SaaS Observability: Error Tracking, Logging, and Performance Monitoring

In production SaaS applications, you're flying blind without observability. When a customer reports "it's slow" or "something broke," you need to know exactly what happened, where, and why — without asking them to reproduce it. That's what observability delivers: visibility into your system's internal state through the data it produces.

At Propelius Technologies, we've debugged thousands of production issues across 650+ applications. This guide covers the three pillars of SaaS observability — error tracking, logging, and performance monitoring — and how to implement them effectively.

The Three Pillars of Observability

1. Logs: The Event Timeline

Logs are timestamped records of discrete events — user actions, system events, errors, warnings. They answer "what happened and when?"

What to log:

• User authentication attempts (success/failure)
• API requests (endpoint, user, status code, duration)
• Database queries (for slow queries)
• Third-party API calls
• Background job execution
• Configuration changes
• Security events (permission denials, suspicious activity)

What NOT to log:

• Passwords, API keys, tokens
• PII unless absolutely necessary (and redact where possible)
• Credit card numbers or sensitive payment data
• Medical records or other regulated data

2. Metrics: The Pulse Check

Metrics are numerical measurements sampled over time — request count, response time, memory usage. They answer "how is the system performing?"

Essential SaaS metrics:

• Request rate: Requests per second/minute
• Error rate: 4xx and 5xx responses as percentage of total
• Latency: P50, P95, P99 response times
• Throughput: Data transferred per second
• CPU & memory: Usage per container/instance
• Database: Connection pool size, query duration, slow queries
• Queue depth: Pending background jobs

3. Traces: The Journey Map

Traces follow a single request through your entire system — across services, databases, caches, and external APIs. They answer "where is the bottleneck?"

In microservices architectures, a user action might touch 10+ services. Distributed tracing shows the entire call chain with timing for each hop.

Error Tracking: Catching Issues Before Users Report Them

Error tracking tools capture exceptions, group them intelligently, and alert your team when new issues appear or known issues spike.

What Makes Good Error Tracking

• Stack traces: Full call stack with file names and line numbers
• Context: User ID, session ID, request parameters, environment variables
• Breadcrumbs: Recent user actions leading up to the error
• Grouping: Intelligent deduplication of similar errors
• Alerts: Slack/email/PagerDuty when new errors appear or rates spike
• Source maps: Unminify JavaScript for readable frontend errors

Error Tracking Tools Comparison

Tool	Best For	Pricing	Key Features
Sentry	Full-stack apps	Free tier, $26+/month	Best breadcrumbs, release tracking, session replay
Rollbar	Backend-heavy apps	Free tier, $25+/month	RQL queries, telemetry, deploy tracking
Bugsnag	Mobile + web	$59+/month	Stability scores, release health
Airbrake	Ruby/Rails apps	$49+/month	Performance monitoring included
LogRocket	Frontend debugging	$99+/month	Session replay + console logs

Propelius recommendation: Start with Sentry. It has the best free tier, excellent SDKs for 30+ languages, and the right balance of features vs. complexity.

Logging Systems: Capturing the Event Stream

Centralized logging means all your logs flow to one place where you can search, filter, and analyze them.

Structured vs Unstructured Logging

Unstructured (old way):

2026-02-24 19:00:00 INFO User john@example.com logged in from 192.168.1.1

Structured (better):

{
  "timestamp": "2026-02-24T19:00:00Z",
  "level": "info",
  "message": "User logged in",
  "user_email": "john@example.com",
  "ip": "192.168.1.1",
  "session_id": "abc123"
}

Structured logs are queryable: user_email:"john@example.com" AND level:"error"

Logging Tools Comparison

Tool	Best For	Pricing	Notes
Datadog	Enterprise	$15+/host/month	Full observability platform, APM included
ELK Stack	Self-hosted	Free (infra costs)	Elasticsearch + Logstash + Kibana
Papertrail	Small teams	Free tier, $7+/month	Simplest setup, good for startups
Loggly	AWS-centric	Free tier, $79+/month	Fast search, AWS integrations
Grafana Loki	Kubernetes	Free (self-hosted)	Like Prometheus but for logs

Performance Monitoring (APM): Measuring Speed

Application Performance Monitoring (APM) tracks how fast your application responds and where time is spent.

