# Best Monitoring Tools for AI-First Engineering Teams

> Production issues don't wait for a root cause. See how 5 monitoring tools compare on setup, pricing, and visibility, and find out which one saves your team the most time.

Source: https://qa.tech/blog/best-monitoring-tools-ai-first-engineering-teams · Published: 2026-09-28

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Your PR passed the review, the CI pipeline succeeded, and the deployment went smoothly. But now, something is slow. The exception rate is climbing, and you’re trying to figure out whether the problem is in the code you just deployed or somewhere else in the stack.

This is where monitoring can help you out. Sure, testing can give you a good idea of how things should perform before deployment, but only production data can tell you what actually happened: whether it was a slow query, a spike in exceptions, a memory leak that only shows up under real traffic, or something else entirely.

Plenty of tools can be useful in this regard, from focused error trackers to full observability platforms. We’ve narrowed the list down to five tools developers can realistically work with without turning monitoring into a full-time job.

[AppSignal](https://www.appsignal.com/) is our top pick for teams that want performance and error monitoring in a single platform while keeping the setup and everyday use simple.

## What Good App Monitoring Actually Looks Like

Before we compare the tools, here's what we tend to look for in a monitoring dashboard:

-   **Ease of setup:** Setup needs to take minutes, not months. [Add the SDK,](https://docs.appsignal.com/application/markers/deploy-markers.html) deploy, and you should be looking at real data almost immediately. If it takes a kickoff call, endless configuration, and a week to be put into operation, then it looks like your monitoring solution will only be slowing you down. And that’s the last thing you want when you’re just trying to solve a problem.

-   **Errors and performance in one place:** A slow endpoint and a high number of exceptions are often two sides of the same coin. That’s why these metrics shouldn’t be presented in separate places. If you need to jump between different dashboards just to connect the dots, it will only make debugging more complicated.

-   **Predictable pricing:** Being able to estimate how much you’ll pay for the service based on usage data is a good thing. If you're constantly checking how much data you've been consuming just to avoid a surprise bill, then the monitoring tool has just become another operational burden for you.

-   **No overhead:** Most teams don't need an observability platform that requires someone to manage it full time. They just want enough visibility to answer some simple questions after a deploy. What has changed? What’s broken? Where should I look first? That’s it.

## Tools Worth Considering

Here's how five tools stack up against those criteria, starting with the one we'd reach for first.

### AppSignal

[AppSignal](https://www.appsignal.com/) is built around a simple idea: that most teams don't need separate tools for error tracking and application performance monitoring. That’s why it combines these two into a single platform, alongside logs, uptime tracking, host metrics, and cron monitoring.

Here’s what makes AppSignal a worthwhile choice.

-   **Quick setup**: You’ll likely want answers immediately after deploy, so monitoring should be ready as soon as you need it. Add the SDK to your app, deploy it, and you'll be looking at real transaction traces and grouped errors within minutes, no sales call required.

-   **Combining** [**error tracking**](https://www.appsignal.com/tour/errors) **and** [**performance monitoring**](https://www.appsignal.com/tour/performance): As mentioned, this is particularly useful when an exception spike and a slow endpoint are symptoms of the same problem.

-   **Specific** [**Ruby monitoring**](https://www.appsignal.com/ruby): AppSignal offers [Global VM Lock metrics](https://docs.appsignal.com/ruby/integrations/global-vm-lock.html), which can come in handy when investigating thread contention issues. Most APM tools don't expose this at all, so teams debugging thread contention or misconfigured worker counts in Puma or Sidekiq are often flying blind, without the one key metric that would explain the mysterious slowdown.

-   [**Predictable pricing**](https://www.appsignal.com/plans): The pricing model is based on requests, which makes it easier to understand and predict compared to pricing based on several telemetry types.

<figure><img src="https://qa.tech/blog-assets/best-monitoring-tools-ai-first-engineering-teams-deploy-marker.png" alt="A deploy marker lined up against the response time spike that follows it."><figcaption>A deploy marker lined up against the response time spike that follows it.</figcaption></figure>

[AppSignal shipped an MCP server in 2026](https://www.appsignal.com/tour/mcp-server), allowing AI coding assistants like Claude or Cursor to query monitoring data using natural language.

There's also a [CLI](https://docs.appsignal.com/cli) built for the same audience. It defaults to human-readable output but can also switch to structured JSON with a flag. Plus, it comes with an agent skill that teaches Claude Code or Codex how to use it directly from the terminal. Basically, you’ve got two different ways to access the same data, both designed for teams that are already incorporating AI tools into their workflow.

