When you create an API, it exposes the business logic of your system to the outside world, which needs to be protected.

To build a scalable API, mitigating vulnerabilities should be the first port of call. AsProgrammableWeb's editor-in-chief David Berlind explains, attacks are becoming sophisticated, multi-dimensional, and hyper-targeted.

The sensitive data Raygun collects must be protected with a multi-layered system that extends beyond the infrastructure of the API. As cyberattacks evolve, the systems we put in place need to be dynamic enough to change and evolve with them. (You can read more about security in APIs here.)

A sound infrastructure is, of course, only part of the story. We need to maintain our API to ensure it's at its most effective for our customers, and that it doesn't waste precious development time.

We need to maintain our API so we can add customers as needed, regardless of the amount of data they send us. To handle those data volumes, and to cope with even more over time, we need to have a lot of trust in our servers, which is why we use horizontal scale to our advantage.

You can use two ways of scaling your systems to enable a robust API: horizontal and vertical. At the start of business, scaling vertically makes more sense because it's more cost efficient (servers are expensive). However, you'll eventually have to scale horizontally to manage your data volumes. Here's how to determine which approach is best for your business scaling needs.

A simple way to scale your software is to pile on more hardware. Faster processors and more memory will certainly grow your software, but you'll need space and hardware continually, which gets expensive quickly.

You can also scale by building code that is optimized for performance; however, premature performance optimization may result in overly complex software that's difficult to maintain. This is a common pitfall because more performant code is less human readable in most cases, and therefore can be more complex. Eventually, your software will evolve to have more features to satisfy the demands of your customers or to keep ahead of the competition. But remember, the more features you add, the harder it is to scale due to the data storage needs that go with that expansion. This is the point at which you will need to use horizontal scale.

Part of effectively scaling horizontally is the ability to replicate and deploy quickly. To do this, one of the first steps we took as a development team was to remove error-prone manual deployments.

To scale efficiently, you need to find templates that work for your development team. Templates are a set of instructions for the autoscaling mechanism to let it know what to do when it starts up.

With autoscaling, modifying your template whenever you make a change to your API is the best approach. Every time you modify your template, you need to update your template with the API code. Then test your template by using it to cycle nodes, so test on one node then re-deploy.

To build a scalable API, you need to know immediately when something goes wrong. Collect and display key metrics from your DevOps tools — the more publicly, the better. "Information Radiators," such as TVs with stats around the office, are a great way to keep system health at the forefront of your mind.

We find that more people pick up on problems this way, especially our engineers who are in the codebase all day long. Spikes are detected very quickly if engineers can access baseline figures — plus they can see the results of any improvements they've made to a piece of code.

Here at Raygun, we recognize that taking a proactive approach to understanding the traffic coming to our API is key for horizontal scaling. We use Crash Reporting to identify and raise problems in our code into a dashboard that is accessible by everyone on the development team. We also use Crash Reporting to monitor for errors in our API specifically and collect data on timings. We put a lot of effort into monitoring custom metrics, such as failure per application, so if people send us bad API keys, we can understand where and why traffic is being rejected.

We've found that thorough software testing using both scale testing and production testing is critical to maintaining quality code and to ensure our API is robust and is able to scale when necessary. Here's a brief breakdown of our using scale and production testing management to ensure a robust API and create a better experience for our customers.

Scale testing is a strategy we employ at Raygun, so we know exactly where bottlenecks are. This strategy means we can cater to larger customers with no nasty surprises. To test what our API can handle, we run regular load tests, looking for our upper limit.

While developing an API for any business, you'll be testing and looking for this upper limit so you can constantly push your boundaries and grow — that's the process of scaling, which should grow into autoscaling.

Anything in your system can fail at any time — just make sure you find it before your users do. At Raygun, we also test thoroughly in production. We operate under the assumption there will always be problems and software bugs, but we have visibility on problems in production with Crash Reporting tools. This acceptance of software problems leaves teams much better prepared for reacting and resolving errors faster.

A prevailing attitude at Raygun that has helped us to build and scale is our strategy to make incremental changes to the code so we can react quickly and roll back if necessary.

We have a "fail early and fail fast" approach, which allows us to move and scale up with our business goals. Remember, allow for failure and never get caught off-guard.

The key to scaling your software is locating bottlenecks before your users do, and often your API is what provides the biggest restrictions. Scale your API, and scale your business.