Graylog vs Parseable: Log Management for the Cloud Era (2026)

Introduction

Graylog has earned its place in the log management landscape. Since 2013, it has provided organizations with a capable, open-source platform for collecting, indexing, and analyzing log data. Its pipeline processing engine, alerting system, and security-focused features have made it a popular choice for teams that need structured log management without paying for Splunk. However, a growing number of teams are now actively researching graylog alternatives as the platform's architectural constraints become more apparent.

Graylog was designed in a different era. Its architecture requires three separate infrastructure components: MongoDB for configuration metadata, Elasticsearch (or OpenSearch) for log indexing and search, and the Graylog server for ingestion and the web interface. In 2016, that was a reasonable design. In 2026, it is a liability.

Organizations no longer need just log management. They need unified observability across logs, metrics, events, and traces (MELT). They need storage costs that scale with object storage economics, not Elasticsearch cluster expansion. They need deployment simplicity that does not require a team of specialists to keep three interdependent systems healthy.

This is why a growing number of engineering teams are evaluating graylog alternatives and landing on Parseable, a unified observability platform that replaces Graylog's three-component architecture with a single binary backed by S3-compatible object storage.

Architecture: Three Components vs One Binary

Graylog's Architecture

A production Graylog deployment requires three separate systems running simultaneously:

1. MongoDB - Stores configuration, user accounts, stream definitions, and metadata. In production, this means a replica set (3 nodes) for high availability.

2. Elasticsearch or OpenSearch - The storage backend for all log data. This is the most resource-intensive component, requiring careful shard sizing, JVM heap tuning, index lifecycle management, and node scaling. A 50-100 GB/day deployment typically needs 3-5 Elasticsearch nodes, each with 32-64 GB RAM and fast SSD storage.

3. Graylog Server - Handles log ingestion (via GELF, Syslog, Beats), processing pipelines, alerting, and the web interface. High availability requires multiple nodes behind a load balancer.

The total footprint: 7-12 servers (3 MongoDB nodes, 3-5 ES nodes, 1-3 Graylog nodes). Each component has its own configuration, upgrade cycle, and failure modes.

Parseable's Architecture

Parseable deploys as a single binary. It handles ingestion, processing, querying, and the web interface. The only external dependency is an S3-compatible object store (AWS S3, MinIO, GCS, Azure Blob).

No MongoDB. No Elasticsearch. No JVM to tune or shards to rebalance. Parseable writes data directly to object storage in Apache Parquet columnar format. For teams that prefer zero infrastructure management, Parseable Cloud provides a fully managed service with a free tier.

Graylog Production Stack          Parseable Production Stack
========================          =========================
MongoDB (3 replica nodes)         Parseable (single binary)
Elasticsearch (3-5 nodes)         S3 bucket
Graylog Server (1-3 nodes)
Load Balancer
--------------------------        -------------------------
Total: 7-12 servers               Total: 1 server + S3

Quick Comparison Table

Storage: Elasticsearch Indices vs S3 Object Storage

Graylog delegates all log storage to Elasticsearch, which stores data in Lucene-based indices on local SSD storage. This provides fast full-text search, but costs compound quickly:

• SSD storage at $0.10-$0.30/GB/month depending on IOPS requirements
• Replica overhead that doubles raw storage for durability
• Index overhead of 20-50% on top of raw data size
• JVM heap of 16-31 GB per node consuming dedicated RAM

At 100 GB/day with 30-day retention, a Graylog/Elasticsearch deployment consumes 6-9 TB of SSD storage, costing $600-$2,700/month in storage alone.

Parseable writes data to S3 in Apache Parquet format with 80-90% compression ratios. The same 100 GB/day workload:

• Raw data: 3 TB/month
• After Parquet compression (85%): ~450 GB on S3
• S3 standard storage at $0.023/GB/month
• Monthly storage cost: ~$10

That is a 60-270x difference in storage cost. Over a year: $124 on Parseable vs $7,200-$32,400 on Graylog's Elasticsearch backend. The data on S3 is stored in open Parquet format readable by Apache Spark, DuckDB, Pandas, Trino, or any Parquet-compatible tool. No vendor lock-in.

Querying: Lucene Syntax vs Standard SQL

Graylog uses Lucene query syntax for searching and a separate pipeline rule language for processing:

source:api-gateway AND level:ERROR AND http_status:[500 TO 599]

Parseable uses standard SQL via Apache Arrow DataFusion:

SELECT source, level, COUNT(*) as event_count,
  AVG(duration_ms) as avg_duration
FROM api_gateway_logs
WHERE level = 'ERROR' AND http_status BETWEEN 500 AND 599
  AND timestamp > NOW() - INTERVAL '1 hour'
GROUP BY source, level
ORDER BY event_count DESC

Every engineer knows SQL. No proprietary syntax to learn, no knowledge that becomes useless if you switch platforms. Window functions, CTEs, JOINs across log streams, and arbitrary aggregations are all available out of the box.

