General

Comparing App Platforms with Heroku/Salesforce, AWS, and Vercel

Published Mar 17, 2021

Updated Jan 31, 2023

8 min read

This article was written over 18 months ago and may contain information that is out of date. Some content may be relevant but please refer to the relevant official documentation or available resources for the latest information.

Introduction

Recently on This Dot's Build It Better show, I had the opportunity to sit down with some folks from popular platform as a service vendors. I asked them to tell me about what got them excited about their platforms, and what advice they have to offer to help viewers choose which platform is best for them.

Salesforce/Heroku

I spoke with Julian Duque and Mohith Shrivastava from Salesforce about their "low code" products, and how those products are enhanced by their Heroku platform for "pro code" solutions. Salesforce makes it easy for almost anyone to get started building and running cloud apps whether they're software engineers or not. The Salesforce Platform offers a wide variety of low-code products that make it simple for non-developers to build apps, and Heroku provides a streamlined set of tools and services for running custom software applications using a wide array of application development frameworks and programming languages.

I personally really love the simplicity and flexibility of the Heroku platform. I've used it for tons of projects over the years. You can host almost any application, built using any language or framework, using Heroku's buildpack technology. Heroku Buildpacks are sets of scripts that automate your app's build and deployment steps. Official Buildpacks for a dozen different platforms are available for use and in most cases, Heroku can automatically detect which one your app needs when you deploy your code. If your stack isn't supported by an official buildpack, you can build your own or use one of the many community maintained buildpacks for languages and frameworks that don't have first party support.

Another benefit of Heroku is that each Heroku app has an internal Git repository and all you need to do to deploy your code is push your code to that repository using git push. There are no additonal tools required for deployments. Not only does this simplify the process of deploying your code by hand, but it also means that Heroku is automatically compatible with any CI/CD system that supports Git, which is almot all of them by now. In addition to custom applicaiton hosting, Heroku also has PaaS integrated offerings for PostgreSQL, Redis, and Apache Kafka that can all be managed through the Heroku dashboard or CLI.

Even though I'm a long time user of Heroku, I wasn't really aware of everything that Salesforce brings to the table. Heroku offers a strong platform for pro code applications, but in addition, the Salesforce Platform provides a variety of low code tools that can be used to build applications by people who aren't experienced in custom software development. These tools allow businesses to begin the digital transformation process without needing to bring on a large in-house IT staff. There are point and click tools for managing authentication and identity as well as automating workflows and building user interfaces. They even offer a product called Einstein that can imbue your workflows with AI powers. However, you don't need to worry about outgrowing the low code solutions because the Salesforce Platform can also be integrated with pro code applications hosted in the Heroku ecosystem. This makes Salesforce/Heroku a great platform that businesses can rely on all the way through their digital transformation process.

Technology isn't the only thing that sets Salesforce and Heroku apart from their competition. They also provide a couple of huge documentation libraries. For the Salesforce Platform, you can head to their Salesforce Trailhead site. Trailhead offers interactive courses and learning tracks that will teach you how to build applications from the ground up on the Salesforce Platform. Heroku also has an expansive documentation library that not only applies directly to the Heroku platform, but I've used their documentation personally many times to assist in resolving problems with my applications on other platforms. The Heroku documentation site is not only comprehensive, but it's also easier to consume than that of many of their competitors (I'm looking at you Amazon). And finally, when documentation isn't enough, Heroku and Salesforce also have excellent support teams who will work quickly to resolve any problems you're experiencing with their platform, and in many cases they can act proactively before you are aware you have a problem.

Vercel

I also spoke with Lee Robinson from Vercel. Vercel is a platform that's quite similar to Heroku in a lot of ways. However they are laser focused on providing a great hosting platform for your Jamstack applications. While Heroku can support a nearly limitless number of programming languages and application frameworks, Vercel is focused on providing the best possible experience for "serverless" Javascript apps. These are apps that use a hybrid or static JavaScript framework for building frontends and backends that are powered by NodeJS serverless functions. Serverless functions written in Python, Go, or Ruby are also supported, but there are no options for supporting functions written in languages that aren't officially supported. Compared to Heroku's flexibility, one might take this to mean that Vercel is an inferior platform, but this isn't the case at all.

