What are logs, metrics, traces, and monitoring?
Before you integrate anything, get clear on the signals you are working with. Each one answers a different question, and each one has a different cost profile.
Logging is the practice of collecting and storing timestamped event records that describe what happened inside an application or system component. Logs are usually plain text or structured JSON, and they answer the question "what happened?" They are essential for troubleshooting, auditing, and compliance.
Metrics are numeric time-series data such as counters, gauges, and histograms that measure system behavior over time. Metrics answer "is the system healthy?" and drive alerting and trend analysis. Prometheus, for example, stores numeric time-series metrics and hands off notifications to Alertmanager.
Traces, often called distributed traces, capture the full journey of a request as it travels through a distributed system. Picture your application as a web of microservices. A trace is the thread that connects every hop, database query, and API call made to fulfill a single user action, like clicking "Buy Now" on an e-commerce site.
Observability is the discipline of understanding system state by collecting logs, metrics, and traces, then applying dashboards, alerts, and analysis to keep systems reliable. Monitoring and observability are often used interchangeably, but they are different approaches to the same problem. One simple distinction: monitoring tells you when a system is broken, and observability helps you understand why it broke, especially when it breaks in a way you have not seen before.
These signals are most useful together. During an incident, your team moves between logs, metrics, and traces. Unified tooling reduces context switching and speeds resolution.
Why combine logs and metrics in the first place?
Correlating logs and metrics helps both operations and budgeting.
For operations, combining signals shortens detection time and speeds up root cause analysis. Your team can pivot directly from a metric alert to the related log lines or traces without switching tools. When error rates spike, you can see the log events behind them instead of searching for them manually.
For cost control, high-volume logs become expensive quickly. Splunk, for example, has historically licensed by ingest volume, so costs rise as your logs grow. Converting repetitive logs into metrics reduces indexing expense while retaining the visibility you need. A telemetry pipeline enables event-to-metric conversions and also handles privacy requirements by routing or redacting data before it reaches storage. Cribl performs those conversions in-stream, so savings appear before data lands anywhere.
Expect correlated alerts across data types, less tool switching during incidents, lower observability spend through routing and data reduction, and stronger compliance through in-pipeline redaction. Cribl's approach to practical logs-to-metrics conversion supports those goals by turning large log volumes into concise metrics before indexing.
Which architecture should you choose for integration?
Architecture is the most important decision when combining logging, metrics, and monitoring. Two models dominate: a unified observability platform, or a modular best-of-breed stack joined by a telemetry pipeline.
Evaluate both against your actual log volume, query patterns, retention requirements, and goals. Feature checklists will not tell you which one fits.
Unified observability platforms
A unified observability platform stores and correlates logs, metrics, and traces inside one data model and one interface.
The upside is faster setup, native cross-signal links, and simpler day-to-day operations. The tradeoff is vendor lock-in, less room to optimize each signal separately, and scaling costs that increase with volume. Many teams put a Cribl pipeline in front of their unified platform to manage ingestion and control costs.
Best-of-breed stacks with telemetry pipelines
A telemetry pipeline sits between your data sources and your destinations, where it routes, filters, enriches, and reshapes observability data in real time before storage. Cribl Stream is designed for this job.
Common combinations pair Prometheus and Grafana for metrics, ELK or Loki for logs, and Jaeger or Tempo for traces. Prometheus focuses on metrics, while the Elastic Stack handles log search and indexing.
The pipeline routes logs to the right tool, redacts sensitive fields before transfer, converts events to metrics to reduce indexing costs, and replays or duplicates streams for migrations and testing. Cribl Stream integrates with OpenTelemetry collectors to extend those functions. For a deeper primer on the signals themselves, see Logs, Events, Metrics, and Traces, Oh My.
How do you implement correlated logging and metrics step by step?
This six-step checklist reflects how experienced SRE and platform teams roll out combined observability. Work through it in order.
Step 1: Define SLOs and identify relevant signals
Start by defining Service Level Objectives such as 99.9% availability or p95 latency under 200 ms. Then identify which metrics and log events actually track those goals.