Deployment and Operations

A production Graylog deployment involves provisioning a MongoDB replica set, an Elasticsearch cluster with tuned JVM heap and shard configuration, Graylog server nodes with input definitions, load balancing, index rotation policies, pipeline rules, and monitoring for all three component types. Teams report initial deployment takes 1-5 days, with ongoing maintenance requiring dedicated engineering time.

Parseable deployment:

# Single binary
curl https://sh.parseable.io | sh
 
# Docker with S3 backend (production)
docker run -p 8000:8000 \
  -e P_S3_URL=https://s3.amazonaws.com \
  -e P_S3_BUCKET=my-parseable-data \
  -e P_S3_ACCESS_KEY=your-access-key \
  -e P_S3_SECRET_KEY=your-secret-key \
  -e P_S3_REGION=us-east-1 \
  parseable/parseable:latest \
  parseable s3-store

No MongoDB to configure. No Elasticsearch JVM heap to tune. No shard strategy to plan. Running and accepting data within minutes. Upgrades: replace the binary and restart. For zero operational overhead, use Parseable Cloud — upgrades, scaling, and infrastructure are handled for you.

Monitoring Scope: Logs Only vs Full MELT Observability

Graylog is a log management platform. It does not handle metrics, traces, or events. If your observability needs extend beyond logs, you need Prometheus for metrics, Jaeger or Tempo for traces, and possibly Grafana for cross-signal dashboards. That means 3-4 separate tools with different deployments, storage backends, query languages, and operational overhead.

Parseable handles logs, metrics, events, and traces in a single platform. All signal types are stored in Parquet on S3, queried with SQL, and visualized in one console. Parseable's native OTLP endpoint means you can point existing OpenTelemetry Collectors at it and start ingesting all signal types immediately. Cross-signal correlation is a SQL JOIN, not a context switch between tools.

Why Parseable Leads Among Graylog Alternatives

80-90% Cost Reduction Through Object Storage Economics

Graylog's Elasticsearch dependency anchors storage costs to SSD pricing. Parseable's S3-native architecture uses object storage at $0.023/GB/month. At 100 GB/day with 30-day retention, total cost of ownership is approximately $13,500/year for self-hosted Parseable vs $40,000-$80,000/year for the full Graylog stack. Parseable Cloud offers managed pricing at $0.37/GB ingested ($29/month minimum) for teams that prefer zero-ops infrastructure. But the real savings include engineering time: every hour spent tuning Elasticsearch shards, managing MongoDB replica sets, or coordinating rolling upgrades costs $150-$250 in engineering time. Even 10 hours/month of Graylog operational overhead costs $18,000-$30,000/year. Parseable's single-binary architecture reduces that to near zero.

Operational Simplicity That Eliminates Entire Problem Categories

Running Graylog means operating three distributed systems, each with distinct failure modes: MongoDB replica set elections, Elasticsearch shard allocation and GC pauses, Graylog journal backpressure. Diagnosing which component causes a slow query requires expertise across all three stacks. Parseable has one process to monitor, one configuration to tune, one set of logs to review.

SQL Queries That Every Engineer Can Write

Graylog's Lucene syntax works for simple searches but is limited for complex analytics. Parseable's SQL interface provides window functions, CTEs, subqueries, and JOINs. At 2 AM during an incident, the on-call engineer should write SQL and get answers, not debug Lucene syntax.

From Logs to Full Observability Without Adding Tools

Choosing Graylog means also deploying and maintaining separate tools for metrics, traces, and events. Parseable provides unified MELT observability from day one. As your needs mature, Parseable grows with you without new tools, deployments, or query languages. Reduced context switching during incidents translates directly to lower MTTR.

AI-Native Analysis

Parseable includes AI-native analysis for surfacing patterns, anomalies, and correlations. For teams running AI and LLM workloads, Parseable provides purpose-built observability for inference pipelines, token usage tracking, and model performance analysis. Graylog does not offer these capabilities.

Open Data Format

Parseable stores all data in Apache Parquet on S3, readable by virtually every data tool. Graylog locks data in Elasticsearch's proprietary Lucene segment format, accessible only through Elasticsearch APIs.

Being Fair to Graylog: Where It Excels

Mature Pipeline Processing: Graylog's pipeline rule engine is one of the best in log management. The ability to parse, enrich, route, and transform log messages using a readable rule language is powerful and well-documented.

Security and SIEM Features: Graylog Security provides threat detection, investigation workflows, and compliance reporting tailored for security operations. For organizations whose primary use case is security log analysis, Graylog's security features are more developed than most general-purpose observability platforms.

Polished Web Interface: Graylog's UI is clean, responsive, and refined over a decade of development. Search, dashboards, and alert configuration are intuitive.

Established Community: Since 2013, Graylog has built a dedicated community with extensive documentation and third-party guides.

Elasticsearch Query Power: For pure full-text search across unstructured log data, Elasticsearch's inverted index provides genuinely fast results.

These are real advantages, but they exist within the constraints of a three-component architecture and logs-only scope.

