Limiting data being logged using Application Insights in Azure Functions

As you may know, Azure Functions have a preview of Application Insights integration enabled. This is another great addition to our serverless architecture since we don't have to add this dependency on our own - it's just there. However, there're some problems when it comes to handling the amount of data, which is being collected, especially when your're on an MSDN subscription.


When you enable Application Insights for your Function App, each function will start collecting different metrics(traces, errors, requests) at different scale. When you go to Azure Portal and access Data volume management tab in the Application Insights blade, you'll see, that there's one metric, which really exceeds our expectations(at least when it comes to the volume of the data traced):

As you can see, Message data takes 75% of the total amount of data collected

When you click on any bar, you'll access Data point volume tab - now we can understand, what kind of 'message' is really being logged:

Although chart says Message, data type related to this particular type of message is Trace

Configuring AI integration

Logging traces is perfectly fine, however we don't always want to do so(especially if you're on an MSDN subscription and don't want to be blocked). If you go to this page, you'll see a detailed information regarding both enabling and working with Application Insights. The most interesting part for us is the configuration section:

  "logger": {
    "categoryFilter": {
      "defaultLevel": "Information",
      "categoryLevels": {
        "Host.Results": "Error",
        "Function": "Error",
        "Host.Aggregator": "Information"
    "aggregator": {
      "batchSize": 1000,
      "flushTimeout": "00:00:30"
  "applicationInsights": {
    "sampling": {
      "isEnabled": true,
      "maxTelemetryItemsPerSecond" : 5

As you can see, we're able to set different levels for each category of data being logged. According to comments in this issue on GitHub, the easiest way to actually limit the data being logged is to set your configuration to the following:

  "logger": {
    "categoryFilter": {
      "defaultLevel": "Error",
      "categoryLevels": {
        "Host.Aggregator": "Information"

This way you should be able to avoid logging to much data or hitting your daily cap for Application Insights.

I strongly recommend you to play with AI integration in Azure Functions and provide feedback regarding possible features or enhancements. It's a great way to collaborate with a team responsible for a product and a chance to make it even better.

Considering appropriate app service plan for your Azure Functions

As we all(or most of us) know, Azure Functions can be hosted using either a regular app service plan or a consumption plan. We can quickly summarize pros and cons of both:

App service plan:

  • fixed cost(+)
  • easy to scale(+)
  • ability to run 64-bit applications(+)
  • can reuse other app service plans(+)
  • fixed cost(--)
  • some triggers need Always On enabled(-)

Consumption plan

  • pay-as-you-go(++)
  • no need to have Always On for e.g. TimerTrigger(+)
  • somehow more difficult to scale(-)
  • if not designed carefully, the cost may exceed our expectations seriously(-)

All right - but what really should I consider when it comes to choosing the correct plan for my application?


In the current world scalability is something, what should be really considered when designing an application and choosing technologies for it. Let's consider a following example - you're designing an e-commerce application, which has to handle really big traffic spikes from time to time(imagine Black Friday or any other black something). The specifics of traffic on your website could be described as:

  • stable and low traffic for the most of a week
  • an increase during weekend
  • occasional huge spikes during special events 

Now let's relate this to our service plans. What is better for us in such scenario?

Well, the problem with consumption plan is the fact, that it needs a running start. You can't expect, that if your application is hit by a huge traffic spike, it'll scale out immediately. What is more, you don't have a possibility to scale up - you're limited to the resources allocated for you for your instance of the consumption plan. You have to consider one more thing - execution of functions is not throttled in any way by default so you may face a situation, where under a heavy load your function utilize too much CPU/memory at once. You can control throttling by following three properties:

  • maxOutstandingRequests
  • maxConcurrentRequests
  • dynamicThrottlesEnabled

which are described here. The one thing you have to remember when using throttling is the possibility, that your client may get HTTP 429 Too Busy responses. Whether it's a problem, only you can decide.

When using s regular app service plan those traffic spikes are much easier to handle. Since you can have scaling rules, you don't care when scaling out happens - it just does when it hits CPU/memory threshold(or other metric if autoscale is enabled). Additionally you can preprovision extra resources if you know when a traffic spike will happen - this way you're prepared.


When designing cloud solutions, the cost is one of the most important factors. If you choose components poorly or overdesign them, the bill at the end of a month won't make you happy for sure. This is the second thing directly related to service plans, which affects our choice when it comes to select what is the best.

Pay-as-you-go model in consumption plan is something, what really make functions interesting. When designed carefully, you can run them for almost free each month(or pay only a few USD/EUR after the free quota is exceeded). The problem is when you keep your functions "red" - in such scenario, it may be easier and cheaper to use a regular app service plan, which ensures the constant cost of this component and won't surprise you after a busy weekend(how is that I have to pay extra 500$ this month?).

Of course with a regular app service plan you lose flexibility and have to remember to scale your application down(or at least have something to automate this). The compromise here depends on your current needs and how the model of your business looks like. However it's still better to discuss it now and be aware of your possibilities rather than discovering them when functions start to respond with HTTP 503 status.