AWS vs Firebase. What backend solution to choose for startup?
Discussing AWS vs Firebase should start with talking about their roots to understand the significance of these technologies. AWS is first. It launched in 2006, and it is basically the reason why launching your startup is so easy these days.
Before AWS, as an entrepreneur, you had to purchase servers and arrange for space in a data center. All that vendor selection and negotiation took a while. Then, setting that up would take weeks or even months. And, by the way, you had to know upfront the capacities you would need.
With AWS, instead of all of that, all you need is a credit card. You don’t need to think about capacities upfront – AWS scales up and down as needed. You don’t need to do much setting up, it is virtually instantaneous. And you can pay-as-you-go based on how much you use. No need for upfront investment. AWS literally started a startup revolution.
Then, Firebase was founded in 2011, launched in 2012 but took off in 2014 when Google acquired it. Firebase pushed the limits of how easy the server side could get for mobile development and real-time apps. AWS was still the pioneer, but its offering was quite extensive and looked like a selection of separate tools. The learning curve for AWS was quite significant. Firebase came in as a fully managed solution. And while AWS does scale, it requires some setting up and monitoring. Firebase scales effortlessly in real-time.
Overall, both AWS and Firebase are great backend solutions. However, there are significant differences for specific use cases in AWS vs Firebase decision-making. We’ll discuss this and more and how it can impact your decision-making as an entrepreneur in terms of cost, time-to-market, complexity and scalability for your startup.
Table of contents
Serverless Backend Solution
There are a variety of architectural backend solutions for your business idea. The major ones though are monolith, microservices, and serverless. Here is a quick overview.
Monolith
A monolith which is when you have client-side code, backend code, and a database in one codebase. This architecture is great for small applications. After all, even one developer can write all the necessary code. It will be written in one language, easily tested and deployed as a single unit.
However, as the codebase grows, if you want to change one thing in your UI – you will have to retest and redeploy the entire monolith. Other problems are code coupling, difficulty dividing developers into teams as it is hard to define code boundaries, performance issues, and so on.
In general, a monolithic architecture is a very quick, simple, and affordable choice that allows your startup to reach success. Only after you’ve reached success, the drawbacks kick in.
Microservices
Microservices architecture appeared to solve the drawbacks of monoliths. Within the microservice architecture, the codebase is developed separately in functional chunks from the start. Each service is an isolated unit. It can be modified and updated without impacting other parts of the application. You can scale a certain service as you need. But for an MVP and while your app is only finding its product-market fit, it may be an overkill. It can be too costly in terms of time, money, and complexity.
Serverless: AWS vs Firebase
Serverless is the best of both worlds (meaning a monolith and microservices). And this is where the struggle AWS vs Firebase is most pronounced. Serverless brings in one more level of abstraction. It does not mean there is no server. It means that server-side code is taken care of by the cloud service provider. For instance, authentication is a built-in feature in Firebase or you can opt for Amazon Cognito. In the picture below you can see the move from a monolith to microservices to serverless. It basically makes bits of your code more independent and granular. Each new architectural backend solution gives you greater control and separation of concerns.
Serverless in itself hasn’t always been a function only. It also evolved as shown in the picture below.
- The first was IaaS, infrastructure as a service.
- Then we started dealing with containers – CaaS, container as a service.
- This further upgraded to PaaS, platform as a service.
- And today we reached FaaS, function as a service.
The point is the more you break it down and abstract away, the more you can focus on what makes your app better for users. The less you worry about technicalities, the more you can focus on the user-facing part. This is why serverless architecture is great for startups when your main goal is to find your target user and align your proposition with their needs and preferences.
Firebase vs AWS: Cost Considerations
In short, Firebase is cheaper at small to medium scale, and AWS is cheaper at large scale. Let’s break it down.
