Errors get a bad rap, but they’re just trying to help. Remember, errors aren’t the enemy, they’re the messenger.
Conventional wisdom tells you to think of errors as failures, as things that thwart progress and frustrate developers. The reality is that errors are actually there to help you. They prevent you from shipping broken code to production. They stop your application from continuing to operate incorrectly and costing you money. They're warning signs pointing directly at the weaknesses in your system.
The real problem isn't errors themselves. It's not knowing about them.
The State of Error Monitoring in 2026
The monitoring landscape has evolved dramatically over the past few years. We've gone from basic exception tracking to fully integrated platforms that correlate errors with performance metrics, logs, and even AI-powered debugging.
Even in the face of the AI explosion, the more things change, the more things stay the same. As of 2026, most teams are still drowning in data without getting answers.
Enterprise platforms like Datadog and New Relic have expanded into everything you can imagine: infrastructure monitoring, APM, logs, security, real user monitoring, synthetic testing, etc. Sentry has built an impressive error-focused platform with session replay and performance tracing. The feature lists keep growing. The dashboards keep multiplying.
And yet, small to mid-sized teams tell us the same thing: "We have a lot of data. We just don't know what it means or what to do about it."
That's the gap we're focused on at Scout. Not more visibility, more answers. If you want to see what we’re building in action, check out this overview of our Errors feature with one of our senior engineers.
Why Manual Error Monitoring Doesn't Work
Let's be honest about manual monitoring, aka amassing piles of logs and trying to sift through them when something goes wrong. It doesn't work. Here's why:
There are long stretches where everything runs smoothly. You're watching dashboards, refreshing logs, and nothing happens. You wonder why you’re paying for your monitoring tools (or why you're celebrating your decision to turn them off or never implement them in the first place). Then, seemingly out of nowhere, a cascade of errors hits your system. Good luck analyzing that in real time while your users are experiencing issues and your stakeholders are asking questions.
The manual approach is inefficient, unproductive, and exhausting. It's also expensive. You're paying developers to stare at screens instead of building features.
Why waste time and money on something that can be automated with more efficiency and better insights?
The Real Benefits of Error Monitoring
Find the Root Cause, Not Just the Symptom
Modern error monitoring isn't just about knowing that something broke. It's about understanding why and where. With tools like Scout, you get full-stack traces with GitHub integration that take you directly to the line of code causing the issue. You see the request parameters and custom context that led to the error. You can correlate error spikes with performance degradation to understand cause and effect.
Stop Chasing Errors Across Multiple Tools
The best error monitoring integrates with your APM data. When errors and performance live in the same interface, you can see the full picture. Did that spike in 500 errors happen because of a slow database query? Is the timeout causing the error, or is the error causing the timeout? Without integration, you're switching between tools trying to piece together a story.
Triage Efficiently at Scale
When you're dealing with hundreds or thousands of errors, you need intelligent grouping and prioritization. Good error-monitoring tools automatically group similar errors, flag critical endpoint failures as high priority, and let you bulk-assign issues to team members. You need to be able to resolve, defer, or reactivate errors with clear audit trails.
Keep Your Team Informed Without the Noise
Instant notifications via Slack, email, PagerDuty, or webhooks enable your team to respond to new issues quickly. Smart alerting is also about reducing noise. You don't need a notification for every occurrence of a known issue you're already working on.
What to Look for in an Error Monitoring Tool
With so many options available, here are the factors that actually matter:
- Integration depth: Does error monitoring connect with your APM, logs, and traces? Or is it another siloed tool?
- Language and framework support: Does it work with your stack? If you're running Rails or Django, make sure the tool has first-class support, not just basic coverage.
- Intelligent grouping: Can it distinguish between unique issues and variations of the same error? Noise reduction matters.
- Context capture: Does it automatically collect request parameters, session data, and custom context? The more context, the faster you can debug.
- Pricing predictability: Usage-based pricing sounds fair until an incident spikes your error volume and your bill. Understand how costs scale.
- Setup complexity: How long until you start seeing value? Some tools require extensive configuration. Others work out of the box.
A Comparison of Error Monitoring Tools
Let's look at the major players and what they offer:
Scout is built for a specific audience: Ruby, Python, Elixir, and PHP teams who want to fix problems, not become monitoring experts.