What Vercel doesn't offer in terms of flexibility, they make up for in developer experience. Where Heroku provides the simplicity of being able to effortlessly scale your applications by dragging a slider, Vercel takes the simplicity to the extreme and automagically scales your applications without needing to ever even use the dashboard or CLI. Not only do they completely automate and manage all the complexities of scaling your app to meet the demands of your users, you also get the benefit of having the Vercel Edge Network CDN to ensure your app is always available and performant no matter where your users are located geographically. This is all part of every single app hosted on Vercel, even the free tier!

Vercel also provides additional tools to help you supercharge your development workflows and improvement cycles. "Develop. Preview. Ship" is Vercel's mantra. To help developers achieve this, not only do they provide Git-based deployments, but for each branch or pull request opened via version control, Vercel provides a "preview URL" which is connected to a preview version of your application that reflects the code on that branch/PR. This eliminates the need for complicated staging and QA workflows, since preview URLs provide isolated environments for testing and demoing new features. Another mantra Lee shared with me is the idea that "developers are scientists." As developers, we can use data to inform how we build the solutions we work on, but often that data can be cumbersome or difficult to obtain. Vercel simplifies the data collection process by offering a high quality analytics platofrm to help you understand how your applicaiton performs, not only in terms of response performance but also tracking frontend user experience metrics like layout shift and input delay. Being able to easily collect and visualize these metrics allows you to really be a scientist and always be able to justify priorities and improvements to your products with real user data.

Another interesting aspect of Vercel is that they've also created a NodeJS application development framework in-house called Next.js that is meant to pair perfectly with their platform. It provides a "zero-configuration" framework for building applications with NodeJS and React. It's an incredibly flexible platform that can support the simplest one-page statically rendered applications, but also can support request-time server-side frontend rendering and custom backend API endpoints supproted by Vercel's serverless functions. To help new and experienced developers alike, Vercel offers a library of starter projects using Next.js and/or other JavaScript frameworks you can use to get your project started with just a few button clicks.

Amazon Web Services

I spoke with Nader Dabit from Amazon about their new Amplify platform. Amazon has been the biggest player in the PaaS marketplace for well over a decade now. Most developers have used an EC2 virtual server or stored application assets and uploads in S3. What developers may not know is that Amazon offers more than 200 different services for use by developers and other business users. Ec2 and S3 are pretty simple and straightforward, but branching out into the broader ecosystem or learning to tie everything together can be pretty intimidating. This isn't a big deal for companies like Netflix or AirBnB who can afford to bring in devops engineers that are already AWS experts, but historically it's been a lot more difficult for less exprienced developers to take full advantage of what AWS has to offer. With Amplify, the AWS team is hoping to demystify the process and give new and experienced developers a way to work with the core AWS platform in a more streamlined way. Instead of having to udnerstand which service to use out of a list of 200+ services with intimidating names, Amplify selects a smaller subset of these services and gives them less esoteric names. So Amazon Cognito becomes "Authentication" and AWS Lambda becomes "Functions". They also provide simplified client libraries over the traditional AWS SDK that are compatible with JavaScript, Android, iOS and Flutter.

Another neat thing about the Amplify platform is that they, like Salesforce, are steering users toward Amazon's low code tools like AWS AppSync and API Gateway, and making it easier for developers to integrate with AWS tools for things like AI/ML predictions and PubSub. Also like Salesforce, if developers outgrow the lowcode tools, it's easier than ever to expand out to the boader ecosystem and some of the more specialized services that amazon offers.

In addition to making it easy to build your application's backend with little or no code, Amplify also offers the frontend components you need to build interactive web or mobile apps. Amplify UI components are available for React, Angular, Vue and more.

And of course, on top of the simplified Amplify toolchain, AWS still provides the same 200+ services they've traditionally offered. So if you outgrow Amplify, or need services that aren't compatible with it, you can always integrate offerings outside of the Amplify ecosystem with other AWS services.