Firebase and AWS both have a free tier. However, with Firebase, you can go to its pricing page, and get a pretty good idea of what you can get for free or calculate costs for the paid services. For instance, Firebase has a 50k free cap for Monthly Active Users (MAU) and 50 MAU using enterprise-grade authentication.
If you go to AWS and click pricing, it will take a while to find this information because of their granular services and tools. You would need to find a service Amazon Cognito and then you will find it has the exact same free caps. 50k MAU users and 50 with enterprise-level authentication. But, it is a separate service in AWS. Once you opt for all the services you need, costs for AWS will jump up. In contrast, Firebase offers full transparency and easy calculation.
However, you can set up all these individual AWS services separately when you scale. Plus, there are savings options. For instance, if you can predict how much computing capacity, you can prepay for some instances, which will save you money. All in all, AWS gives you more control and saving opportunities at scale. So, it will be cheaper than Firebase. Firebase at a large scale can quickly get expensive.
AWS vs Firebase: Time-to-market
Taking a simple MVP development for ballpark comparison, you can launch in two to four weeks with Firebase, while AWS will take up to 3 months. Of course, it all depends on the nature of the project, developers’ expertise, and such. But these are the estimates to give a general idea. Why is there such a difference?
As mentioned above, Firebase is a fully-managed solution and AWS is an array of separate tools. With Firebase, a lot of functionality comes out of the box and is built-in. For instance, Firebase for serverless architecture or microservices offers:
- User Authentication;
- Real-time Database (NoSQL database);
- Cloud Storage (media files storage);
- Cloud Messaging (push notifications & messaging);
- Cloud Functions.
This functionality is often more than enough for e-commerce mobile apps, chat apps, a variety of delivery services, chat apps and event-driven mobile applications. Plus, gaming mobile apps can also be implemented with Firebase. Last but not least, Firebase is perfect for prototyping.
App’s Lifecycle Management
Furthermore, Firebase has tools for managing the app’s life cycle completely. It offers development SDKs. And then it also offers tools for testing and monitoring. Using Firebase, you can check how your app performs on different devices and screen sizes, you can get crash reporting of client-side issues, and generally monitor the performance of your app. By combining Remote Config and Firebase analytics, you can successfully run A/B testing. And there are tools for working on user engagement. It includes dynamic app updates without having to redeploy it, sending push notifications, tracking user behavior, and dynamic links.
For AWS, all these features are separate services. Because of that, implementing simple MVPs as mentioned above will be 30-50% longer when using AWS. Considering AWS vs Firebase, Firebase’s all-in-one approach really makes Firebase the best choice for MVP development or a startup that is only looking for its users.
App Complexity
Firebase offers a simplified backend to the point where a front-end developer can utilize Firebase’s backend features and launch a well-performing app. Cloud functions, real-time synchronization, and user authentication are quite easy to use. However, with that level of simplicity, it is hard to bring in complexity. Therefore, if your app needs to perform some sort of extra computation, Firebase isn’t really a viable solution. AWS use cases include:
- If you think of multi-filtering like you can see on the Amazon website or complex searches, then it is only possible with AWS.
- Gaming that enables multiplayer options or AR/VR technology also should be done with AWS.
- For IoT, AWS is the go-to solution because of its dedicated tooling and capabilities for device-to-cloud communication.
- Apps that require ML computing power, social networking aimed at millions of users, e-commerce with complex flows, and enterprise-level apps also go to AWS for a set of very good reasons.
When it comes to AWS, there are simply more options. In terms of Authentication, there is mainly AWS Cognito. And Firebase Cloud Functions are similar to AWS Lambda. However:
- Firebase does not have anything comparable to EC2 for computing power;
- And then, for real-time database management, AWS offers Amazon DynamoDB and AWS AppSync.
- Plus, there is Amazon Aurora for relational DB to be synced globally in real-time.
- For IoT, there are AWS IoT Core and Amazon Timestream.
- Plus, Amazon ElastiCache has no analogous functionality in Firebase.
