Laravel developers in 2026 have three mature monitoring options, each built for a different layer of the stack. Telescope gives you a microscope into individual requests. Pulse gives you a dashboard for application-level trends. Deploynix monitoring gives you infrastructure-level visibility with health alerting.
The question isn't which one is best. The question is which combination you need — because they're not competitors. They're complementary tools that, when used together, give you complete observability from the server hardware up through individual Eloquent queries.
This post breaks down what each tool does, where they overlap, how they differ in performance overhead, and which setup makes sense based on your application's size and stage.
What Each Tool Actually Does
Before comparing features, it helps to understand the fundamental design philosophy behind each tool.
Laravel Telescope: The Development Debugger
Telescope is a debugging assistant. It records every request, exception, database query, queued job, mail, notification, cache operation, and scheduled task that your application processes. You browse this data through a web dashboard that lets you inspect individual entries in detail.
Telescope answers questions like: What exact SQL queries did this request execute? What was the payload of this queued job that failed? Why did this notification take 3 seconds to send?
It shines during development and staging. In production, its recording overhead and storage requirements make it impractical for high-traffic applications unless heavily filtered.
Laravel Pulse: The Application Dashboard
Pulse is an application performance monitoring tool. Instead of recording every individual event, it aggregates data into trends and surfaces the metrics that matter: slowest requests, most frequent queries, queue throughput, cache hit rates, and active user counts.
Pulse answers questions like: Which endpoints are consistently slow this week? Are my queue workers keeping up with job volume? What's my cache hit ratio trending toward?
It's designed for production use. Its aggregation approach keeps storage requirements manageable, and its sampling capabilities let you control the performance overhead.
Deploynix Monitoring: The Infrastructure Layer
Deploynix monitoring operates at the server and site level. It tracks CPU usage, memory consumption, disk I/O, network throughput, and disk space across all your managed servers. It provides real-time alerts when metrics cross thresholds you define.
Deploynix answers questions like: Is the server running out of memory? Has CPU usage been sustained above 90% for the last hour? Is the application responding to HTTP requests? When did disk usage cross the warning threshold?
It doesn't know about your Laravel application's internals — it doesn't see individual queries or jobs. But it sees everything happening at the operating system level, which is exactly the layer you need visibility into when your application monitoring tools report that "everything looks fine" but users are experiencing slowness.
Feature Comparison
Here's a direct comparison of what each tool monitors:
Capability
Telescope
Pulse
Deploynix
Individual request inspection
Yes
No
No
Request performance trends
No
Yes
No
Database query logging
Yes (all)
Yes (slow)
No
Exception tracking
Yes
Yes
No
Queue job inspection
Yes
Yes
No
Cache operations
Yes
Yes
No
CPU/Memory monitoring
No
No
Yes
Disk space alerts
No
No
Yes
Server health monitoring
No
No
Yes
Real-time server metrics
No
No
Yes
Mail/notification logging
Yes
No
No
Model event tracking
Yes
No
No
Gate/policy inspection
Yes
No
No
Health check alerting
No
No
Yes
User activity tracking
No
Yes
No
Performance percentiles
No
Yes
No
The overlap is minimal. Telescope and Pulse both track queries and exceptions, but in fundamentally different ways. Telescope records every single query for later inspection. Pulse aggregates query performance into trends and highlights outliers. Deploynix doesn't touch queries at all — it watches the MySQL process's resource consumption from the OS level.
Performance Overhead
This is where the differences matter most in production.
Telescope's Overhead
Telescope's default configuration records everything. Every request generates multiple database inserts for the request entry, query entries, model entries, and any other watchers you have enabled. On a high-traffic application, this creates significant database load.
You can mitigate this with selective recording:
// app/Providers/TelescopeServiceProvider.php
public function register(): void
{
Telescope::filter(function (IncomingEntry $entry) {
if ($this->app->environment('local')) {
return true;
}
return $entry->isReportableException() ||
$entry->isFailedRequest() ||
$entry->isFailedJob() ||
$entry->isSlowQuery() ||
$entry->isScheduledTask();
});
}
Even with filtering, Telescope adds measurable overhead per request because the filtering logic itself runs on every request. For applications handling hundreds of requests per second, this matters.
Recommendation: Enable Telescope fully in local and staging environments. In production, either disable it entirely or filter aggressively to only capture failures.
Pulse's Overhead
Pulse was designed for production from day one. It uses an aggregation pipeline that batches metrics before writing them to the database, reducing write frequency dramatically compared to Telescope.
Pulse also supports sampling — you can tell it to only record every Nth request:
// config/pulse.php
'sample_rate' => 0.1, // Record 10% of requests
At a 10% sample rate, Pulse gives you statistically meaningful trend data with a fraction of the overhead. The aggregated storage is also much smaller — Pulse automatically prunes old data based on configurable retention periods.
Recommendation: Run Pulse in production with sampling enabled. A 10-25% sample rate provides excellent trend data for most applications.
Deploynix's Overhead
Deploynix monitoring runs at the OS level, completely outside your Laravel application. It uses lightweight system agents that collect metrics from /proc, sysstat, and similar system interfaces. Your application doesn't execute a single extra line of PHP.
The HTTP health check adds one request every 30-60 seconds (configurable), which is negligible on any application.
Recommendation: Always enable Deploynix monitoring. There's no meaningful overhead to your application.