Key Performance Metrics

• Apdex score: User satisfaction based on response time (0-1 scale)
• Throughput: Requests handled per minute
• Error rate: Percentage of failed requests
• Transaction traces: Slow request breakdown (database, external APIs, rendering)

APM Tools Comparison

Tool	Best For	Pricing	Strengths
New Relic	Full-stack visibility	Free tier, $99+/month	Real user monitoring, synthetics
Datadog APM	Microservices	$31/host/month	Best service maps, unified platform
AppDynamics	Enterprise Java	Custom pricing	Business transaction monitoring
Elastic APM	ELK users	Free (self-hosted)	Integrates with existing ELK stack
Scout APM	Ruby/Python/PHP	$199+/month	Minimal overhead, great UX

Implementation Strategy for SaaS Startups

Phase 1: MVP Stage (Month 1-3)

• Error tracking: Sentry (free tier)
• Logging: Papertrail or native cloud logging (AWS CloudWatch, GCP Stackdriver)
• Performance: Basic uptime monitoring (UptimeRobot, Pingdom)

Cost: $0-20/month

Phase 2: Growth Stage (100+ customers)

• Error tracking: Sentry paid tier
• Logging: Datadog or ELK Stack
• Performance: New Relic or Datadog APM
• Real User Monitoring: Track actual user experience

Cost: $300-1,000/month

Phase 3: Scale Stage (1,000+ customers)

• Full observability platform: Datadog or New Relic
• Distributed tracing: Jaeger or Datadog APM
• Custom dashboards: Grafana for business metrics
• Synthetic monitoring: Simulate user journeys
• On-call rotation: PagerDuty integration

Cost: $2,000-10,000/month

Best Practices

Use Log Levels Correctly

• DEBUG: Detailed diagnostic info (turn off in production)
• INFO: Normal events (user logged in, order created)
• WARN: Unexpected but handled (rate limit hit, retry queued)
• ERROR: Something failed but app continues (payment declined)
• FATAL: Critical failure requiring immediate attention

Add Correlation IDs

Generate a unique ID for each request and include it in every log entry, error, and trace. This lets you reconstruct the entire user journey across services.

Avoid Alert Fatigue

Only alert on:

• New error types (not already acknowledged)
• Error rate above threshold (not single occurrences)
• Critical metrics degradation (P95 latency >2s)
• System health checks failing

Too many alerts = ignored alerts = missed incidents.

Set Retention Policies

Storage costs add up fast. Typical retention:

• Errors: 90 days (or until resolved)
• Logs: 7-30 days for detailed logs, 1 year for aggregated
• Metrics: Raw for 30 days, downsampled for 1 year
• Traces: Sample 1-10%, keep 7 days

FAQs

Do I need error tracking, logging, AND performance monitoring?

Start with error tracking (Sentry) and basic logging. Add performance monitoring once you have 50+ customers and can afford $100+/month. All three together give you complete visibility, but error tracking is non-negotiable from day one.

How much does observability cost at scale?

Budget 2-5% of infrastructure spend. For a company spending $10K/month on servers, expect $200-500/month on observability tools. Datadog or New Relic can hit $1,000-5,000/month at significant scale.

Should I log to files or stdout?

Log to stdout and let your container orchestration (Kubernetes, ECS, Docker) capture it. This is the 12-factor app pattern and makes centralization trivial. Avoid writing to disk unless you're on bare metal or VMs.

What's the difference between APM and RUM?

APM (Application Performance Monitoring) measures server-side performance. RUM (Real User Monitoring) measures client-side performance from actual user browsers. You need both — APM shows backend bottlenecks, RUM shows frontend rendering and network latency.

Can I self-host everything instead of paying for SaaS tools?

Yes, using ELK Stack (logs), Prometheus + Grafana (metrics), and Jaeger (traces). You'll save on tool costs but pay for engineering time, server capacity, and maintenance. SaaS tools are worth it unless you have dedicated DevOps team or strict data residency requirements.

Conclusion

Observability isn't optional for production SaaS applications — it's the difference between debugging in minutes vs. hours, and catching issues before customers notice vs. after they churn.

Start simple: Sentry + basic logging. Add complexity as you grow and can afford the tooling and engineering time.

Instrument everything: Every API endpoint, database query, external call, and user action should generate telemetry.

Alert wisely: Only page on what matters. Everything else can wait for morning.

At Propelius Technologies, we build observability into every SaaS application from day one. Our 30-day MVP sprints include Sentry integration, structured logging, and basic performance monitoring as standard. Schedule a consultation to discuss your observability needs.