Tech Stacks of the Top 5 SaaS Survey Platforms

Introduction

If you are building a survey software solution from the ground up, you will have to make some important technology choices early on in the process. For example, the type of database can significantly affect many aspects of your project, from the performance of your data analytics layer to your solution’s ability to scale.

As with anything else, there’s more than one way to skin a cat. Your tech choices should respond to the specifics of your particular project and there are a few fundamental questions that can inform your software architecture decisions:

• What type of data do you need to collect?
• How frequently will you be collecting it?
• What is the expected volume of survey responses?
• What kind of insights and analytics are required?

For example, if you are collecting a large number of responses and you don’t have any requirements for aggregate data analytics, then you should probably go with some sort of NoSQL database for your persistence layer. Chances are, however, that you will need to export survey responses in tabular format and express the results through charts and graphs. In that case you will need to have a relational database in addition to (or in place of) your NoSQL data storage.

After considering your specific requirements, it still might be useful to take a peek at the list of technologies behind some of the top SaaS survey products.

Research

This article is not meant to review or rank the best products on the market and they are not listed in any particular order. If you are looking for feature-by-feature comparisons, I would recommend searching for a recent report by someone like G2, Forrester, or Gartner.

The tech stacks listed here are estimated and may not be 100% accurate. In my research I avoided sites like BuiltWith or StackShare, because I have found them to be less reliable when it comes to backend technologies. Instead, I studied some job postings from the listed vendors in order to get a better idea of their engineering infrastructure. Furthermore, I found some interesting threads on social sites, in which employees of those companies discuss their tech stacks.

Results

SurveyMonkey

SurveyMonkey is probably the best-known name in the industry. They recently rebranded the company to Momentive, although as of 2022 they continue to use the SurveyMonkey brand for their flagship product. Being such a large organization and having launched almost 25 years ago, I would imagine they have gone through a lot of technological changes through the years.

Qualtrics

Another big player that has been around for many years and has gone through a lot of transformations. Recently acquired by SAP, they seem to have focused on the enterprise market.

Typeform

I would categorize Typeform as the cool kid in the industry. Born during the peak of the Web 2.0 revolution, the aesthetically minimalistic design of their UI makes them stand out among the clunky web apps of the competition.

Jotform

Jotform has also been around for a while, but they seem to have done a much better job at maintaining a modern look and feel than the bigger players.

Alchemer

This application used to be called SurveyGizmo. Similarly to SurveyMonkey, they have rebranded in an effort to step out of the oversaturated market of survey tools, and address the goal-oriented enterprise segment.

Summary

Typically for SaaS products, AWS sticks out as the common infrastructure choice across all of the vendors we looked at.

On the data persistence side of things I am not surprised to see a combination of relational and NoSQL engines. This resonates with the large volume of data and aggressive performance needs of the products. MySQL and MongoDB dominate the list, but I was happy to see SurveyMonkey’s VP of DB Operations state that Microsoft SQL Server is their primary storage engine (See the original post on Quora).

Another common theme can be seen in the area of backend programming - Python. This is not at all surprising as Python is one of the most popular languages for analytics and data science.