Greetings, startup founders and tech enthusiasts! As technical co-founders who've journeyed through the thrilling, yet daunting, process of building tech products from scratch, we've seen the highs, the lows, and the many twists and turns in between. Before starting up, we were tech warriors, fighting battles in the trenches of startups and marching in the grand armies of tech giants.
In well-established companies, many decisions are pre-made. The tech infrastructure? Already set. The cloud provider? Chosen. Servers, storage, compute, programming languages? All figured out. You walk in and everything's ready, like a fully furnished house, inviting you to sit down and get to work. Contrast this with the wild, wild west of startups - where you're staring at an empty plot of land, tasked with building everything from the ground up. It’s daunting, yes, but also incredibly exciting. The number of options can feel overwhelming and confusing - like staring at a restaurant menu that's 20 pages long.
We know what it feels like. We've been there. We've made tough decisions and learned valuable lessons, often the hard way. Now, we want to pay it forward and share our hard-earned knowledge with you.
That's why we've decided to launch this blog post series - Startup Data Stacks. We'll share our experiences, insights, and tips to help you navigate your tech startup journey with more confidence and clarity. We aim to be the tour guides we wish we had when we started, helping you save a ton of time and effort so you can focus on what really matters - building your product and growing your startup.
This series aims to help you set up your analytics and data infrastructure from scratch so you can be better prepared to make data-driven decisions. We’ll share what worked for us. We're not claiming this is the only way or even the best way, it’s just what worked for us. You might find that some things that worked for us might work for you as well. Or, our experience might inspire you to find a completely different path that works better for your startup.
Let’s dive right in!
Our first post tackles one of the earliest, yet crucial, decisions every startup founder needs to make: Where do I host my app or website?
Without a doubt, cloud hosting is the way to go for startups. The idea of setting up on-premise servers is pretty much obsolete given the time, cost, and effort involved. Cloud hosting is nimble, scalable, and comes with a host of features that will make your tech life infinitely easier. Now, the big question is, which cloud provider to choose?
In the world of cloud computing, three behemoths stand tall: Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure. Each one has its unique strengths and potential weaknesses. This blog post aims to offer a balanced comparison to hopefully ease your decision-making process. Remember, many cloud providers extend significant credits to startups, a crucial consideration for your business decisions.
With a strong lineage as the oldest and most mature player in the cloud market, AWS offers an extensive range of services. It's not only its age but also the robustness of its offerings that make it a top choice. Its highly reliable infrastructure and top-notch customer support have consistently won us over. While its vastness can be overwhelming for beginners, the strength of its community is a powerful asset. A rich ecosystem of blogs, forums, and resources means that solutions to problems are often just a search away. Do keep in mind that AWS's pricing model can take some time to fully grasp. For those interested in startup credits, AWS Activate is worth checking out.
While GCP excels in areas like AI, machine learning, and containerization, it is still playing catch-up to its more established rivals. It does offer some compelling features such as live migration for virtual machines and a more straightforward pricing model than AWS. However, in our personal experience, GCP's customer support hasn't quite lived up to our expectations. Furthermore, its coverage in terms of services and regions is not as extensive as AWS or Azure. Do check out GCP's startup credits.
If your startup is deeply tied to Windows and .NET, Azure might be a natural fit. It offers strong integration with Microsoft's suite of products and an impressive hybrid cloud offering. Azure may also be attractive to startups focusing on AI due to its close integration with OpenAI. Unfortunately, we don't have personal experience with Azure's customer support, so we can't provide a fair comparison in that respect. Information on Azure's startup credits can be found here.
As much as it's advisable to stick with one provider to minimize data transfer costs, there could be scenarios where a multi-cloud approach makes sense. This could be the case if you have credits from multiple providers or specific needs that different platforms best address. Beware, though - complexity can increase rapidly.
Remember, choosing a hosting platform is a crucial decision, but not a permanent one. As your business grows and evolves, so too will your needs - and your hosting platform can adapt accordingly. Don't be afraid to change course when needed. The cloud is vast, and there's a perfect spot somewhere in it for your startup.
