AWS Lambda vs Google Cloud Dataflow comparison

The compared Amazon Web Services (AWS) and Google solutions aren't in the same category. Amazon Web Services (AWS) is ranked #1 in CS , with an average rating of 8.4, and holds a 21.3% mindshare in the category. Google is ranked #7 in SA , with an average rating of 8.0, and holds a 7.1% mindshare. Additionally, 94% of Amazon Web Services (AWS) users are willing to recommend the solution, compared to 92% of Google users who would recommend it.

AWS Lambda

Read 85 AWS Lambda reviews

4,528 Views
2,369 Comparison Views

94% willing to recommend

Google Cloud Dataflow

Read 13 Google Cloud Dataflow reviews

3,122 Views
2,513 Comparison Views

92% willing to recommend

AWS Lambda

Google Cloud Dataflow

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Executive Summary

We performed a comparison between AWS Lambda and Google Cloud Dataflow based on real PeerSpot user reviews.

Find out what your peers are saying about Amazon Web Services (AWS), Apache, Zadara and others in Compute Service.

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Buyer's Guide

Compute Service

March 2025

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Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Categories and Ranking

AWS Lambda

Average Rating

8.4

Reviews Sentiment

7.5

Number of Reviews

Ranking in other categories

Compute Service (1st)

Google Cloud Dataflow

Average Rating

8.0

Reviews Sentiment

7.3

Number of Reviews

Ranking in other categories

Streaming Analytics (7th)

Mindshare comparison

AWS Lambda and Google Cloud Dataflow aren’t in the same category and serve different purposes. AWS Lambda is designed for Compute Service and holds a mindshare of 21.3%, up 21.4% compared to last year.
Google Cloud Dataflow, on the other hand, focuses on Streaming Analytics, holds 7.1% mindshare, down 7.2% since last year.

Compute Service

Streaming Analytics

Featured Reviews

Wai L Lin O

Cloud Engineer - Supervisor at KBZ Bank

A serverless solution with easy integration features

We use AWS Lambda because it provides a solution for our needs without requiring us to manage our infrastructure. With the tool, we only pay for the resources we use. Additionally, it is straightforward to implement and integrates with other services like API Gateway. The tool's serverless nature has had the most significant impact on our workflow. I find it particularly attractive because it eliminates the need for managing servers. In my previous experience, managing upgrades and updates was quite challenging. The solution's integration process with other AWS services was relatively easy. We primarily use AWS services such as EventBridge for scheduling processes and log management.

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Jana Polianskaja

Data Engineer at Accenture

Build Scalable Data Pipelines with Apache Beam and Google Cloud Dataflow

As a data engineer, I find several features of Google Cloud Dataflow particularly valuable. The ability to test solutions locally using Direct Runner is crucial for development, allowing me to validate pipelines without incurring the costs of full Dataflow jobs. The unified programming model for both batch and streaming processing is exceptional - requiring only minor code adjustments to optimize for either mode. This flexibility extends to language support, with robust implementations in both Java and Python, allowing teams to leverage their existing expertise. The platform's comprehensive monitoring capabilities are another standout feature. The intuitive interface, Grafana integration, and extensive service connectivity make troubleshooting and performance tracking highly efficient. Furthermore, seamless integration with Google Cloud Composer (managed Airflow) enables sophisticated orchestration of data pipelines.

Read full review

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros

"We moved our users into the Amazon Cognito pool, so it helps us to standardize our security practices, approaches, etc. We can customize Lambda for authentication to integrate it with API Gateway and other services."

"The main features of this solution are the ability to integrate multiple AWS applications or external applications very quickly and organize all of them. Additionally, it is easy to use and you can run various programming languages, such as Python, Go, and Java."

"We use AWS Lambda because it provides a solution for our needs without requiring us to manage our infrastructure. With the tool, we only pay for the resources we use. Additionally, it is straightforward to implement and integrates with other services like API Gateway."

"It makes configurations more convenient as changes can be made through the environmental variables without altering the main code."

"We are building a Twitter-like application in the boot camp. I have used Lamda for the integration of the post-confirmation page in the application. This will help you get your one-time password via mail. You can log in with the help of a post-confirmation page. We didn’t want to setup an instance specifically for confirmation. We used the Lambda function so that it goes back to sleep after pushing up."