Another thing I really like about Amplify, and AWS in general, is the pricing. All of the Amplify services have a free tier. This makes it useful for hobby projects or to keep development costs low before you launch your applications. Also, it's important to note that the other services like Heroku and Vercel are often based on AWS themselves (. As such, buying services direct from AWS will usually save you at least a little bit of money over using a more managed service.

Conclusion

Developers have a ton of choices when they are choosing a platform to build their applications on. All of the vendors I spoke with have compelling solutions that will make your life as a developer better. I always personally reach for platforms like Heroku or Vercel first since they're quick and easy to get started with, but it's clear that AWS has taken note of that and is trying to close that gap. So really, there's not a bad choice if these are your options. I hope I've explained them well enough so you can choose which one suits your project the best!

This Dot is a consultancy dedicated to guiding companies through their modernization and digital transformation journeys. Specializing in replatforming, modernizing, and launching new initiatives, we stand out by taking true ownership of your engineering projects.

We love helping teams with projects that have missed their deadlines or helping keep your strategic digital initiatives on course. Check out our case studies and our clients that trust us with their engineering.

About the author(s)

Adam Clarke
@vaxinate

The Journey To Making A New Framework: TanStack Start with Tanner Linsley

Danny Thompson, Director of Technology at This Dot Labs, talks with Tanner Linsley, Creator of TanStack, about his latest project, TanStack Start. They discuss the challenges of existing frameworks like Next.js and Remix, the development of TanStack Router, and the future of React Server Components. Tanner also explains how caching strategies and fine-grained invalidation can transform the user experience. Chapters - - Introduction & Tanner’s Background (00:00) - Going Full-Time on TanStack (01:00) - The Birth of TanStack Router (02:21) - Why Build Another Framework? (04:00) - React Server Components: Potential & Limitations (07:05) - Fine-Grained Cache Invalidation & UX (09:02) - Parallel Data Fetching in Routing (13:39) - TanStack Start: Alpha & Future Plans (16:41) - Where to Learn More About TanStack (18:48) Find Tanner Linsley on Social Media Twitter: https://x.com/tannerlinsley Linkedin: https://www.linkedin.com/in/tannerlinsley/ Github: https://github.com/tannerlinsley TanStack: https://tanstack.com/ Sponsored by Wix Studio...

Oct 9, 2024

1 min

TanStack QueryJavaScriptReact

Learning Paths for Next.JS Developers with Ankita Kulkarni

In this video, Rob Ocel and co-hosts Tracy Lee, Adam Rackis, and Danny Thompson talk with tech educator Ankita Kulkarni about her journey from engineering leader to full-time educator. Ankita shares insights on teaching Next.js, bridging practical knowledge gaps, and helping developers tackle real-world challenges. They discuss Next.js as a React-based framework, its benefits, and the challenges it presents for beginners. Chapters - Introduction to the Podcast and Guests 00:01 - Meet Ankita Kulkarni, Tech Educator 00:26 - Ankita's Transition to Full-Time Education 01:41 - Teaching Practical Knowledge in Next.js 03:19 - Effective Methods for Teaching Next.js 05:27 - Challenges of Being a Full-Time Educator 07:48 - Balancing Broad and Specific Examples 09:54 - Embracing Mistakes as a Teaching Tool 12:13 - Pair Programming and Mentorship 14:00 - Discussion on Next.js and Framework Adoption 16:48 - Advantages and Challenges of Next.js 18:12 - Choosing the Right Framework for Your Needs 20:35 - Impact of Next.js in React Documentation 22:26 - Learning Paths for New Developers 23:24 - The Rise of Full-Stack Web Development 25:09 - Benefits of Frameworks Abstracting Complexity 26:27 - OpenNext and Deployment Flexibility 28:06 - Ankita's Excitement for New Next.js Features 30:35 - The Future of Next.js Without Vercel 32:16 - Final Thoughts and Where to Find Everyone Online 34:21 Follow Ankita Kulkarni on Social Media Twitter: https://x.com/kulkarniankita9 YouTube: https://www.youtube.com/@kulkarniankita Sponsored by This Dot: thisdot.co...