Having chosen your cloud provider, you now need to select your cloud service - Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Container as a Service (CaaS), or Serverless computing.
IaaS is like renting a plot of land to build your house. You get the raw materials (servers, storage, and networking) and the rest is up to you. It provides high flexibility and control but demands significant time and expertise. AWS EC2, Google Compute Engine (GCE), and Azure Virtual Machines fall under this category.
IaaS is a fit for large-scale, long-term projects with fluctuating demands. Say you're building a data-intensive application, such as a real-time analytics system. The high degree of control offered by IaaS is invaluable in these scenarios.
In PaaS, you get a portion of the land with the foundation already built. You just have to construct the house. PaaS provides a managed hosting environment where you only worry about the application code. AWS Elastic Beanstalk, Google App Engine, and Azure App Service are examples.
PaaS shines when you need to rapidly deploy and iterate on applications with standard tech stacks. If you are a startup with a lean development team, this can be a game-changer. However, its less flexibility can be a downside if your application requires more custom setups.
CaaS offers the ability to manage containers, applications, and clusters through a single API. Think of it like a condo where you own your unit but share the building and its maintenance. AWS ECS/EKS, Google Kubernetes Engine (GKE), and Azure Kubernetes Service (AKS) are the go-to services.
CaaS is excellent if you need the portability of containers and desire a simplified orchestration and automation. It works well for microservices architectures and DevOps practices. Keep in mind that CaaS comes with a learning curve if you're not familiar with containerization and orchestration concepts.
Serverless is like a fully furnished and managed house. You just move in and live your life. It automatically manages and scales your apps. You pay only for the compute time you consume, making it cost-effective for sporadic usage patterns. AWS Lambda, Google Cloud Functions, and Azure Functions are leading the pack.
Serverless is great for lightweight, stateless applications that respond to events and triggers, such as real-time file processing or data transformation tasks. While it alleviates much of the server management, it might not be suitable for long-running applications due to its execution time limit.
Remember, you aren't confined to one compute model for your entire application. Modern, complex applications often benefit from a combination of compute models, using each where they shine best. For instance, you might use PaaS (like AWS Elastic Beanstalk) for your core backend logic due to its simplicity and automated scaling capabilities. For your search service, you could employ serverless architectures (AWS Lambda, for instance) due to the intermittent and unpredictable nature of search requests. Serverless architectures can scale instantly to meet the demand and you only pay for the actual compute time used. For hosting machine learning models, which often demand more resource-intensive, specialized workloads, you might choose a Container as a Service (CaaS) solution such as AWS ECS or EKS. They offer the necessary granular control, isolation and the flexibility to use any libraries or dependencies that your ML models require.
Each of these options comes with its degree of technical complexity. IaaS provides the most control but requires a deep understanding of infrastructure management. PaaS simplifies deployment but may restrict some custom configurations. CaaS requires expertise in container orchestration. While serverless minimizes infrastructure management, it requires familiarity with its event-driven programming model.
Cost is certainly a significant factor to consider. The same cloud service can vary in cost depending on the geographic region where your application is hosted. Some regions, like Asia, tend to be pricier than others, like North America. However, the proximity of your hosting location to your end-users can greatly impact the performance and overall user experience.
Many cloud providers offer 'free tier' services and startup credits that can significantly offset initial costs. Make sure to explore these options when choosing your cloud provider. Balancing cost, performance, and scalability provides the best overall value for your startup's specific needs.
Choosing the right hosting platform is a critical decision that can significantly impact your startup's trajectory. Each platform has its own set of benefits, and your choice should align with your expected user traffic, compute requirements, team's expertise, cost-effectiveness, and ease of setup and management.
However, we recommend choosing the cloud service/provider that lets you build and iterate your product faster. While it's essential to understand the offerings and make an informed choice, don't get bogged down in figuring out the perfect configuration right away. Choose a solution that enables you to move swiftly, get your product into users' hands, and gather valuable feedback. Once you’ve built the product users want, you can always re-engineer the tech for scale.
We hope this post empowers you to make an informed decision about your hosting platform. Keep an eye out for our next post where we will continue to break down the process of setting up a strong analytics and data infrastructure for your startup