Recommended Setups by Application Size
Solo Developer / Side Project
Use: Deploynix monitoring only.
When you're launching an MVP or running a small project, you don't need application-level monitoring yet. Deploynix's built-in server monitoring and health alerts will tell you if something goes catastrophically wrong. Use Telescope locally during development to debug issues, but don't bother installing it in production.
At this stage, your time is better spent building features than configuring monitoring dashboards. Deploynix handles the infrastructure concerns so you don't have to SSH into servers to check disk space or CPU usage.
Growing Application (1,000+ Daily Users)
Use: Deploynix monitoring + Pulse.
Once your application serves real traffic consistently, you need trend data. Pulse shows you which endpoints are slowing down, whether your cache strategy is working, and if queue processing is keeping pace with demand.
Install Pulse with a 25% sample rate:
'sample_rate' => 0.25,
This gives you solid performance data without measurable overhead. Combined with Deploynix's server metrics, you can correlate application slowness (Pulse) with infrastructure bottlenecks (Deploynix).
For example, Pulse might show that your /api/reports endpoint's P95 response time jumped from 200ms to 1,200ms. Deploynix's server monitoring might simultaneously show that MySQL's CPU usage spiked to 95%. Now you know the problem is a database query issue on that endpoint, not a code regression or memory leak.
Production Application (10,000+ Daily Users)
Use: Deploynix monitoring + Pulse + Telescope (filtered).
At this scale, you need all three layers. Pulse gives you the trends. Deploynix gives you infrastructure alerting. And Telescope — heavily filtered to only capture failures and slow queries — gives you the detailed forensics you need when something goes wrong.
Configure Telescope to only record problems:
Telescope::filter(function (IncomingEntry $entry) {
return $entry->isReportableException() ||
$entry->isFailedRequest() ||
$entry->isFailedJob() ||
$entry->type === EntryType::SLOW_QUERY;
});
When Pulse's dashboard shows a spike in slow requests, switch to Telescope to inspect the individual queries and request payloads that caused it. When Deploynix alerts you to high memory usage, check Pulse to see if a particular endpoint is processing larger payloads than usual.
High-Traffic Application (100,000+ Daily Users)
Use: Deploynix monitoring + Pulse (low sample rate) + dedicated APM.
At very high traffic volumes, consider adding a dedicated APM service (New Relic, Datadog, or similar) alongside Pulse and Deploynix. Telescope's database storage model doesn't scale well at this level even with aggressive filtering.
Run Pulse at a 5-10% sample rate for trend data. Use Deploynix for infrastructure monitoring and alerting. Let your APM handle the detailed request tracing that Telescope would otherwise provide.
Making Them Work Together
The real power emerges when you use these tools as a unified observability stack rather than isolated dashboards.
Triage workflow:
- Deploynix alerts you that a server's CPU has been above 90% for 10 minutes.
- Open Pulse to check which endpoints or queues are consuming the most resources.
- If Pulse points to a specific endpoint, open Telescope to inspect recent requests to that endpoint and find the problematic query or operation.
- Fix the issue, deploy through Deploynix, and watch all three dashboards confirm the resolution.
Performance investigation workflow:
- Pulse shows P95 response time increasing over the past week.
- Drill into Pulse's slow queries tab to identify which queries are degrading.
- Check Deploynix's server metrics to see if the database server's disk I/O is saturated.
- If disk I/O is the bottleneck, use Deploynix to scale up the database server or add read replicas.
Configuration Tips
Don't Run Telescope and Pulse on the Same Database
Both tools write monitoring data to your database. In production, configure them to use a separate database connection to avoid their writes competing with your application's queries:
// config/pulse.php
'storage' => [
'driver' => 'database',
'connection' => 'pulse',
],
// config/database.php
'pulse' => [
'driver' => 'mysql',
'host' => env('PULSE_DB_HOST', '127.0.0.1'),
'database' => env('PULSE_DB_DATABASE', 'pulse'),
// ...
],
Set Retention Policies
Both Telescope and Pulse accumulate data. Configure pruning to prevent storage from growing unbounded:
// Telescope: prune entries older than 48 hours
$schedule->command('telescope:prune --hours=48')->daily();
// Pulse: configure in config/pulse.php
'retain' => [
'entries' => 7 * 24 * 60, // 7 days in minutes
'aggregates' => 90 * 24 * 60, // 90 days in minutes
],
Use Deploynix's Monitoring for Alerting
Neither Telescope nor Pulse has built-in alerting. They're dashboards you check, not systems that notify you. Deploynix fills this gap with health alerts that trigger notifications when your servers or sites enter unhealthy states.
Pair Deploynix's server-level health alerts with an external uptime monitoring service that polls your custom health check endpoint, and you get alerts that combine infrastructure metrics with application health data.
Conclusion
Telescope, Pulse, and Deploynix monitoring aren't competing solutions — they're complementary layers of a complete observability stack. Telescope gives you the microscope for debugging individual issues. Pulse gives you the dashboard for tracking application performance trends. Deploynix gives you the infrastructure monitoring and alerting foundation that the other two are built on top of.
Start with Deploynix monitoring on every server. Add Pulse when you need application-level trend data. Layer in Telescope when you need forensic debugging capabilities in production. Together, they give you visibility from the CPU cores up through individual database queries — and the confidence that you'll know about problems before your users do.
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