Nov 12, 2024

2 mins

NextJSJavaScript

Building Docker Containers

Revisiting The Basics In my earlier post, Getting Started with Docker, I covered building a basic Dockerfile using the FROM, COPY, RUN, and CMD instructions and how to use a .dockerignore file to keep unnecessary files out of your images and containers. If you haven't read that post, go check it out to learn the basics of building Docker images. In this post, I'll cover some more advanced techniques for building container images. In addition, I recently published a post exploring advanced Docker CLI usage. I recommend giving it a read, too, if you aren't already a CLI pro. Installing Dependencies Using FROM with an official image for your language or framework will get you a long way, but many applications will require a system dependency that's not included in the FROM image. For example, many applications use ImageMagick for processing image uploads, but it's not included by default in the Debian images that most language images are based on. You can use RUN and apt-get to install missing dependencies. ` We started the Dockerfile just like the example from my earlier post, using the official NodeJS 15 image, but then we do 2 additional steps to install ImageMagick using apt-get. To keep the base image size low, Debian does not come pre-loaded with all of the data it needs to install packages from apt-get, so we need to run apt-get update first so that apt-get has that info. Then, we simply use apt-get install -y imagemagick to install imagemagick. The -y option is used to automatically respond with "yes" when apt-get prompts you to confirm the package installation. RUN vs CMD (vs ENTRYPOINT) By now you've probably noticed that there are two different instructions that run commands in your containers, RUN and CMD. While both are used to run commands, they're used in very different contexts. As we've seen in previous examples, RUN is used exclusively in the build process to run commands to modify the image as needed. CMD is different because it specifies the command that will be run by the container when you launch it using docker run. You can have as many RUN instructions as you need, but only one CMD. If you need to run a different command at runtime, you can pass it as an argument when you launch the container with docker run (check out my Docker CLI Deep Dive post). Additionally, Docker provides the ENTRYPOINT instruction. This is a command that the command you provide to the CMD instruction will be passed to as arguments. If you do not provide an ENTRYPOINT it will default to /bin/sh -c which will cause your CMD command to execute in a basic unix shell environment. The default ENTRYPOINT will satisfy most use cases. It's possible to override a container's CMD at runtime, but it is not possible to change its ENTRYPOINT. Docker's own ENTRYPOINT documentation goes into more detail about how it can be used. In the example Dockerfile above, you probably noticed that the way commands are passed to CMD and RUN looks different. Typically, when using RUN you provide commands using shell syntax, and you provide commands to CMD (and ENTRYPOINT) using the exec syntax, but they can be used interchangably. When using shell syntax, you can resolve shell expressions within your command. You can use shell variables and operators like output pipes (|) and redirects (>, >>), as well as boolean operations (&&, ||) to join commands. Exec syntax is much more straightforward. Each string within the bracketed array is joined with the other elements with a space in between and run exactly as provided. Layers and Caching Each isntruction in your Dockerfile adds a new Layer to your image. For performance reasons, it's considered a best practice to limit the total number of layers that comprises your finished image. There are a number of ways to do this. The simplest is by combining lines where RUN or COPY are used in close proximity to each other. Consider the example above where we installed ImageMagick; instead of using two separate RUN instructions, we can combine them using the bash && operator. ` Combining copy commands is a bit easier. The COPY instruction takes any number of arguments. The first N parameters provided to COPY are interpreted as a list of files to copy, and the N+1th paramter is the location to copy those files to. You can also use * as a wildcard character as I did in the first example when copying the package.json and package-lock.json files to the image. Anothing thing to consider when thinking about how your image layers are composed is caching. When Docker processes your Dockerfile to build your image, it runs each of the instructions in order to create the layers of your image. Docker analyzes each instruction before it is run and checks its cache to determine whether or not there is an identical existing image layer. When analyzing RUN instructions, Docker looks for any cached image layer that was built using the exact same command and uses it instead of rebuilding the same layer. For COPY and ADD instructions, it analyzes the files to be copied and looks for a previously built layer that has the exact same file contents. If at any point any instruction requires its layer to be rebuilt, all of the following instructions will result in a rebuild. Optimizing your Dockerfile to take advantage of the layer cache can greatly reduce the time it takes to build your image. Organize your Dockerfile so that the layers least likely to change are processed first (ex: installing dependencies) and those more likely to change (ex: copying application code) are processed later. Conclusion These techniqes will help you create more advanced container images and hopefully help you optimize them. However, I've only covered a small slice of the options available to you when building container images. If you dig deeper into the official Dockerfile reference you'll find information about all of the instructions available to you and more advanced concepts and use cases....

