Best Log Management Tools
Find the needle in the haystack when things break at 3 AM
By Toolradar Editorial Team · Updated
Datadog Logs offers the best UX as part of a full observability platform. Elastic (ELK) is powerful and can be self-hosted for cost control. Papertrail is simple and affordable for smaller operations. Splunk remains the enterprise leader but at enterprise prices. Consider your scale and whether you need standalone logs or integrated observability.
Log management becomes critical when things go wrong—and they will. Without centralized logging, debugging means SSH-ing into servers and grepping files. At scale, that's impossible. Good log management aggregates logs from all sources, makes them searchable, enables alerting, and helps you understand what happened when systems misbehave. Here's how to choose.
What is Log Management?
Log management platforms collect, store, index, and search log data from applications and infrastructure. They ingest logs from various sources (applications, servers, containers, cloud services), parse and structure them, enable fast search across massive volumes, and provide visualization and alerting. Modern platforms integrate with broader observability (metrics, traces).
Why Log Management Matters
Logs are your application's story. When something breaks, logs tell you what happened. Without centralized management: logs are scattered across servers, old logs are lost, searching is slow, and correlating events is manual. Good log management enables fast debugging, proactive alerting, and understanding of system behavior at scale.
Key Features to Look For
Collect logs from multiple sources into one place
Query across large volumes of logs quickly
Structure unstructured logs for useful querying
Get notified on specific log patterns or anomalies
Store logs for compliance and historical analysis
Real-time log streaming for active debugging
Visualize log-based metrics and patterns
Link logs to traces and metrics for full context
Key Factors to Consider
Evaluation Checklist
Pricing Overview
Papertrail free (50MB/mo), Elastic self-hosted (free), New Relic free (100GB/mo)
Papertrail Maple $18/mo (2GB), Elastic Cloud Standard $95/mo, Datadog ~$200-500/mo
Splunk from $100/GB/day, Datadog at scale, Elastic Platinum $125+/mo
Top Picks
Based on features, user feedback, and value for money.
Teams wanting integrated logs, metrics, and traces in a single platform
Teams wanting flexibility and cost control through self-hosting or managed cloud
Small teams wanting dead-simple log aggregation without enterprise overhead
Mistakes to Avoid
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Logging everything at DEBUG level in production — A single chatty microservice can generate 10-50GB/day of debug logs. At Datadog's $0.10/GB, that's $100-500/month from one service. Set production to INFO/WARN and use sampling for high-frequency events
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Unstructured log messages —
Error processing orderis useless.{"level":"error","service":"checkout","order_id":"12345","error":"payment_declined"}is searchable. Structured JSON logging costs nothing extra and makes every query faster - ×
No log rotation on self-hosted — Elasticsearch nodes running out of disk at 3am crash your entire observability stack. Set index lifecycle policies: hot (7 days), warm (30 days), delete (90 days). Monitor disk usage with alerts at 80%
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No alerting on error rate spikes — Logs without alerts are archaeology, not monitoring. Alert on error rate exceeding baseline (e.g., >5% of requests returning 500s in 5 minutes), not individual errors
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Same retention for all log levels — Keep ERROR/WARN for 30-90 days, INFO for 7-14 days, and DEBUG for 1-3 days. This cuts storage costs by 60-80% without losing important debugging capability
Expert Tips
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Use structured logging (JSON) from day one — Retrofitting unstructured logs into structured format across a codebase takes weeks. Libraries like Winston (Node), Logback (Java), and structlog (Python) make JSON logging trivial
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Implement log sampling for high-traffic endpoints — Log 10% of successful requests but 100% of errors. This cuts volume by 80-90% with minimal loss of debugging value. Most APM tools support head-based sampling
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Set up saved searches for common incidents — Pre-build queries like 'all 500 errors in the last hour by service' and 'database query timeouts.' During incidents, you need answers in seconds, not time to compose queries
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Use Datadog's Flex Logs or Elastic's cold tier for compliance — Hot storage costs $1.70/million events on Datadog but Flex Logs costs $0.05/million. Route compliance-required logs to cold storage automatically
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Correlate logs with request IDs — Include a unique trace/request ID in every log line. When a user reports an issue, search by their request ID to see the full journey across all services. This turns 30-minute debugging into 2-minute lookups
Red Flags to Watch For
- !Per-GB pricing with no ingestion controls—without sampling or filtering at ingestion, costs double when a chatty service starts debug-logging in production
- !Retention-based pricing without archive tier—paying hot-storage rates ($1.70/million events) for logs you only need for compliance but never query wastes budget
- !No correlation between logs and traces/metrics—if your monitoring stack can't link a log error to the specific request trace, debugging stays painful
- !Search takes 10+ seconds for common queries—slow search means engineers avoid the tool and SSH into servers directly, defeating the purpose
The Bottom Line
Datadog Logs ($0.10/GB ingestion + retention fees) provides the best experience as part of a complete observability platform—worth the premium if you also use their APM and infrastructure monitoring. Elastic (free self-hosted, Cloud from $95/month) offers unmatched power and flexibility for teams with DevOps capacity. Papertrail (free to $7/month) is perfect for small teams wanting simplicity. Control costs by logging thoughtfully—structured logging, sampling, and tiered retention save 50-80% without losing debugging capability.
Frequently Asked Questions
How much log data is typical?
Varies wildly. Small apps: 1-10 GB/day. Medium SaaS: 50-200 GB/day. Large platforms: TB/day. Start by measuring your actual output before pricing tools. Most applications over-log initially.
Should I self-host Elastic or use managed?
Managed unless you have strong DevOps expertise and volume that justifies the operational overhead. Self-hosted ELK is powerful but running Elasticsearch clusters at scale is non-trivial.
How long should I retain logs?
Depends on use case. Debugging: 7-30 days hot storage is usually enough. Compliance: often 1-7 years (archive to cold storage). Security: 90 days to 1 year is common. Most queries hit recent data.
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