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 21.0% mindshare in the category. Google is ranked #7 in SA , with an average rating of 8.0, and holds a 7.4% 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 83 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, Spot by NetApp 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 21.0%, down 23.2% compared to last year.
Google Cloud Dataflow, on the other hand, focuses on Streaming Analytics, holds 7.4% mindshare, up 7.0% 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.

Read full review

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

"Some of the most valuable features are that it's easy to install and use. The performance is also good."

"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."

"I have found all of the features valuable. It's an easy and cheap solution."

"The most valuable feature is that it scans the cloud system and if they are any security anomalies it triggers an email."

"Amazon takes care of the scalability. That's the right way. It's automatic and it's fully managed. That's one benefit of Lambda."

"I like that it's easy to use and maintain. Lambda is good and supports different platforms, so you don't need to worry about language or maintenance."

"What I like most about AWS Lambda is that it's very easy to deploy."

"The most valuable feature of AWS Lambda, from a conceptual point, is its functions. For example, it's mathematical templates into which you can write, and create your solution. You write small pieces of a solution under given parameters."

More AWS Lambda pros

"I would rate the overall solution a ten out of ten."

"It is a scalable solution."

"The solution allows us to program in any language we desire."

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

"The best feature of Google Cloud Dataflow is its practical connectedness."

"The integration within Google Cloud Platform is very good."

"I don't need a server running all the time while using the tool. It is also easy to setup. The product offers a pay-as-you-go service."

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

More Google Cloud Dataflow pros

Cons

"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."

"What could be improved in AWS Lambda is a tricky question because I base the area for improvement on a specific matrix, for example, latency, so I'm still determining if I can be the judge on that. However, room for improvement could be when you're using AWS Lambda as a backend, it can be challenging to use it for monitoring. Monitoring is critical in development, and I don't have much expertise in the area, but you can use other services such as Xray. I found that monitoring on AWS Lambda is a challenge. The tool needs better monitoring. Another area for improvement in AWS Lambda is the cold start, where it takes some time to invoke a function the first time, but after that, invoking it becomes swift. Still, there's room for improvement in that AWS Lambda process. In the next release of AWS Lambda, I'd like AWS to improve monitoring so that I can monitor codes better."

"The product could make the process of integration easier."

"Lambda's dashboard could be more user-friendly and customizable. I want the dashboard to have more information to quickly identify what functions and events are running. Also, we want to be able to add more trigger points, push notifications, and events."

"The product needs some updating as far as ease-of-customization and configuration opportunities to work with solutions outside of the cloud."

"Lambda can only be used in one account; there's no possibility to utilize it in another account."

"AWS Lambda could be improved with better stability."

"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."

"I would like to see improvements in consistency and flexibility for schema design for NoSQL data stored in wide columns."

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

"Google Cloud Dataflow should include a little cost optimization."

"The deployment time could also be reduced."

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

"Google Cloud Data Flow can improve by having full simple integration with Kafka topics. It's not that complicated, but it could improve a bit. The UI is easy to use but the experience could be better. There are other tools available that do a better job."

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

More Google Cloud Dataflow cons

Pricing and Cost Advice

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

"Price-wise, AWS Lambda is a five out of ten."

"AWS Lambda is cost-effective, with a minimal maintenance cost."

"The pricing is on-demand and based on runs or times that are billed out monthly."

"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."

"We only need to pay for the compute time our code consumes."

"The fees are volume-based."

"AWS Lambda is not expensive for micro testing but is expensive if used for long deployment or long services."

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 Dataflow is a cheap solution."

"The solution is not very expensive."

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

"Google Cloud is slightly cheaper than AWS."

"The tool is cheap."

"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."

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Top Industries

By visitors reading reviews

Educational Organization

68%

Financial Services Firm

Computer Software Company

Manufacturing Company

Financial Services Firm

17%

Manufacturing Company

12%

Retailer

11%

Computer Software Company

11%

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.

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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 17% of the time

Apache Spark vs AWS Lambda

Compared 10% 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 41% of the time

Apache Flink vs Google Cloud Dataflow

Compared 14% 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, Spot by NetApp and others in Compute Service. Updated: March 2025.

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