Dec 22, 2020

5 mins

Docker

Introduction to Zod for Data Validation

As web developers, we're often working with data from external sources like APIs we don't control or user inputs submitted to our backends. We can't always rely on this data to take the form we expect, and we can encounter unexpected errors when it deviates from expectations. But with the Zod library, we can define what our data ought to look like and parse the incoming data against those defined schemas. This lets us work with that data confidently, or to quickly throw an error when it isn't correct. Why use Zod? TypeScript is great for letting us define the shape of our data in our code. It helps us write more correct code the first time around by warning us if we are doing something we shouldn't. But TypeScript can't do everything for us. For example, we can define a variable as a string or a number, but we can't say "a string that starts with user_id_ and is 20 characters long" or "an integer between 1 and 5". There are limits to how much TypeScript can narrow down our data for us. Also, TypeScript is a tool for us developers. When we compile our code, our types are not available to the vanilla JavaScript. JavaScript can't validate that the data we actually use in our code matches what we thought we'd get when we wrote our TypeScript types unless you're willing to manually write code to perform those checks. This is where we can reach for a tool like Zod. With Zod, we can write data schemas. These schemas, in the simplest scenarios, look very much like TypeScript types. But we can do more with Zod than we can with TypeScript alone. Zod schemas let us create additional rules for data parsing and validation. A 20-character string that starts with user_id_? It's z.string().startsWith('user_id_').length(20). An integer between 1 and 5 inclusive? It's z.number().int().gte(1).lte(5). Zod's primitives give us many extra functions to be more specific about *exactly* what data we expect. Unlike TypeScript, Zod schemas aren't removed on compilation to JavaScript—we still have access to them! If our app receives some data, we can verify that it matches the expected shape by passing it to your Zod schema's parse function. You'll either get back your data in exactly the shape you defined in your schema, or Zod will give you an error to tell you what was wrong. Zod schemas aren't a replacement for TypeScript; rather, they are an excellent complement. Once we've defined our Zod schema, it's simple to derive a TypeScript type from it and to use that type as we normally would. But when we really need to be sure our data conforms to the schema, we can always parse the data with our schema for that extra confidence. Defining Data Schemas Zod schemas are the variables that define our expectations for the shape of our data, validate those expectations, and transform the data if necessary to match our desired shape. It's easy to start with simple schemas, and to add complexity as required. Zod provides different functions that represent data structures and related validation options, which can be combined to create larger schemas. In many cases, you'll probably be building a schema for a data object with properties of some primitive type. For example, here's a schema that would validate a JavaScript object representing an order for a pizza: ` Zod provides a number of primitives for defining schemas that line up with JavaScript primitives: string, number, bigint, boolean, date, symbol, undefined, and null. It also includes primitives void, any, unknown, and never for additional typing information. In addition to basic primitives, Zod can define object, array, and other native data structure schemas, as well as schemas for data structures not natively part of JavaScript like tuple and enum. The documentation contains considerable detail on the available data structures and how to use them. Parsing and Validating Data with Schemas With Zod schemas, you're not only telling your program what data should look like; you're also creating the tools to easily verify that the incoming data matches the schema definitions. This is where Zod really shines, as it greatly simplifies the process of validating data like user inputs or third party API responses. Let's say you're writing a website form to register new users. At a minimum, you'll need to make sure the new user's email address is a valid email address. For a password, we'll ask for something at least 8 characters long and including one letter, one number, and one special character. (Yes, this is not really the best way to write strong passwords; but for the sake of showing off how Zod works, we're going with it.) We'll also ask the user to confirm their password by typing it twice. First, let's create a Zod schema to model these inputs: ` So far, this schema is pretty basic. It's only making sure that whatever the user types as an email is an email, and it's checking that the password is at least 8 characters long. But it is *not* checking if password and confirmPassword match, nor checking for