Modern IT and security teams face a flood of telemetry data. Logs, metrics, and traces each describe part of system behavior, but using them separately creates blind spots, slows investigations, and increases costs. Combining logging, metrics, traces, and context, such as network and service topology, into one monitoring and observability strategy helps teams detect issues faster, find root causes efficiently, and control costs without losing data quality. This guide explains the core concepts, architecture choices, and step-by-step methods for building a combined observability system. Whether you use a unified platform or connect multiple tools through telemetry pipelines, the goal is the same: make data useful for decisions. Cribl’s vendor-neutral pipeline and telemetry platform lets organizations route, transform, and optimize, store, and analyze observability data across any destination, giving them control and flexibility.
Understanding logging, metrics, and monitoring
Before integration, teams need a clear understanding of the three main observability signals.
Logging is the process of collecting and storing timestamped event records that describe what happened inside an application or system component. Logs are usually text or structured JSON and answer “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 are used for alerting and trend analysis. Prometheus, for example, stores numeric time-series metrics and uses Alertmanager for notifications.
Traces (often called distributed traces) represent the entire journey of a request or transaction as it travels through a distributed software system. If you think of an application as a complex web of microservices, a trace is the continuous thread that connects every single hop, database query, and API call made to fulfill a specific user action (like clicking "Buy Now" on an e-commerce site).
Observability is the discipline of observing system state by collecting logs, metrics, and traces, then applying dashboards, alerts, and analysis to maintain reliability. While monitoring and observability are often used interchangeably, they represent two fundamentally different approaches to managing system health.
The easiest way to distinguish them is: Monitoring tells you when a system is broken; observability helps you understand why it broke, especially when it breaks in a way you’ve never seen before.
These signals are most effective together. During incidents, teams often move between logs, metrics, and traces. Unified tooling reduces context switching and speeds resolution.
Defining the purpose of combining logs and metrics
Correlating logs and metrics provides measurable operational and cost benefits
Combining signals shortens detection time and accelerates root cause analysis. Teams can pivot directly from a metric alert to the related log lines or traces without switching tools. When the error rate spikes, you can quickly see the log events that caused it instead of searching manually.
Costs also weigh heavily. High-volume logs get expensive quickly. Splunk, for instance, charges by data ingest volume, and costs rise as logs grow. By converting repetitive logs into metrics, organizations reduce indexing expenses while keeping visibility. Telemetry pipelines enable event-to-metric conversions and can address privacy requirements by routing or redacting data before it reaches storage. Cribl’s vendor-neutral pipeline and telemetry platform lets organizations route, transform, optimize, store, and analyze observability data across any destination, giving them control and flexibility. Cribl Stream performs these conversions in-stream to cut indexing costs.
Expected results from a combined approach include:
- Correlated alerts across data types
- Less tool switching during incidents
- Lower observability costs through routing and data reduction
- Improved compliance through in-pipeline redaction and routing
Cribl’s log-to-metric conversion supports these goals by turning large log volumes into concise metrics before indexing.
Choosing the right architecture for integration
Architecture is the most important design choice when combining log, metric, and monitoring data. The dominant models are a unified observability platform or a modular “best-of-breed” stack joined by telemetry pipelines.
Teams should evaluate based on real log volume, query patterns, retention, and goals rather than feature lists.
Unified observability platforms
A unified observability platform stores and correlates logs, metrics, and traces within one data model and interface.
Its benefits include faster setup, native cross-signal links, and simpler operations. The trade-off is vendor lock-in, less flexibility in optimizing per signal, and higher scaling costs. Many teams use Cribl pipelines in front of unified platforms to manage ingestion and cost.
Best-of-breed stacks with telemetry pipelines
Telemetry pipelines sit between data sources and destinations to route, filter, enrich, and change observability data in real time before storage. Cribl Stream is the market leader for telemetry pipelines.
Common combinations pair Prometheus and Grafana for metrics, ELK or Loki for logs, and Jaeger or Tempo for traces. Prometheus specializes in metrics, while the Elastic Stack handles log search and indexing.
Key pipeline functions include:
- Routing logs
- Redacting sensitive fields before transfer
- Converting events to metrics to reduce indexing costs
- Replaying or duplicating data streams for migration or tests
Cribl Stream integrates with OpenTelemetry collectors to extend those functions. See Logs, Events, Metrics, and Traces for details.
Implementing correlated logging and metrics: step-by-step
This six-step checklist reflects how experienced SRE and platform teams deploy combined observability.
Define SLOs and identify relevant signals
Start by defining Service Level Objectives such as 99.9% availability or p95 latency under 200ms. Identify which metrics and log events track those goals.
Use the four golden signals — latency, traffic, errors, and saturation — as the core metrics for user-facing health. Link each SLO to specific metrics and logs needed for root-cause analysis. This ensures every alert leads to a useful follow-up investigation.
Conduct ingestion and query performance tests
Validate data volumes before finalizing architecture. Test one to two weeks of production logs to estimate cost. Check query speed under realistic retention windows because performance matters most during incidents.