Where most tools give you dashboards and leave interpretation to you, Scout is designed around the question "what do I do about this?" Error monitoring is fully integrated with APM with errors, traces, and logs in one interface. When an error spikes, you can immediately see if it correlates with a performance regression, drill into the trace, and find the problematic code without switching tools or piecing together data from multiple sources.
Key features include intelligent error grouping with automatic context capture, critical endpoint tagging for automatic prioritization, team collaboration with assignment and triage workflows, GitHub integration for code-level visibility, and 30-day retention of full error data.
Pricing is simple: a generous free tier gets you access to everything including 5k errors. If you need more than that, you can get 50,000 additional errors for just $19. No per-seat charges, your whole team gets access. No surprise bills when traffic spikes.
Scout also offers an MCP Server for teams using AI coding assistants. Your AI can query Scout directly to understand performance issues and generate fixes with full context.
The tradeoff: Scout supports Ruby, Python, PHP, and Elixir. If you need JavaScript frontend monitoring or mobile SDKs, you'll need to look elsewhere.
Sentry
Sentry is the established leader in error tracking, used by over 100,000 organizations. They've expanded into session replay, performance monitoring, and recently added Sentry Logs and an AI debugging agent called Seer.
Sentry's breadth is impressive, they support nearly every language and framework. If you're running a polyglot stack with React frontends, mobile apps, and multiple backend languages, Sentry's coverage is hard to beat.
Where Scout wins: Sentry's event-based pricing can become unpredictable at scale. At 100K errors per day, you're looking at roughly $440/month; at 1M errors/day, that jumps to around $3,600/month. An incident that spikes your error volume can spike your bill too. Scout's transaction-based pricing is more predictable, you know what you're paying regardless of how many errors occur.
Sentry is also primarily an error tool that added performance monitoring. Scout has an APM that treats errors as first-class citizens alongside performance. The integration depth is different: in Scout, errors and traces live in the same view by design, not as bolt-on features.
Datadog
Datadog is a comprehensive observability platform including infrastructure monitoring, APM, logs, security, RUM, synthetics, and more. For large DevOps teams managing complex multi-cloud infrastructure, it's powerful.
Where Scout wins: Datadog is built for organizations with dedicated DevOps teams. The pricing reflects that: infrastructure monitoring runs $15-23/host/month, APM starts at $31/host/month, and the modules add up quickly. Mid-sized companies routinely spend $50,000-150,000/year; enterprise deployments can exceed $1 million annually.
For a Rails or Django team without dedicated infrastructure engineers, Datadog is overkill. You're paying for Kubernetes monitoring, security scanning, and 850+ integrations you'll never use. Scout gives you what you actually need, APM, errors, logs, and traces, in an interface designed for application developers, not platform engineers.
Scout's agent also adds just 2.2% overhead in benchmarks, substantially less than enterprise APM tools which is important when you're optimizing for performance, not just measuring it.
New Relic
New Relic offers full-stack observability with their Errors Inbox feature for tracking and triaging errors across your stack. They've been a market leader since 2008 and recently positioned around AI-powered insights.
The free tier is generous: 100GB of data ingestion and one full platform user. For teams just getting started with monitoring, it's an easy entry point.
Where Scout wins: New Relic's pricing model combines user-based fees (starting at $10/user for full platform access) with data ingestion costs ($0.35-0.55/GB beyond the free tier). Many customers report sticker shock as their applications grow—especially in microservices environments where data volumes are hard to predict.
The platform is also complex. New Relic does a lot, which means there's a lot to learn. Scout's focused feature set means you're productive in minutes, not weeks. And because Scout doesn't charge per seat, your whole team can access monitoring without budget negotiations.
AppSignal
AppSignal is a developer-focused APM built for Ruby, Elixir, Node.js, and Python teams. Founded in 2012 by a small team in the Netherlands, they've built a reputation for simplicity and predictable pricing.
Their "no surprise bills" policy is genuinely developer-friendly: they don't charge for overages, and they won't even discuss upgrades unless you're over your plan limits for 2 out of 3 months. Plans start at $23/month with unlimited apps and hosts.
Where Scout wins: AppSignal and Scout share similar philosophies of focused tools for application developers, not sprawling enterprise platforms. The difference is in the details.
Scout's "actionable vs. observable" approach goes deeper than dashboards. Features like critical endpoint tagging (automatic high-priority flagging for your most important routes) and the MCP Server (AI-native debugging) reflect a focus on answering questions, not just displaying data.
