Part I: Getting Around the Datadog Pricing Model

Jul 20th, 2023
Part I: Getting Around the Datadog Pricing Model
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Table of contents

  1. Introduction
  2. Datadog Pricing Model 101
  3. Why Should We Even Care About Datadog Costs?
  4. It’s Not Your Core Competence
  5. Scalable - Throughput and $ Wise
  6. Implicitly Increases With Infrastructure Scaling
  7. Difficult To Forecast
  8. So Why Not Just Use Datadog Usage Dashboards?
  9. To Care Or Not To Care
  10. Next Up


Unless you’re living under a rock, you’ve probably heard of Datadog, one of the most loved and used observability platforms currently available.

Recently, we at Finout, set out on the journey to shine a bright light on Datadog's pricing model, and allow organizations to better understand them, prepare for the end-of-month invoice, and optimize their bottom line. 

The main reason for us to go down this rabbit hole is that the Datadog platform lacks observability on its out cost (yep, I see the irony here), making it quite challenging to reason about its cost, optimize it, and be alerted on the ominous increase.

In the upcoming series of posts, I’ll cover 

  1. Why should you take an interest in your Datadog costs?
  2. How Datadog pricing models work, and the lessons learned as a Datadog user.
  3. The approaches we tried to crack the Datadog cost/usage model.
  4. What can we do to get Datadog costs under control?

This article, the first one in the series will try to answer the basic question of them all; Why you should care about your Datadog costs.

Firstly, a disclaimer - Datadog is an AMAZING product. And although throughout this series I'm going to cover how costly it is, it’s also very valuable, and the observability gained by it is exceptionally good. You pay a lot but also get quite a lot in return. 

I feel it’s important to explicitly mention it before we dive in.

Datadog Pricing Model 101

Datadog has numerous products, for various use cases, and most of them have add-ons with additional functionality, capabilities, and cost. But the general rule of thumb you need to understand is that the more you use, the more you pay:

You are billed on the hosts to monitor or profile, You are billed on the volume (GB) of logs you are sending and indexing, etc.

If you know, or have a hunch how much you’re going to use - you can:

    1. Commit to a usage for relevant Datadog products, and get a decent discount
  • Prepay a “Base fee” - An upfront payment, then get discounted prices for any used products, and then any usage gets billed from that prepaid amount. It remains debit cards in a way.

But the key here, is you need to know how much to commit to, and then basically wait and pray you won’t overuse it.

Why Should We Even Care About Datadog Costs?

If you’re asking that, you are either:

  1. Very optimized - And therefore a real kudos for you! I know how complex achieving this is.
  2. Still at a small scale, therefore your Datadog costs are limited.
  3. You never used Datadog, and therefore don’t understand the Datadog pricing model and in turn, know how easy it is to get to a very large invoice.

Let’s break it down, and understand what makes Datadog different for your main cloud provider.

It’s Not Your Core Competence

First, let’s agree that a typical Datadog invoice can easily get to 4-10% of your cloud provider invoice - and since we know how expensive your cloud provider is, you can now understand how expensive Datadog is too.

Datadog is NOT your core competence, it’s a supporting tool - a very good one, and a very important one, but a supporting tool nonetheless. It’s not the EC2 instances that keep your business available or the underlying storage that stores the data that gives you a competitive edge, it’s an observability platform that helps your operations organization keep the lights on and the engine going.

You are paying, because it’s not your core competence - you’re paying because you want to focus on building your core value.

That being said, we should be vigilant on how much we pay on services that are outside our core competence too, and since there’s always room for “more monitoring”, and more monitoring means more cost, where do we draw the line?

Scalable - Throughput and $ Wise

It’s crazily scaleable, therefore anything you’ll throw at it will be ingested, stored, available for you to query, and then alerted on. Sounds amazing right? 

Well, it’s indeed amazing, and then - anything you throw at it also gets billed. 

And that’s what makes it so easy to get out of hand - launching a new service that sends too many logs can easily be the difference between $100 a month to $1500 a month. This can be due to developer error, or just since the service you’ve just launched serves so much traffic that even the minimal necessary logging causes excessive costs (and this happened to me, and I've written about our log cost optimization effort).

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Implicitly Increases With Infrastructure Scaling

As the business grows you add more infrastructure instances (EC2 instances, serverless function, etc), and you implicitly add additional resources to be monitored, which in turn increases the Datadog billed amount.

This is the expected behavior - you just need to keep your unit economics in check, i.e. the increase in monitoring cost is indeed in linear correlation to the business and infrastructure costs.

Difficult To Forecast

Lastly, it’s quite hard to provision usage, and therefore forecast the expected cost.

Ask your average developer what their services’s throughput, and what’s the volume of logs each request generates - I doubt they could give you the number, And even if they knew - there’s still the process of translating usage to estimated cost.

Alternatively - what’s the expected number of hosts we’re going to use to support the Black Friday sale or the Super Bowl halftime? And should we commit to this number or our average regular usage? 

What are the expected additional services we’re about to develop - how many compute resources, serverless, log, and metrics they are going to use?

And since those numbers are hard to come up by, the commitment we buy from Datadog is also hard to come by. Most of the time, the de-facto way to do it is to start using the product with some (or no commitment) and adjust the commitment according to the actual usage (which is forever changing).

So Why Not Just Use Datadog Usage Dashboards?

It’s true, that Datadog offers usage metering, where you can have a general understanding of your stance and usage.

But does the average developer/product owner understand what 4TB of 7 days retention indexed logs mean in terms of cost and operability of the service? 

Can we do with 3.5TB of 3 days retention? 

And how much will we save if we go for it? 

Do they understand how this can be optimized? and should this even be?

Do they understand how much it costs, which portion of it is committed up-front, and which gets billed on a pay-per-use pricing model?

To Care Or Not To Care

We pay for Datadog so someone else will handle the headache of managing such a complex platform, but when the Datadog expense gets to be a headache of its own - this is the time to regroup and rethink your strategy.

Through the right usage analysis that turns into usage optimizations and better commitment allocation, it’s possible to save a large chunk of the end-of-month bill. A chunk that can be in the ranges of tens and even hundreds of thousands of dollars a year.

And since either way when your bill gets painful enough, you’re going to do the maths and calculate your commitments from time to time - why not do it better, and save more?

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Next Up

This post serves as an introduction to the challenges and the importance of the topic. You can find the link to the original blog post here.

In the next post, we’ll talk about the Datadog pricing model and its pitfalls.

Click here to continue to the next blog post.

As always, thoughts and comments are welcome on Twitter at @cherkaskyb

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