Azure Serverless Scaling Controller

Monitor, simulate, and control Azure Functions scaling behavior to optimize costs and protect downstream resources.

Added Dec 3, 2025

Cloud Infrastructure

Developer Tools

Cost Management

Opportunity score

/ 100

Very low opportunity

Unlock the score breakdown

See how signal quality, demand and competition contribute to this score, with supporting evidence and the full calculation.

Loading score details

The Problem

Azure Functions scaling is opaque and unpredictable. Developers struggle with unexpected costs, performance bottlenecks, and overloading downstream resources like SQL databases due to lack of visibility and control over scaling triggers and concurrency limits.

Potential Solution

A SaaS platform that provides real-time observability into Azure Functions scaling decisions, predictive simulations for workload patterns, and programmable scaling policies with safeguards to prevent resource contention and cost overruns.

Why Now?

As serverless adoption accelerates and workloads become more complex, teams need granular control beyond native Azure tooling to manage costs and performance at scale.

What brought you here?

Find an idea

Validate my idea

Research a market

Find potential customers

What would you like to see?

More original sources

Competitor details

Pricing and willingness to pay

Customer details

What to do next

I found what I needed

Market validation

Search demand

Trend snapshot pending

Competition

Loading competitors...

Showing 1-5 of 5 signals

RedditNov 30, 2025

Azure Functions Concurrency problems

Hello, I am trying to understand what may be limiting my Azure Functions performance. Right now I have two Azure functions that trigger on HTTP requests. Function App 1 receives a request from my Web API, and as a result executes about 42 requests straight to Function App 2. Function App 2 then receives those 40 responses (and scales to 40 instances?) and does some simple calculations based on the request and then each one returns a response within 10 milliseconds. This all works well and good with a few hundred requests to Function App2 but once it balloons to 1thousand-15thousand requests the response times steadily grow. Each calculation starts to take more and more time as if they are pending. What I would expect to happen instead is that 15k function apps each spawn and handle each individual request concurrently and within a few milliseconds. Instead this is taking up to 10 minutes. Could this be SNAT port related? Concurrency related? I have tried eliminating the expensive calculation operation so that the same number of requests are made but with no complex calculations and the problem almost completely goes away. This leads me to believe that it is not connection related but the Function App 2's inability to scale up to 15,000 instances to handle that 10ms calculation. Thoughts? Any help would be greatly appreciated.

RedditNov 30, 2025

Function Apps hosted on Container Apps

I’m trying to understand how function apps work on container app, as app service plans are not elastic enough when traffic increases by 10x in a minute. So, I understand that there is the ARM property ‘kind=functionapp’, which lets the host know that the container is running a function. Then, the Azure Portal still shows the Container App UI but the instance type is ‘Container App (Function)’. All great, however the HTTP autoscaler is set to 10 concurrency request which is quite a low value and apparently can't be edited. This is such of a problem as it means ACA needs 1000 instances to serve having 1000 req/s, even though CPU and memory are at 1% of usage. During one of my tests, I was suddenly able to override that value by both Terraform azapi provider and Azure Portal, however I didn’t understand why and what differs from preview tests (screenshot). I have noticed that although I’m using the azapi with the latest ARM api available ‘2025-07-01’., exporting the ARM template from the Portal sets the version to ‘2025-02-01-preview’. Does anyone have experience on this and know how to set a proper value for the HTTP KEDA autoscaler? Many thanks EDIT: clarity

RedditNov 30, 2025

Is Azure Functions the appropriate solution for my workflow?

I need to process about 15,000 HTTP requests in under 10 seconds. Each request performs a quick (10-20ms calculation) and returns a result. Current Setup: I have a web app that is working great. A user makes a selection and when they click a button it sends about 40 HTTP requests to my 1st python HTTP trigger function app. I am on a dedicated app service plan. This 1st function app then does some simple logic based on the request content and determines that 1,000-15,000 calculations are required to complete the request. Those 1,000-15,000 calcs are then sent to the 2nd HTTP triggered python function app. Each calculation is simple and takes between 10 and 20ms to complete. I would expect all of these 15k requests to execute concurrently and well within 10 seconds. Instead it is taking over 5 minutes to complete them all. Smaller batches of requests work fine. A few hundred requests finish in less than 10 seconds. Is this a limitation of function apps? Should I look into hosting as an app service or on a VM? We had a similar solution working on AWS Lambda without issue but I'd rather use Azure right now. The network processing time seems to be between 2-5ms. I know this because I tried a test with the calculation operation removed entirely. The two function apps facilitated the same 15k HTTP requests in a total time of less than 10 seconds. Therefore I think it's something to do with asking it to perform 15k 10ms calculations at the same time that it can't quite cope with for some reason. When I add back in the calculation step it takes several minutes to complete. Thank you.

Unlock 4 more signals

Go beyond the grade and inspect the evidence behind this opportunity.

Reddit discussions

See the original problems, requests, and conversations.

4 more