Scout's Ruby and Python instrumentation is also purpose-built for those ecosystems. Some AppSignal users note their Python support could be stronger while Scout treats Python as a first-class citizen alongside Ruby.
Honeybadger
Honeybadger combines error tracking, APM, uptime monitoring, and cron job check-ins. Like Scout, they're a small bootstrapped team (founded 2012) that prioritizes developers over growth metrics.
Their error tracking is solid: real-time alerts, smart de-duping, and context-rich events. The newer Honeybadger Insights feature adds log querying and anomaly detection. Plans start at $26/month, with a free Developer tier for solo projects.
Honeybadger is consistently praised for exceptional support—developer-to-developer responses, fast turnaround, and a willingness to ship features based on customer requests.
Where Scout wins: Honeybadger positions itself as complementary to tools like New Relic and Datadog rather than a replacement. That's honest, but it also means you might need multiple tools to get full coverage.
Scout is designed as a complete solution for Ruby and Python teams. APM, errors, logs, and traces in one place, with deep integration between them. You don't need Honeybadger for errors plus Datadog for APM plus Papertrail for logs—Scout handles all of it in a single, coherent interface.
The Bottom Line
Every tool on this list can track errors. The question is what happens after you know an error occurred.
If you need broad language coverage and can manage complexity, Sentry or Datadog might be the right choice. If you're a large enterprise with dedicated DevOps resources, New Relic or Datadog can scale with you.
But if you're a Ruby or Python team that wants to spend time shipping features instead of configuring dashboards, if you want answers instead of data, Scout is built for you.
Why Integration Matters
Here's the case for integrated error monitoring in one scenario:
Your application starts throwing timeout errors. In an error-only tool, you see the stack trace. You know what is failing. But you don't know why.
With integrated APM and error monitoring, you can see that response times on that endpoint spiked at the same time the errors started. You drill into a trace and discover a particular database query is taking 10x longer than usual. You check the query analyzer and find a missing index on a recently-added column.
Root cause identified. Fix deployed. The whole investigation took minutes instead of hours.
That's what we mean by "actionable vs. observable." It's not about seeing more data. It's about getting to answers faster.
Getting Started with Scout Error Monitoring
If you're running Ruby or Python applications and want error monitoring that actually helps you fix problems, Scout is designed for teams like yours. Scout specializes in their supported languages as opposed to generalizing into broader support which means less time doing interpretation for your team.
Setup takes about five minutes:
For Ruby/Rails, add the gem to your Gemfile, set errors_enabled: true in your configuration, and deploy. The agent automatically captures exceptions with full context.
For Python (Django, Flask, FastAPI, and more), install scout-apm via pip, set SCOUT_ERRORS_ENABLED=true, and deploy.
Errors start flowing into Scout immediately, grouped intelligently and correlated with your performance data.
Every plan (even the free tier) includes unlimited users and applications. You're not penalized for growing your team. And with a 14-day free trial (no credit card required), you can see the value before committing. Find the free tier suits your needs for now? Great, we’re here for it.
Conclusion
Error monitoring isn't optional. The cost of ignorance in revenue, reputation, and developer sanity is too high.
The question isn't whether to monitor errors. It's whether your current approach is giving you answers or just more data to sift through.
If you're spending more time configuring dashboards than fixing bugs, if you're switching between tools to piece together what happened, if your monitoring bill grows faster than your application, it might be time to try something built for teams who ship code, not teams who manage infrastructure.
Start a free trial with Scout or schedule a demo to see how integrated error monitoring can change the way you support your production apps.
Frequently asked questions
What features should I look for in a real-time error monitoring tool?
Look for rich context (stack traces, session data), noise‑filtered alerts, strong integrations, and deployment flexibility that fits your security and compliance needs.
How do real-time error alerts help reduce downtime?
They notify teams the moment issues occur and include context to act quickly, directly reducing mean time to detect and repair.
What integrations are important for effective error monitoring?
Tight integrations with alerting tools, issue trackers, collaboration tools, and major cloud platforms enable fast triage, assignment, and resolution.
How can error monitoring improve developer workflows?
By surfacing actionable diagnostics where developers work, reducing alert fatigue, and streamlining handoffs across teams for faster fixes.
What factors influence the total cost of ownership for error monitoring tools?
Licensing, data volume and retention, integration and maintenance effort, and the operational gains from lowering MTTR all impact total cost.
