Based on your app's features, which scaling strategy should you go for: scale up vs scale out?
How do you figure out which one's best for you?
What are each scaling model's use cases, benefits, and tradeoffs?
What specific needs — more memory, high availability, accessibility, more processor capacity, cost efficiency, long-term viability, etc. — does each solution respond to?
And what key factors should you keep in mind when choosing to scale your app out or up?
Now, let's get you some answers:
1. Scale Up vs Scale Out: What's the Difference?
The challenge you're facing now:
Your web app's under heavier traffic loads these days. So, you need to expend its presence, accessibility, power, other resources...
So, do you scale up or scale out?
What's the difference between vertical scale up and horizontal scale out?
1.1. What Does Vertical Scaling (or “Scaling Up”) Mean?
The process comes down to adding more power to your current machine so that it should carry more load.
Let's say that your server can no longer handle your app's load of input/output demands. By scaling it up you add more RAM and processing capacity to your existing server.
Or you switch to a new, more powerful server.
1.2. What Does Horizontal Scaling (or “Scaling Out”) Mean?
“What does scale out mean?”
It means adding more power by bringing in more lower-performance machines to the mix.
In short, the key difference between the scale up and the scale out process is the specific approach to the way that you're adding computing resources to your system:
Adding more processor capacity to your existing server vs adding more simple servers to your infrastructure, that share the memory workload and the processing effort.
2. Why Would You Scale Out?
Faced with a “scale up vs scale out” dilemma you ask yourself:
“What are the benefits of horizontal scaling?”
2.1. Higher availability for your app
Or, better said: higher, instant, and continuous availability for your application.
No matter how heavy the workload gets, each system component remains bounded over time.
2.2. You're not limited to your existing hardware capacity
You can bring in new and new machines to your infrastructure to expend its capacity.
2.3. You're not constrained to dig deep into your wallet each time traffic is on the rise
No need to pay for your server's upgrade each and every time you're dealing with peak demand.
2.4. You can tie your costs to use
2.5. You don't need to take your server offline at every traffic spike
Instead, you can keep the existing resources online all while adding some more, so that your app can cope with the workload and remain available. All the time...
2.6 You can make the most out of this scaling model's elasticity
Add as many computing services as needed so that your app withstands the peak demand.
2.7. You can adjust it to your needs
Size and... resize your network of machines to serve your app's fluctuating needs of memory and processor capacity.
2.8. You get to tap into the latest server technologies
Why keep expanding the same old hardware when you can get the most out of the newest server technologies for system monitoring and fault tolerance and keep downtime to a minimum?
2.9. You're free to upgrade your system
Unlike with the scaling-up model, where some upgrades might be limited by vendor lock-in, when you scale your app horizontally you're free to level up to the latest:
3. Why Would You Scale Up?
What are the key benefits of scaling your app vertically?
3.1. It's easier to manage
… and to address specific data quality issues.
Here, the “scale up vs scale out” dilemma comes down to:
Having one storage system management vs having to manage a whole cluster of different elements.
3.2. It's (more) cost-effective
You'll pay less for your network equipment and licensing since you only have one larger server to manage.
4. What Are the Tradeoffs of Horizontal Scaling?
For there are some power-performance trade-offs to be aware of when you opt for this scaling model:
- your servers have to be stateless: they can't contain any user-related data such as profile pictures or sessions
- scaling up your app leads to more complexity (cloning servers is needed)
- your downstream servers (e.g. databases and caches) are challenged to withstand more connections simultaneously while upstream servers are scaling out
5. What Are the Trade-Offs of Vertical Scaling?
As you're trying to solve your “scale up vs scale out” dilemma, you'll ask yourself:
What are the challenges of vertical scaling?
Well, here are the main aspects that could discourage you from choosing it:
- it's less viable: you're locked-in to a specific hardware piece on the market
- you need to go over the same server upgrading process at every spike of traffic
- you're constrained to taking your existing server offline while replacing it with a new, more powerful one: during this time, your app is non-available
6. When Would It Be Appropriate to Scale Vertically?
When should you scale up your deployment?
- when you're dealing with repeatedly increasing workloads
- if you haven't reached the full potential of your current infrastructure and you can still add on storage, CPUs, memory resources
- if you don't anticipate growth of your dataset over the next 3-5 years
- when you need to store large files that you can't split and distribute across multiple nodes
- when dealing with a small data set
7. And In Which Cases Does It Make Sense to Scale Horizontally?
Here are some of the best scenarios where “out” is the answer to your “scale up vs scale out” dilemma:
- you've already structured your app so that it should scale up, but it didn't reach the level of performance that you expected
- you've reached the limit of your current infrastructure's potential so... there's no other option but to scale out
- you expect huge and steady growth in data over a long period of time
- you need to distribute an overstrained storage workload across several storage nodes
8. Final Word: When to Scale Up vs Scale Out
Here are the 2 key factors to consider when you're trying to figure out which strategy is best for you:
- Your expansion needs: are they long-term or short-term? Are you dealing with a temporary traffic peak or do you predict a constant traffic overload in the long term?
- The type of workload that you're dealing with: how large is your dataset?
If you're still not sure which approach — scale-up or scale-out — would best suit your app project, let us help you find your answer:
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