Use the four golden signals—latency, traffic, errors, and saturation—as your core metrics for user-facing health. Link each SLO to the specific metrics and logs needed for root-cause analysis. Do this well and every alert leads somewhere useful.
Step 2: Conduct ingestion and query performance tests
Validate your data volumes before you lock in an architecture. Run one to two weeks of production logs through your candidate design to estimate cost. Check query speed under realistic retention windows, because performance matters most during an incident.
Document your daily ingest rate, query latency at 7 and 30 days, and projected annual storage cost. Those three numbers should drive your architecture decision.
Step 3: Deploy OpenTelemetry agents and data pipelines
OpenTelemetry collects logs, metrics, and traces in a vendor-neutral format. Pairing OTel collectors with Cribl Stream gives you a flexible split: OTel handles collection, and Cribl handles routing and transformation.
Cribl Edge is a lightweight collector that gathers data at the source and forwards it to Stream for centralized processing. A typical flow looks like this: Application to OTel SDK to OTel Collector to Cribl Stream, then out to Prometheus for metrics, Elasticsearch for logs, and S3 for archives.
Explore more data collection use cases for logs to see how this plays out across sources.
Step 4: Convert events to metrics and apply in-stream processing
This step gives the largest cost and performance benefit. Event-to-metric conversion extracts numeric measures from log events and emits time-series metrics, cutting storage requirements without cutting visibility.
Turn HTTP access logs into request_count and latency_p99 metrics. Turn error logs into error_rate_by_service. While in the pipeline, parse unstructured logs into structured JSON, sample verbose sources, and redact PII. Compare output before and after to measure data reduction.
For a hands-on walkthrough, see Cribl's guide to extracting metrics from logs.
Step 5: Establish alerting linked to logs and traces
Every alert should include contextual links so investigators can jump straight to the relevant log query or trace for the same time window.
Grafana visualizes Prometheus metrics with queries such as rate(http_requests_total[5m]), and its integration with Loki and Tempo supports pivoting from metric to log to trace.
Do not rely on static thresholds alone. Align alerts with SLO error budgets and add anomaly detection where it helps.
Step 6: Optimize data retention and storage tiering
Storage tiering sends data to different backends based on how often you query it and how long you need to keep it. Hot tiers are fast and costly. Cold tiers are slower and cheaper. Cribl routes telemetry according to how it is actually used: frequently accessed data goes to fast analytics tools, less-used data to a lake or lower-cost storage, and archival data to long-term retention.
Upstream, Cribl Stream filters, shapes, and enriches data before it lands, so each destination receives the right fidelity at the right cost instead of everything piling into one expensive system.
Cribl Lake stores telemetry in open, non-proprietary formats, giving you an affordable way to retain full-fidelity logs, metrics, and traces for the long term with no lock-in. Stream can then collect, process, enrich, route, and replay that telemetry, sending the right data to the right downstream tool in the required format for investigation, troubleshooting, compliance, or broader analysis. Store more in Lake, and let Stream activate it when it becomes valuable.
Cribl Search lets your team search across tiers in place, so you can investigate or promote data without rehydration delays. Search provides a unified analytics experience across Cribl Lake and other data sources, with fast queries, customizable dashboards, charts, tables, and scheduled searches for ongoing monitoring. Dashboards support interactive panels, inputs, and drilldowns, so you can move from a high-level trend to the detailed event without stitching multiple tools together.
Treat these windows as starting points and adjust them based on your actual query patterns. In most environments the bulk of queries hit only the most recent few days of data, which is what makes tiering pay off.
What operational best practices keep combined observability healthy?
Once your combined system is live, these six practices keep it reliable and efficient:
Use structured logging. JSON with consistent fields like timestamp, service, level, and correlation ID makes parsing and correlation straightforward.
Monitor the pipeline itself. Track throughput, backpressure, and errors so data never disappears silently. Cribl Stream's built-in monitoring covers this.
Keep metric labels stable. Avoid high-cardinality values such as user IDs as labels. They increase cost and slow queries.