"The feature I found most valuable about Lambda is the fact that it's serverless."

"You can spin up anything instantly without any investment."

"One of the most valuable features of AWS Lambda is the performance. Lambda is very technical and has very high performance, as well as good real-time performance."

More AWS Lambda pros

"The most valuable features of Google Cloud Dataflow are the integration, it's very simple if you have the complete stack, which we are using. It is overall very easy to use, user-friendly friendly, and cost-effective if you know how to use it. The solution is very flexible for programmers, if you know how to do scripts or program in Python or any other language, it's extremely easy to use."

"The most valuable features of Google Cloud Dataflow are scalability and connectivity."

"It is a scalable solution."

"The support team is good and it's easy to use."

"Google's support team is good at resolving issues, especially with large data."

"The service is relatively cheap compared to other batch-processing engines."

"Google Cloud Dataflow is useful for streaming and data pipelines."

"It allows me to test solutions locally using runners like Direct Runner without having to start a Dataflow job, which can be costly."

More Google Cloud Dataflow pros

Cons

"Lambda has limitations on the amount of memory you can use and is not a good solution for long running processes."

"Lambda would benefit from a debugging feature as well."

"The deployment process is a bit complex, so it could be simplified to make it easier for beginners to deploy."

"One area of improvement is to include support for more programming languages. AWS Lambda does not support a lot of programming languages. You have to write the Lambda functions in a certain programming language. We are using C++. My developer knows a couple of other languages. Python is his favorite language, but Python is not supported in AWS Lambda."

"We face some problems with the event-driven execution model."

"My engineers work with it on a daily basis. I just don't have enough depth of knowledge about what kinds of edge cases they may have tried and found lacking. There may be some issues with some language support at one point or another because we couldn't get the underlying libraries in there. A lot of what we do is either in JavaScript, Python, or some of the non-compiled languages. I'm not sure if we've ever tried building a C# solution, for instance, in Lambda or a Java solution in Lambda. It doesn't mean those aren't its capabilities. I would rather refer to my engineers for where the boundaries are."

"AWS Lambda's GUI could be improved with a twist or tweak in its look and feel to make it more impressive."

"I wish to see better execution time in the next release."

More AWS Lambda cons

"When I deploy the product in local errors, a lot of errors pop up which are not always caught. The solution's error logging is bad. It can take a lot of time to debug the errors. It needs to have better logs."

"They should do a market survey and then make improvements."

"Occasionally, dealing with a huge volume of data causes failure due to array size."

"The authentication part of the product is an area of concern where improvements are required."

"The deployment time could also be reduced."

"I would like Google Cloud Dataflow to be integrated with IT data flow and other related services to make it easier to use as it is a complex tool."

"Promoting the technology more broadly would help increase its adoption."

"The solution's setup process could be more accessible."

More Google Cloud Dataflow cons

Pricing and Cost Advice

"This is a product that is pay-per-use, as opposed to a licensing fee."

"You're not paying for a server if you're not using it, which is another reason I like it. So, you're not paying if you're not using it. It scales, and you're charged based on usage. It all depends on the use case. Some can be extremely inexpensive if you have very low volume transaction rates. That way, you don't have to fire up and absorb the cost of the servers just sitting there waiting for a transaction to come through. You're only paying when you use it. So, depending upon the use model, Lambda could be highly efficient relative to an EC2 solution. You don't have to have things reallocated."

"The fees are volume-based."

"AWS Lambda is inexpensive."

"For licensing, we pay a yearly subscription."

"Lambda is an affordable solution. They offer free requests every month and charge per the compute time. If you are working in a big organization, usually AWS offer a savings plan where you get approximately 70% discount on pricing."

"The solution is free of cost for the first year, and after that, it becomes expensive."

"It costs maybe less than $10 per month in my use case."

More AWS Lambda pricing and cost advice

"On a scale from one to ten, where one is cheap, and ten is expensive, I rate the solution's pricing a seven to eight out of ten."

"Google Cloud is slightly cheaper than AWS."

"On a scale from one to ten, where one is cheap, and ten is expensive, I rate Google Cloud Dataflow's pricing a four out of ten."

"The solution is cost-effective."