Document daily ingest rate, query latency at 7 and 30 days, and projected annual storage cost to guide architecture.
Deploy OpenTelemetry agents and data pipelines
OpenTelemetry collects logs, metrics, and traces from systems in a vendor-neutral format. Deploying OTel collectors with Cribl Stream creates a flexible setup where OTel handles collection and Cribl manages routing and transformation.
Cribl Edge is a lightweight collector that forwards data to Stream for centralized processing. A typical flow: Application → OTel SDK → OTel Collector → Cribl Stream → Prometheus (metrics), Elasticsearch (logs), and S3 (archives).
View more Data Collection use cases.
Convert events to metrics and apply in-stream processing
This step yields the largest cost and performance benefits. Event-to-metric conversion extracts numeric measures from log events and emits time-series metrics, cutting storage needs without losing visibility.
Examples include turning HTTP access logs into `request_count` and `latency_p99` metrics or error logs into `error_rate_by_service`. Additional steps include parsing unstructured logs into structured JSON, sampling verbose logs, and redacting PII. A before-and-after output comparison helps measure data reduction.
See Cribl’s guide on extracting metrics from logs
Establish alerting linked to logs and traces
Include contextual links in alerts so investigators can jump 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]). Grafana’s integration with Loki and Tempo supports these workflows.
Avoid static thresholds alone; align alerts with SLO budgets and use anomaly detection where needed.
Optimize data retention and storage tiering
Storage tiering directs data to different backends based on query frequency and retention needs. Hot tiers are fast and costly; cold tiers are slower and cheaper. Cribl does data tiering by routing telemetry based on how it’s actually used, sending frequently accessed data to fast analytics tools, less-used data to lakehouse or lower-cost storage, and archival data to long-term retention tiers.
Upstream, Cribl Stream can filter, shape, and enrich data before it lands, so each destination gets the right fidelity at the right cost instead of dumping everything into one expensive system.
Then Cribl Search lets teams search across tiers in place, so they can investigate or promote data when needed without rehydration delays or vendor lock-in.
Cribl Lake stores telemetry in open, non-proprietary formats, giving teams a cost-effective way to retain full-fidelity logs, metrics, and traces for long-term access without vendor lock-in.
Cribl Stream can then collect, process, enrich, route, and replay that telemetry, sending the right data to the right downstream tools in the required format for investigation, troubleshooting, compliance, or broader analysis.
Together, they let organizations store more telemetry affordably in Lake and use Stream to activate that data whenever it becomes valuable.
Cribl Search completes the picture by giving teams a unified search and analytics experience across telemetry in Cribl Lake and other data sources, with fast queries, customizable dashboards, charts, tables, and scheduled searches for ongoing monitoring and investigation.
Search dashboards support interactive panels, inputs, and drilldowns, so users can move from high-level trends to detailed analysis without stitching together multiple tools.
Adjust retention based on actual queries. Most teams find that over 80% of queries target the most recent 7 days, making tiering cost-effective.
Operational best practices for combined observability
Once implemented, these practices keep systems reliable and efficient:1. Use structured logging. JSON format with consistent fields like timestamp, service, level, and correlation ID enables parsing and correlation.
2. Monitor the pipeline. Track throughput, backpressure, and errors to prevent silent data loss. Cribl Stream’s monitoring supports this.
3. Keep metric labels stable. Avoid high-cardinality data such as user IDs as labels.
4. Test queries under load. Regularly check dashboard and log query latency under simulated incident conditions.
5. Link alerts to SLO error budgets. Replace arbitrary thresholds with burn-rate alerts.
6. Review and trim data. Audit signals quarterly to remove unused or low-value data.
Comparing Cribl with other solutions for data observability
Cribl works as the telemetry pipeline between sources and destinations. It complements Prometheus, Elasticsearch, and similar tools by controlling what data is sent, its format, and volume.
Cribl’s key advantages:
- Vendor neutrality. Route data to multiple destinations such as Splunk, S3, and Elasticsearch at once.
- Log-to-metric conversion. Transform log events into metrics to reduce indexing cost.
- Data reduction. Filter, sample, or cut unnecessary fields before storage.
- Compliance and privacy. Mask sensitive content before writing to storage.
- Cribl Edge plus Stream architecture. Collect at the source and process centrally.
This neutral control layer avoids vendor lock-in. It also goes beyond basic OTel collectors with more transformation and routing options. For integration examples, see Cribl’s guide on sending logs and metrics to New Relic.
Key tradeoffs and considerations in integration
Teams face several tradeoffs while implementing combined observability:
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 but at high cost. Filtering reduces expense but risks losing signals. Converting logs to metrics and tiering storage balances both.
Real-time alerting vs. batch analysis. Metrics provide fast detection; logs allow deeper analysis. Combining them provides both.
Open-source vs. commercial. Open-source tools like Prometheus and ELK save licensing costs but need engineering effort .
Centralization vs. edge processing. Edge processing with Cribl Edge reduces transfer overhead; central processing with Cribl Stream simplifies management. Most setups use both.
Reassess these tradeoffs regularly as data volumes and requirements change.
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.
