I am using AWS Lambda for years and always been one of the most convenient ways to run backend code without managing servers. I write a function, deploy it, and AWS takes care of provisioning and scaling the underlying infrastructure which I love a lot.

But Lambda has never been only about CPU and memory. For many workloads, the real bottleneck is the network.

A function might need to download a large file from Amazon S3, call another service (maybe downstream service), process the response, and upload the result somewhere else. In such cases, giving the Lambda more CPU does not necessarily solve the problem if the function is spending most of its execution time waiting … for data to move across the network.

Amazon Web Services has now announced an important improvement in this area: Lambda network bandwidth can scale with the memory allocated to the function, reaching up to 3 Gbps for functions configured with 10 GB of memory.

That sounds like a relatively small infrastructure change, but it can have a meaningful impact on data-intensive serverless applications.

Previous Lambda: More Memory Mostly Meant More Compute

Before looking at the announcement, it is important to understand how Lambda resources work.

When you configure a Lambda function, you choose its memory allocation. The available range is currently 128 MB to 10,240 MB. Memory is also Lambda’s primary control for CPU allocation. As you increase memory, Lambda proportionally provides more CPU capacity.

AWS documentation notes that increasing memory can improve performance for CPU-bound, network-bound, and memory-bound functions.

Example:-

Resources:
  MyFunction:
    Type: AWS::Serverless::Function
    Properties:
      MemorySize: 2048
      Timeout: 30

Here, the function has 2 GB of memory. If the function is CPU-intensive, increasing this to 4 GB or 8 GB may make it execute faster because it receives more CPU resources.

But consider a different application: Imagine your Lambda downloads a 500 MB video from S3, performs some processing, and uploads the result (we had one requirement – for checking video is fake or real).

The code might be perfectly optimized and the function might have plenty of free memory. Yet the invocation can still spend significant time transferring data. That is where network bandwidth becomes important.

In other words, the bottleneck can look like this:

Lambda --> Download 500MB --> AWS S3

Increasing CPU doesn’t necessarily solve a network bottleneck. This is the problem the new change addresses.

The New AWS Lambda Network Bandwidth Announcement

AWS has announced increased network bandwidth for Lambda functions, with bandwidth scaling according to the memory configured for the function.

According to the announcement, Lambda functions can now reach up to 3 Gbps of network bandwidth, with the higher bandwidth available as memory allocation increases. The announced range starts at 625 Mbps at 2 GB of memory and reaches 3 Gbps at 10 GB.

The important idea is not simply “Lambda is now 3 Gbps.” The more useful way to understand it is:

More Lambda memory
        ↓
More compute resources
        +
More network bandwidth
        ↓
Potentially faster execution

This makes Lambda memory an even more important performance tuning parameter. AWS already recommends testing different memory configurations for performance because memory affects CPU and can improve CPU-, I/O-, and network-bound workloads.

So a workload that previously looked like:

2 GB Lambda
    ↓
Network bottleneck
    ↓
Long execution time

could potentially become:

10 GB Lambda
    ↓
Higher network throughput
    ↓
Shorter execution time

There is one important qualification: the announcement concerns network access by the Lambda execution environment. It should not be confused with Lambda response streaming.

Lambda response streaming has separate limits. For streamed responses, the first 6 MB is not subject to the same bandwidth limit, while data after that is currently limited to 2 MB/s. AWS explicitly distinguishes this from network access made by the function itself.

A Real-World Scenario: Image Processing with S3

Let’s take a practical example. Suppose you are building an image-processing application. A user uploads a large image to S3:

User --> S3 -(event)-> Lambda -(download image)-> Image Processing -(upload optimized image)-> S3

The Lambda function could perform operations such as:

For small images, network bandwidth probably isn’t a major concern. But imagine users are uploading 200 MB, 500 MB, or larger files. Now the Lambda function has to move a significant amount of data.