Mind you, AppSignal is not meant to be a Datadog replacement for a 200-service platform team. It's actually aimed at developers who want full visibility without becoming a part-time observability administrator.

**Best for**: Teams that want APM and error monitoring in one place without taking on enterprise-level observability overhead.

### Scout

[Scout](https://www.scoutapm.com/) (formerly known as Scout APM) focuses on application performance. It helps you discover code execution errors, identify N+1 queries, profile memory, and detect slow endpoints. The setup process is extremely simple, and the tool does a good job pointing developers toward the line of code responsible for slowdowns.

However, the problem is that Scout is not a complete monitoring solution. For example, even though error tracking is available, it’s not particularly strong. So, the teams that want to pair Scout with error tracking tools will encounter the same old problem that AppSignal was designed to solve.

**Best for:** Developers who primarily need deep visibility into application performance.

### Honeybadger

[Honeybadger](https://www.honeybadger.io/) is also built for error tracking and app performance monitoring. In fact, it’s particularly good at error monitoring, uptime monitoring, and check-ins for catching silently failing cron jobs. The team behind it is also upfront about what the tool can and can’t do, as well as how its pricing works.

The product is described as offering “just enough APM”, which is fair. There’s nothing fancy about it. It’s not a full-fledged performance monitoring product, but it does cover the basics well.

**Best for:** Teams that mainly need reliable error and uptime monitoring.

### Sentry

[Sentry](https://sentry.io/welcome/) is arguably the best-known name on the list, and for a good reason. It’s particularly strong when it comes to error tracing, stack tracing contexts, and release tracking. Plus, it supports more than a hundred languages and frameworks.

The main issue with Sentry lies in estimating costs and how much you’ll actually need to pay. Pricing is split across five categories (errors, spans, replays, logs, and more), which implies that the price indicated on the website may end up looking very different from what a team ends up paying after Sentry has been used beyond a simple project.

Another thing you should keep in mind is that Sentry isn’t trying to be an all-in-one product. So, if you’re already using a simple error tracker like Bugsnag or Rollbar, and you’re only planning on adding Sentry for better performance tracking, you won’t be solving the underlying issue.

That’s actually what AppSignal is meant to solve. Instead of adding another tool on top of your existing stack, you can just opt for one tool that can do what the other two were doing separately.

**Best for:** Teams where error tracking depth is the priority.

### New Relic

[New Relic](https://newrelic.com/) is a true full-stack observability tool. It’s got APM, infrastructure, log integration, tracing, and plenty of other features. For teams working across a large and heterogenous service context, having all that in a platform can be invaluable.

[Pricing](https://newrelic.com/pricing), however, is where things get complicated. There are two main factors you need to consider: the amount of data you ingest per GB and the number of accounts you are using. Both can add up quickly. For instance, a five-person team sending a large volume of telemetry data could end up spending anywhere from hundreds to thousands of dollars a month. Without a good way to track usage, it can be difficult to predict what you’ll end up spending.

**Best for:** Larger teams that need broad, full-stack observability rather than a focused developer workflow.

## How They Compare

| Criteria | AppSignal | Scout | Honeybadger | Sentry | New Relic |
| --- | --- | --- | --- | --- | --- |
| Errors + APM in one tool | Yes | Partial (perf-focused) | Partial (light APM) | Perf-focused, not full APM | Yes |
| Setup time | Minutes | Minutes | Minutes | Minutes | Hours to days |
| Pricing model | Per request, flat tiers | Per transaction | Per error | Per event, five metered categories | Per GB + per seat |
| Predictable billing | Yes | Yes | Yes | Requires careful modeling | Requires active management |
| Small/mid teams | Yes | Yes | Yes | Yes at low volume | No |
| Notable strength | All in one + GVL metrics | Query-level tracing | Error and cron monitoring | Error tracking depth | Full-stack breadth |

## The Verdict

If you're a developer who wants to know what happened to your app right after a deploy, AppSignal is the right solution. It gives you insight into performance and errors within a single dashboard, takes just a few minutes to set up, and doesn’t come with any nasty surprises once the bill arrives.

If you need something more specific, though, Scout and Honeybadger are both solid choices. Scout makes sense if you want pure performance tracing, while Honeybadger is a great fit for simple error tracking and uptime monitoring. Sentry might also be useful because of its deep capabilities in error tracking, but the cost is something to keep an eye on. New Relic is the way to go after your infrastructure becomes complex enough to justify its broader capabilities and heftier price.

For most developers, though, the best tool to start with is the one that would not require them to become its manager.

[Try AppSignal free](https://appsignal.com/users/sign_up) for 30 days, no credit card required and see what's actually happening in production before your next deploy.