at the account level and can be overridden in DNS or HTTP policies. Policy context can also be passed along in the URL.

    Learn more in our documentation for HTTP Redirect and Block page redirect.

  1. Signed URLs and Infrastructure Improvements on Stream Live WebRTC Beta

    Stream

    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).

    For more information, learn about the Stream Live WebRTC beta.

  1. R2 Data Catalog is a managed Apache Iceberg data catalog built directly into R2 buckets

    R2

    Today, we're launching R2 Data Catalog in open beta, a managed Apache Iceberg catalog built directly into your Cloudflare R2 bucket.

    If you're 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:

    Terminal window
    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.

  1. Cloudflare Pipelines now available in beta

    Pipelines R2 Workers

    Cloudflare Pipelines is now available in beta, to all users with a Workers Paid plan.

    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 pipeline
    $ 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-pipeline
    

    Id:    0e00c5ff09b34d018152af98d06f5a1xvc
    Name:  my-clickstream-pipeline
    Sources:
      HTTP:
        Endpoint:        https://0e00c5ff09b34d018152af98d06f5a1xvc.pipelines.cloudflare.com/
        Authentication:  off
        Format:          JSON
      Worker:
        Format:  JSON
    Destination:
      Type:         R2
      Bucket:       my-bucket
      Format:       newline-delimited JSON
      Compression:  GZIP
    Batch 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.

  1. D1 Read Replication Public Beta

    D1 Workers

    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 header
    const 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 session
    response.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.

    Deploy to Cloudflare

    To learn more about how read replication was implemented, go to our blog post ↗.

  1. Hyperdrive now supports custom TLS/SSL certificates

    Hyperdrive

    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:

    Terminal window
    # Create CA certificate bundle
    npx wrangler cert upload certificate-authority --ca-cert your-ca-cert.pem --name your-custom-ca-name
    

    # Create client certificate pair
    npx wrangler cert upload mtls-certificate --cert client-cert.pem --key client-key.pem --name your-client-cert-name

    Then create a Hyperdrive configuration with the certificates and desired SSL mode:

    Terminal window
    npx wrangler hyperdrive create your-hyperdrive-config \
      --connection-string="postgres://user:password@hostname:port/database" \
      --ca-certificate-id <CA_CERT_ID> \
      --mtls-certificate-id <CLIENT_CERT_ID>
      --sslmode verify-full

    Learn more about configuring SSL/TLS certificates for Hyperdrive to enhance your database security posture.