A simplified Python implementation might look like this:

import boto3
from PIL import Image
from io import BytesIO

s3 = boto3.client("s3")

def lambda_handler(event, context):

    bucket = event["bucket"]
    key = event["key"]

    response = s3.get_object(
        Bucket=bucket,
        Key=key
    )

    image_data = response["Body"].read()

    image = Image.open(BytesIO(image_data))
    image.thumbnail((1920, 1080))

    output = BytesIO()
    image.save(output, format="WEBP")

    s3.put_object(
        Bucket=bucket,
        Key=f"processed/{key}.webp",
        Body=output.getvalue(),
        ContentType="image/webp"
    )

    return {
        "statusCode": 200,
        "message": "Image processed successfully"
    }

The processing itself might be fast enough. But the function still has to download the source object and upload the processed object. With higher network throughput, a larger Lambda configuration can potentially reduce the time spent transferring this data.

For example, you might benchmark:

Configuration A
Memory: 2 GB
Network: up to 625 Mbps
Execution: X seconds

Configuration B
Memory: 8 GB
Network: substantially higher
Execution: Y seconds

Configuration C
Memory: 10 GB
Network: up to 3 Gbps
Execution: Z seconds

You should not assume that the 10 GB configuration will automatically be the fastest or cheapest. S3 throughput, application behavior, serialization, image-processing time, concurrency, and other factors can become the new bottleneck. The correct approach is to benchmark the workload.

This Can Also Change the Cost Calculation

There is another interesting aspect here. Lambda pricing is affected by allocated memory and execution duration. Therefore, simply increasing memory is not automatically cheaper.

Suppose a function currently runs for 20 seconds at 2 GB:

2 GB × 20 seconds
= 40 GB-seconds

After increasing memory, imagine it can finish in 5 seconds at 8 GB:

8 GB × 5 seconds
= 40 GB-seconds

The simplified compute consumption is comparable. The real AWS bill depends on the exact pricing model and additional services involved, but this illustrates an important Lambda optimization principle:

Don’t optimize Lambda based only on memory consumption. Optimize for cost per successful unit of work.

If increasing memory dramatically reduces execution time, the higher-memory configuration can sometimes provide better performance without a proportional increase in cost.

AWS itself recommends measuring different memory configurations rather than blindly selecting the smallest possible configuration. Tools such as AWS Lambda Power Tuning can be used to test different memory settings against a real workload.

When Should You Care About This?

This announcement is particularly relevant if your Lambda functions are network-heavy. Examples include:

S3 → Lambda → S3

for file transformation,

API → Lambda → External API

for large API payloads, or:

S3 → Lambda → ML processing → S3

for data and AI pipelines.

It is less significant for a conventional CRUD API where Lambda exchanges a few kilobytes with DynamoDB or another backend. Also remember that Lambda still has its normal architectural boundaries. A single invocation can run for a maximum of 15 minutes, and function memory can be configured up to 10,240 MB. So this doesn’t mean Lambda has suddenly replaced EC2, ECS, or AWS Batch for every high-throughput workload.

It simply makes Lambda a more capable option for workloads where transferring data was previously part of the performance problem.

Conclusion

The most interesting part of AWS’s Lambda network bandwidth announcement isn’t the number “3 Gbps.” It is the direction AWS is taking.

Lambda memory has traditionally been treated as a way to control compute resources. Now, for eligible workloads, increasing memory can also provide significantly more network capacity.

That gives developers another performance lever:

Lambda memory
↓
CPU + memory + network capacity
↓
Potentially shorter execution time
↓
Better throughput

For a simple API Lambda, you may never notice the difference. For workloads processing large S3 objects, images, datasets, API responses, or other network-heavy data, however, this can be significant.

The practical takeaway is simple: if your Lambda function is network-bound, don’t automatically assume that reducing memory is the best optimization. Test higher memory configurations and measure execution time, throughput, and total cost.

The best Lambda configuration isn’t necessarily the one with the least memory. It is the one that completes your workload efficiently at the right cost.

Read the original AWS announcement

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