Migration Guide: From Graylog to Parseable

Step 1: Deploy Parseable Alongside Graylog

The fastest path is Parseable Cloud — sign up for a free tier account and skip infrastructure provisioning. If you prefer self-hosting, deploy in parallel with minimal resources (<50 MB memory footprint):

docker run -p 8000:8000 \
  -e P_S3_URL=https://s3.amazonaws.com \
  -e P_S3_BUCKET=parseable-migration \
  -e P_S3_ACCESS_KEY=your-key \
  -e P_S3_SECRET_KEY=your-secret \
  -e P_S3_REGION=us-east-1 \
  parseable/parseable:latest \
  parseable s3-store

Step 2: Dual-Ship Logs via OpenTelemetry Collector

exporters:
  otlphttp/parseable:
    endpoint: "http://parseable-host:8000/v1"
  otlphttp/graylog:
    endpoint: "http://graylog-host:12201"
 
service:
  pipelines:
    logs:
      receivers: [filelog, otlp]
      exporters: [otlphttp/parseable, otlphttp/graylog]

Step 3: Translate Queries to SQL

-- Graylog: source:api-gateway AND level:ERROR
SELECT * FROM logs
WHERE source = 'api-gateway' AND level = 'ERROR'
ORDER BY timestamp DESC;
 
-- Graylog: statistical dashboard widget
SELECT date_trunc('minute', timestamp) as minute,
  COUNT(*) as total,
  COUNT(*) FILTER (WHERE level = 'ERROR') as errors
FROM logs WHERE timestamp > NOW() - INTERVAL '1 hour'
GROUP BY minute ORDER BY minute;

Step 4: Add Metrics and Traces

Expand beyond Graylog's capabilities by adding all signal types:

service:
  pipelines:
    logs:
      exporters: [otlphttp/parseable]
    traces:
      exporters: [otlphttp/parseable]
    metrics:
      exporters: [otlphttp/parseable]

Step 5: Decommission Graylog

Once validated, stop shipping to Graylog. Keep it read-only through your retention period, then shut down all three components and reclaim 7-12 servers.

Component Count: The Hidden Cost Multiplier

Scaling

Scaling Graylog means independently scaling each component: adding Elasticsearch data nodes (with hours-long shard rebalancing), adding Graylog server nodes, and occasionally scaling MongoDB. Each action requires planning, provisioning, and validation.

Scaling Parseable: S3 scales automatically. For more throughput, add Parseable instances behind a load balancer. Each instance queries the same S3 data independently. No shard rebalancing. No JVM heap resizing.

Frequently Asked Questions

Is Graylog still a good choice for log management in 2026?

Graylog remains capable for log management, particularly if you need its pipeline processing engine and SIEM features. However, its three-component architecture creates operational complexity that is hard to justify when single-binary alternatives exist. If you need unified observability, simpler operations, or lower costs, Parseable is the stronger choice.

Can Parseable replace Graylog for security log analysis?

Parseable provides powerful log analysis with SQL queries supporting audit log analysis, access pattern investigation, and AI-native anomaly detection. For basic to intermediate security analysis, Parseable is fully capable. For organizations requiring purpose-built SIEM features like pre-built threat detection rules and compliance frameworks, Graylog Security or a dedicated SIEM may be more appropriate.

How does Parseable handle full-text search without Elasticsearch?

Parseable uses Apache Arrow DataFusion for SQL-based querying over Parquet files with predicate pushdown for sub-second performance. For 95% of real-world log queries involving structured or semi-structured fields, Parseable's SQL approach is equally fast and far more flexible. For searching arbitrary substrings across billions of unstructured entries, Elasticsearch's inverted index has a narrow edge.

What is the cost difference between Graylog and Parseable at scale?

At 100 GB/day with 30-day retention, Graylog's full infrastructure costs range from $40,000-$80,000/year. Parseable costs approximately $13,500/year. That is an 80-90% reduction. The gap widens at higher volumes because S3 scales at $0.023/GB/month while Elasticsearch requires proportionally more expensive compute nodes.

Does Parseable support Graylog's ingestion formats?

Parseable ingests via its HTTP API and native OTLP endpoint. For existing Graylog sources (GELF, Syslog, Beats), use an OpenTelemetry Collector as a universal receiver to forward to Parseable. This also enables adding metrics and trace collection through the same infrastructure.

Is Parseable only for logs?

No. Parseable is a unified MELT observability platform handling logs, metrics, events, and traces. This is its most significant advantage over Graylog, which handles logs only. All signal types are stored on S3 in Parquet format and queried with standard SQL via the native OTLP endpoint.

Evaluating Graylog Alternatives? Start Here

If you are evaluating graylog alternatives or planning a migration, Parseable offers the fastest path to simpler, cheaper, and more capable observability.

• Start with Parseable Cloud — starts at $0.37/GB, free tier available
• Self-hosted deployment — single binary, deploy in 2 minutes
• Read the docs — guides, API reference, and tutorials
• Join our Slack — community and engineering support

Deploy in under five minutes, dual-ship alongside Graylog, and let the data speak for itself.

Looking for more comparisons? Read our guides on Splunk alternatives for log management, open source log management tools, and Elasticsearch vs Parseable for detailed comparisons across the observability landscape.