"Google Cloud Dataflow is a cheap solution."

"The solution is not very expensive."

"The tool is cheap."

"The price of the solution depends on many factors, such as how they pay for tools in the company and its size."

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Top Industries

By visitors reading reviews

Educational Organization

67%

Financial Services Firm

Computer Software Company

Manufacturing Company

Financial Services Firm

17%

Manufacturing Company

12%

Retailer

11%

Computer Software Company

10%

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

Questions from the Community

Which is better, AWS Lambda or Batch?

AWS Lambda is a serverless solution. It doesn’t require any infrastructure, which allows for cost savings. There is no setup process to deal with, as the entire solution is in the cloud. If you use...

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What do you like most about AWS Lambda?

The tool scales automatically based on the number of incoming requests.

See all answers

What is your experience regarding pricing and costs for AWS Lambda?

AWS Lambda is cheaper compared to running an instance continuously. You only pay for what you use, making it cost-effective.

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What do you like most about Google Cloud Dataflow?

The product's installation process is easy...The tool's maintenance part is somewhat easy.

See all answers

What is your experience regarding pricing and costs for Google Cloud Dataflow?

Pricing is normal. It is part of a package received from Google, and they are not charging us too high.

See all answers

What needs improvement with Google Cloud Dataflow?

I am not sure, as we built only one job, and it is running on a daily basis. Everything else is managed using BigQuery schedulers and Talend. However, occasionally, dealing with a huge volume of da...

See all answers

Comparisons

AWS Batch vs AWS Lambda

Compared 52% of the time

Apache NiFi vs AWS Lambda

Compared 18% of the time

Apache Spark vs AWS Lambda

Compared 11% of the time

Amazon EC2 Auto Scaling vs AWS Lambda

Compared 9% of the time

AWS Fargate vs AWS Lambda

Compared 3% of the time

More AWS Lambda Competitors

Databricks vs Google Cloud Dataflow

Compared 39% of the time

Apache Flink vs Google Cloud Dataflow

Compared 15% of the time

Apache NiFi vs Google Cloud Dataflow

Compared 12% of the time

Amazon MSK vs Google Cloud Dataflow

Compared 9% of the time

Talend Data Streams vs Google Cloud Dataflow

Compared 2% of the time

More Google Cloud Dataflow Competitors

Product Reports

Buyer's Guide

AWS Lambda

April 2025

Download AWS Lambda product report

Buyer's Guide

Google Cloud Dataflow

April 2025

Download Google Cloud Dataflow product report

Also Known As

No data available

Google Dataflow

Overview

AWS Lambda is a compute service that lets you run code without provisioning or managing servers. AWS Lambda executes your code only when needed and scales automatically, from a few requests per day to thousands per second. You pay only for the compute time you consume - there is no charge when your code is not running. With AWS Lambda, you can run code for virtually any type of application or backend service - all with zero administration. AWS Lambda runs your code on a high-availability compute infrastructure and performs all of the administration of the compute resources, including server and operating system maintenance, capacity provisioning and automatic scaling, code monitoring and logging. All you need to do is supply your code in one of the languages that AWS Lambda supports (currently Node.js, Java, C# and Python).

You can use AWS Lambda to run your code in response to events, such as changes to data in an Amazon S3 bucket or an Amazon DynamoDB table; to run your code in response to HTTP requests using Amazon API Gateway; or invoke your code using API calls made using AWS SDKs. With these capabilities, you can use Lambda to easily build data processing triggers for AWS services like Amazon S3 and Amazon DynamoDB process streaming data stored in Amazon Kinesis, or create your own back end that operates at AWS scale, performance, and security.

Amazon Web Services (AWS)

Google Dataflow is a unified programming model and a managed service for developing and executing a wide range of data processing patterns including ETL, batch computation, and continuous computation. Cloud Dataflow frees you from operational tasks like resource management and performance optimization.

Google

Sample Customers

Netflix

Absolutdata, Backflip Studios, Bluecore, Claritics, Crystalloids, Energyworx, GenieConnect, Leanplum, Nomanini, Redbus, Streak, TabTale

Find out what your peers are saying about Amazon Web Services (AWS), Apache, Zadara and others in Compute Service. Updated: March 2025.

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