Switching from Datadog to Scout: A Practical Guide

Last updated: May 2026

Datadog is a powerful observability platform. It is also complex, expensive, and built for organizations with dedicated DevOps teams who have time to configure dashboards, tune alert rules, and manage data ingestion costs. If your team has outgrown the value Datadog provides relative to what it costs and demands, switching to Scout is straightforward.

This guide covers the practical steps, what to expect during the transition, and what BackerKit’s engineering team learned when they made the same switch.

Why Teams Switch

The teams that move from Datadog to Scout tend to share a pattern: they adopted Datadog because it was “industry standard” or because a previous team member set it up, and over time the product became more than the team needed. Common reasons:

Complexity without payoff. Datadog has 500+ integrations, infrastructure monitoring, security scanning, network monitoring, and more. If your team just needs to know why your Rails or Django app is slow, navigating that product surface takes longer than fixing the actual problem.

Cost growth. Datadog charges per host with feature add-ons. As your infrastructure grows, costs grow in multiple dimensions that are hard to predict. Teams that primarily need application monitoring often find they are paying for infrastructure, log, and security features they do not use.

Dashboard fatigue. Building and maintaining useful dashboards in Datadog requires ongoing investment. Small teams end up with dashboards nobody looks at, or worse, dashboards that show metrics nobody can act on.

BackerKit’s staff engineer JT Archie put it directly:

“Datadog is very much: here’s some presets you can use, here’s community presets. Someone just said this is industry standard, so we should use it. Which is not a good reason to buy software.”

What You Get When You Switch

Scout replaces the application monitoring layer of Datadog with a tool that is simpler and more focused. Here is what changes:

What you gain: Errors, logs, and traces in one view. Automatic N+1 detection. Memory bloat identification at the code level. 5-minute setup. Predictable pricing.

What you give up: Infrastructure monitoring, network monitoring, security scanning, browser/RUM, and the 500+ integration ecosystem. If you need those, keep Datadog for infrastructure and run Scout for application monitoring alongside it.

The BackerKit Story

BackerKit manages $3.7 billion in crowdfunding pledges with a 5-person engineering team running Rails on Heroku. They used Datadog for years before switching to Scout.

The change that mattered most was not a metric. It was a meeting.

Every Friday, the team holds a 30-minute performance review. Before Scout, they spent that time clicking around Datadog trying to find relevant data. After Scout, they spend it choosing which issues to fix. Mae Beale, BackerKit’s director of engineering, described the shift:

“We don’t have time to go through all kinds of configs or screens. Scout tells us: here are things that are important. We’re able to hone in and maximize the bandwidth we have for performance optimization.”

During the free trial, Scout independently flagged an ActiveRecord issue the team had already identified through other channels. The fact that Scout found it automatically, without configuration, was the deciding factor.

The team now uses Scout’s weekly digest email as their meeting agenda, pulls up the dashboard to investigate changes, and creates prioritized tickets directly from what Scout surfaces. They also use the Scout MCP server with GitHub Copilot to feed trace data into their AI coding assistant for faster investigation.

Read the full BackerKit case study.

How to Switch: Step by Step

1. Install Scout alongside Datadog

You do not need to remove Datadog first. Install Scout’s agent in your application and run both tools in parallel for at least a week. This gives you a comparison baseline and ensures Scout is capturing the data you need before you cut over.

For Rails:

gem 'scout_apm'

For Django/Flask/FastAPI:

pip install scout-apm

For Laravel:

composer require scoutapp/scout-apm-laravel

For Phoenix:

{:scout_apm, "~> 2.0"}

2. Compare for one week

During the parallel period, check that Scout is capturing:

• Your slowest endpoints (compare against Datadog’s APM view)
• Database query timing
• Background job performance (Sidekiq, Celery, Oban, etc.)
• Error groups
• Deploy markers

Scout’s auto-instrumentation should capture all of this without configuration. If something is missing, check the Scout docs for your framework.

3. Redirect your workflow

Move your weekly performance review (or whatever your team’s debugging routine is) to Scout. Use Scout’s interface for one full cycle before removing Datadog. The goal is to confirm that Scout gives your team the same or better answers faster.

4. Set up alerts and integrations

Configure Scout alerts for the metrics your team actually responds to:

• Response time thresholds on critical endpoints
• Error rate increases
• Background job latency

Connect Scout to Slack, PagerDuty, or email. If your team uses AI coding assistants, set up the Scout MCP server so your agents can query production data directly.

5. Remove Datadog

Once your team has used Scout as the primary tool for at least one sprint, remove the Datadog agent from your application and cancel the subscription. Keep any Datadog infrastructure monitoring if you need it separately.

Running Both Tools

Some teams keep Datadog for infrastructure monitoring and run Scout for application monitoring. This is a reasonable setup if you need host-level metrics, network monitoring, or AWS service telemetry that Scout does not provide. The two agents run independently with minimal overhead.

Common Questions

How long does the switch take?

The technical switch (installing Scout) takes 5 minutes. The workflow switch (moving your team’s debugging routine to Scout) takes about a week. Most teams run both tools in parallel for 1-2 weeks before fully cutting over.

Will I lose historical data?

Scout starts collecting data from the moment you install the agent. You will not have historical data from before the switch. If you need to reference old Datadog data, keep your Datadog account active (on a lower tier if needed) until you are confident you do not need it.

What if I need infrastructure monitoring?

Scout monitors applications, not infrastructure. If you need host metrics, container monitoring, or AWS service telemetry, keep Datadog (or switch to a lighter infrastructure tool) for that layer. Many Scout customers run Scout for APM alongside a separate infrastructure monitoring tool.

What about the pricing difference?

Scout’s pricing is transaction-based with everything included (APM, errors, logs). Datadog charges per host plus add-ons for each feature. For most small-to-medium application teams, the total cost with Scout is significantly lower. The free tier includes 300,000 monthly transactions with error monitoring and log management.

If you are considering the switch, start a free Scout trial and run it alongside Datadog for a week. No credit card required. For application monitoring with errors, logs, and traces, we provide the fastest path to useful information without the bloat.

For a feature-by-feature comparison, see Scout vs Datadog: Developer-Focused Monitoring vs Enterprise Observability.