the complexity requirements. Let's enhance our schema to fix that! ` By adding refine with a custom validation function, we have been able to verify that the passwords match. If they don't, parsing will give us an error to let us know that the data was invalid. We can also chain refine functions to add checks for our password complexity rules: ` Here we've chained multiple refine functions. You could alternatively use superRefine, which gives you even more fine grained control. Now that we've built out our schema and added refinements for extra validation, we can parse some user inputs. Let's see two test cases: one that's bound to fail, and one that will succeed. ` There are two main ways we can use our schema to validate our data: parse and safeParse. The main difference is that parse will throw an error if validation fails, while safeParse will return an object with a success property of either true or false, and either a data property with your parsed data or an error property with the details of a ZodError explaining why the parsing failed. In the case of our example data, userInput2 will parse just fine and return the data for you to use. But userInput1 will create a ZodError listing all of the ways it has failed validation. ` ` We can use these error messages to communicate to the user how they need to fix their form inputs if validation fails. Each error in the list describes the validation failure and gives us a human readable message to go with it. You'll notice that the validation errors for checking for a valid email and for checking password length have a lot of details, but we've got three items at the end of the error list that don't really tell us anything useful: just a custom error of Invalid input. The first is from our refine checking if the passwords match, and the second two are from our refine functions checking for password complexity (numbers and special characters). Let's modify our refine functions so that these errors are useful! We'll add our own error parameters to customize the message we get back and the path to the data that failed validation. ` Now, our error messages from failures in refine are informative! You can figure out which form fields aren't validating from the path, and then display the messages next to form fields to let the user know how to remedy the error. ` By giving our refine checks a custom path and message, we can make better use of the returned errors. In this case, we can highlight specific problem form fields for the user and give them the message about what is wrong. Integrating with TypeScript Integrating Zod with TypeScript is very easy. Using z.infer<typeof YourSchema> will allow you to avoid writing extra TypeScript types that merely reflect the intent of your Zod schemas. You can create a type from any Zod schema like so: ` Using a TypeScript type derived from a Zod schema does *not* give you any extra level of data validation at the type level beyond what TypeScript is capable of. If you create a type from z.string.min(3).max(20), the TypeScript type will still just be string. And when compiled to JavaScript, even that will be gone! That's why you still need to use parse/safeParse on incoming data to validate it before proceeding as if it really does match your requirements. A common pattern with inferring types from Zod schemas is to use the same name for both. Because the schema is a variable, there's no name conflict if the type uses the same name. However, I find that this can lead to confusing situations when trying to import one or the other—my personal preference is to name the Zod schema with Schema at the end to make it clear which is which. Conclusion Zod is an excellent tool for easily and confidently asserting that the data you're working with is exactly the sort of data you were expecting. It gives us the ability to assert at runtime that we've got what we wanted, and allows us to then craft strategies to handle what happens if that data is wrong. Combined with the ability to infer TypeScript types from Zod schemas, it lets us write and run more reliable code with greater confidence....

Nov 15, 2024

7 mins

JavaScriptZod

Let's innovate together!

We're ready to be your trusted technical partners in your digital innovation journey.

Whether it's modernization or custom software solutions, our team of experts can guide you through best practices and how to build scalable, performant software that lasts.

Comparing App Platforms with Heroku/Salesforce, AWS, and Vercel

Introduction

Salesforce/Heroku

Vercel

Amazon Web Services

Conclusion

Adam Clarke

You might also like

The Journey To Making A New Framework: TanStack Start with Tanner Linsley

Learning Paths for Next.JS Developers with Ankita Kulkarni

Building Docker Containers

Introduction to Zod for Data Validation

Let's innovate together!

You might also like

The Journey To Making A New Framework: TanStack Start with Tanner Linsley

Learning Paths for Next.JS Developers with Ankita Kulkarni

Building Docker Containers

Introduction to Zod for Data Validation