Thus, if your project comes with a high degree of complexity, AWS will win the AWS vs Firebase struggle here.
AWS vs Firebase: Scalability
In AWS vs Firebase, it’s a general rule that Firebase is good for small to medium-sized apps while AWS is a perfect solution for large-scale and enterprise-grade apps. And today’s Startup Services will consider the best architecture for your project. But when you have a business idea, how do you know which app it will be in simple terms? After all, is it a scale when we have an app that sells a lot of goods at low prices to hundreds of thousands of users? Or if it is a highly advanced computational app working in a niche market? Both solutions are considered large-scale. In the first case it is because of the number of users. In the second case, it is because of complexity.
Overall, there are 4 main criteria which determine if your app is small, medium or large-scale.
Number of monthly active users (MAU)
- Up to 10k, it is a small-scale app.
- From 10k to 100k, it is a medium-sized app.
- When the user count is in millions, it is a large-scale app.
Number of concurrent users
These are measured at peak times when the maximum number of users interact with your app.
- To consider an app a small-scale one, this number is measured in hundreds, most often around 100.
- For a medium-sized app, it is usually from 1,000 concurrent users.
- If we have more than 10k concurrent users, then it is a large-scale app.
Data Storage
- For a small-scale app, data storage is around tens of GB (10-90 GB).
- In a medium-sized app, appetites for storage space grow beyond 100GB and up to 1TB.
- More than 1TB of data to store upgrades your app to a large scale.
Complex Workflows
This cannot be expressed in numbers. If your app has to do a lot of processing like for an IoT app, or advanced analytics, or some serious personalization run by machine learning algorithms, then your app is large-scale.
However, whatever large-scale app you pick, it almost always started as a small-scale solution and went through a technological overhaul. The examples include Headspace, Pocket, Airdrop, Calm, Todoist, and more. In addition, it is often a mistake to not choose a lean MVP methodology or dive into complexity over a customer-centric approach. We’ve written about it in our article “Top 10 Marketplace Mistakes to Avoid in Your Startup”. Therefore, even if your plan is to become a large-scale app, you still can benefit from using Firebase at early stages and move onto AWS as your app needs to grow.
AWS vs Firebase Summary
| Firebase | AWS | |
| Cost | cheaper at small to medium scale with transparent pricing | cheaper at large scale, each service can be set up separately and there are savings opportunity at scale |
| Time-to-market | 2-4 weeks | ~30% longer than Firebase, can be up to 3 months |
| App complexity | Simple apps, e-commerce with a few filters and search, gaming, localized delivery services, real-time communication: messaging, smaller scale social apps, etc | Complex apps that include AR/VR, multiple filters, complex database querying, millions of users globally, ML, IoT |
| Scalability | small to medium-sized apps | Large scale & enterprise-grade apps |
FAQ: AWS vs Firebase for Startups
AWS offers a wide array of tools and services for customizable and scalable backend solutions, ideal for complex and large-scale apps. Firebase is a fully managed backend solution, best suited for quick MVP development and small to medium-sized apps.
Firebase is more cost-effective for small to medium-scale applications due to its transparent pricing. AWS, however, becomes more affordable at large-scale deployments because of its granular services and savings options.
Firebase allows startups to launch an MVP in 2-4 weeks with its ready-to-use features like authentication, real-time databases, and cloud functions. AWS, with its modular services, can take 30-50% longer to set up for MVPs.
Firebase excels at simplicity and ease of use but struggles with highly complex app requirements like advanced ML, AR/VR, and multi-filtering e-commerce. AWS is better suited for such scenarios with tools like EC2, DynamoDB, and AWS Lambda.
Firebase is great for small to medium-scale apps, supporting up to 100,000 monthly active users. For apps requiring enterprise-grade scalability and global infrastructure, AWS is a more robust option.
Yes, many startups begin with Firebase for its quick setup and move to AWS as their app grows in complexity and scale. This approach balances cost, time-to-market, and future scalability.