Test queries under load. Regularly check dashboard and log query latency under simulated incident conditions.
Link alerts to SLO error budgets. Replace arbitrary thresholds with burn-rate alerts.
Review and trim data quarterly. Audit your signals and drop anything unused or low value.
How does Cribl compare with other data observability solutions?
Cribl operates as the telemetry pipeline between your sources and your destinations. It complements Prometheus, Elasticsearch, and similar tools by controlling what data is sent, in what format, and at what volume. You do not need to replace existing tools; you add a control layer in front of them.
What the control layer provides:
Vendor neutrality. Route the same data to Splunk, S3, and Elasticsearch at once, and change destinations without touching agents.
Log-to-metric conversion. Transform log events into metrics in flight to cut indexing cost.
Data reduction. Filter, sample, or trim unnecessary fields before storage.
Compliance and privacy. Mask sensitive content before it is written anywhere.
Edge plus Stream architecture. Collect at the source with Cribl Edge and process centrally with Cribl Stream.
This control layer helps avoid lock-in and offers more transformation and routing options than a basic OTel collector. For a concrete integration example, see Cribl's guide on a low-code approach to getting logs and metrics into New Relic with Cribl LogStream.
What tradeoffs should you weigh when combining logs, metrics, and monitoring?
No architecture is free of tradeoffs. Here are five to consider.
Unified platform vs. modular stack. Unified systems are simpler but less flexible. Modular stacks offer customization and cost control but require more management.
Data completeness vs. cost control. Storing everything gives full insight at a high price. Filtering saves money but risks dropping signals. Converting logs to metrics and tiering storage balances the two.
Real-time alerting vs. deep analysis. Metrics provide fast detection. Logs provide depth. Combining them gives you both.
Open-source vs. commercial. Open-source tools like Prometheus and ELK reduce licensing costs but require engineering effort to run at scale.
Centralization vs. edge processing. Processing at the edge with Cribl Edge reduces transfer overhead. Central processing with Cribl Stream simplifies management. Many mature setups use both.
Revisit these tradeoffs regularly. Your data volumes and requirements will change, and your architecture should be allowed to change with them.
Stop choosing between full visibility and a sane budget
The old bargain in observability was index everything and pay for it, or drop data and hope you never need it. Combining logging, metrics, and monitoring the right way changes that. When a vendor-neutral pipeline is at the center of your telemetry, you decide what gets collected, how it is shaped, and where it lands. That is the premise behind Cribl, and it is why many customers use the platform for their IT and security data.
The data engine at the heart of Cribl's platform handles the mechanics this guide describes. Stream converts noisy logs into metrics, redacts sensitive fields, and routes each signal to the tool where it provides the most value. Edge collects at the source with a small footprint. Lake retains full-fidelity telemetry in open formats at object-storage economics. Search lets your analysts and tools query it all in place, without rehydration and without lock-in.
The result is telemetry that serves your team: correlated alerts, faster root cause analysis, and a bill that reflects the value of your data rather than raw volume. If you are ready to try this with your own logs and metrics, create a free Cribl.Cloud account or try a hands-on sandbox and route your first pipeline in minutes.
Combining Logging Metrics and Monitoring
What are the main differences between logs and metrics?
Logs are text-based event records describing what occurred, while metrics are numeric data that measure system state. Logs are high-volume and good for debugging; metrics are compact and suited for alerts and trends.
How do logs and metrics work together to improve observability?
Metrics detect anomalies, and logs explain their causes. Combining them lets teams move directly from an alert to the related log events, reducing resolution time.
What are the four golden signals and why are they important?
The four golden signals — latency, traffic, errors, and saturation — measure key aspects of user experience. They guide SLOs and alert setups that trigger further log and trace analysis.
How can I reduce costs when monitoring logs and metrics?
Use a telemetry pipeline to convert redundant log events into metrics, apply storage tiering to archive older data cheaply, and filter or sample verbose logs before indexing. These methods reduce costs without losing observability.
