Logstash vs Parseable: Why Teams Are Switching in 2026

Updated: February 2026 | Reading time: 15 min

Introduction

Logstash has been the workhorse of log pipelines for over a decade. As the "L" in the ELK stack, it became the default way to ingest, transform, and route log data into Elasticsearch. But in 2026, engineering teams are actively searching for logstash alternatives, questioning whether they need Logstash at all, and whether the entire ELK architecture it sits inside is the right approach for modern observability.

The core problem is architectural. Logstash is a data pipeline processor. It does not store data. It does not query data. It does not visualize data. It is one component in a multi-component stack that requires Elasticsearch for storage and indexing, Kibana for visualization, and often Beats or Filebeat for collection. Each component must be independently deployed, configured, scaled, monitored, and upgraded. At scale, managing an ELK stack becomes a full-time job for multiple engineers.

Parseable takes a fundamentally different approach. It is a unified observability platform, covering logs, metrics, events, and traces, delivered as a single binary that writes directly to S3-compatible object storage. There is no separate pipeline processor, no Elasticsearch cluster, no Kibana instance, and no JVM heap to tune. One binary replaces the entire stack.

This guide provides a detailed, head-to-head comparison of Logstash (and the ELK stack it depends on) against Parseable, covering architecture, cost, performance, deployment complexity, and migration paths. If you are running Logstash today and evaluating logstash alternatives, this is the comparison you need.

TL;DR: Logstash is a pipeline processor that requires the entire ELK stack to function. Parseable is a complete unified observability platform (logs, metrics, events, traces) in a single binary. Teams switching from ELK to Parseable typically see 80-90% cost reduction, eliminate 4-6 infrastructure components, and go from hours of pipeline debugging to direct HTTP ingest with SQL querying. Start at app.parseable.com or deploy the single binary in 5 minutes.

Architecture: Pipeline Processor vs Complete Platform

This is the fundamental difference that drives every other comparison point. Logstash and Parseable are not the same category of tool. Understanding this architectural gap is essential before evaluating features, cost, or performance.

Logstash: A Pipeline Processor in a Multi-Component Stack

Logstash is a server-side data processing pipeline. It ingests data from multiple sources, transforms it, and sends it to a destination, most commonly Elasticsearch. Here is what a production ELK deployment actually requires:

Data Sources → Filebeat/Beats → Logstash → Elasticsearch → Kibana
                  (collection)    (processing)   (storage/indexing)  (visualization)

Each box in that diagram is a separate application with its own:

• Deployment and configuration - Filebeat agents on every host, Logstash pipeline configs, Elasticsearch cluster settings, Kibana server setup
• Resource requirements - Logstash needs 1-4 GB of JVM heap per instance, Elasticsearch nodes need 16-64 GB RAM with fast SSDs, Kibana needs its own server
• Scaling strategy - Logstash scales horizontally with a load balancer, Elasticsearch requires shard rebalancing and node management, Beats need deployment orchestration across all hosts
• Monitoring - Each component needs its own health checks, metrics collection, and alerting
• Upgrade path - Version compatibility between Beats, Logstash, Elasticsearch, and Kibana must be carefully managed

A typical production ELK stack at 100 GB/day ingestion involves:

That is 60 to 215+ individual processes to deploy, configure, and maintain, just to collect, process, store, and query logs. And this covers only logs. There is no metrics, no traces, no events. For full observability, you need additional tools on top.

Parseable: A Complete Unified Observability Platform

Parseable's architecture is radically different:

Data Sources → Parseable (single binary) → S3/Object Storage
                (ingest + process + store + query + visualize)

One binary. One configuration. That binary handles:

• Ingestion - Direct HTTP API, native OTLP endpoint, syslog, and collector integrations
• Processing - Schema detection, field extraction, and enrichment at ingest time
• Storage - Writes compressed Apache Parquet files to S3-compatible object storage
• Querying - SQL query engine powered by Apache Arrow DataFusion
• Visualization - Built-in web console with dashboards, alerts, and live tail
• Full MELT observability - Logs, Metrics, Events, and Traces in one platform

A production Parseable deployment at 100 GB/day ingestion:

That is a reduction from 60-215+ components to 2-4. This is not an incremental improvement. It is an architectural shift that eliminates entire categories of operational work.

Quick Comparison Table

Detailed Feature Comparison

Data Ingestion

Logstash ingests data through input plugins. There are over 200 input plugins covering files, syslog, TCP/UDP, Kafka, Redis, HTTP, JDBC, S3, and dozens of other sources. This breadth is Logstash's greatest strength. If your data source exists, there is probably a Logstash input plugin for it.

However, this plugin architecture comes with configuration complexity. A typical Logstash pipeline configuration looks like this:

# logstash.conf
input {
  beats {
    port => 5044
  }
  tcp {
    port => 5000
    codec => json
  }
}
 
filter {
  grok {
    match => { "message" => "%{COMBINEDAPACHELOG}" }
  }
  date {
    match => [ "timestamp", "dd/MMM/yyyy:HH:mm:ss Z" ]
  }
  mutate {
    remove_field => ["@version", "host"]
  }
}
 
output {
  elasticsearch {
    hosts => ["http://es-node1:9200", "http://es-node2:9200"]
    index => "logs-%{+YYYY.MM.dd}"
    user => "elastic"
    password => "changeme"
  }
}

Every pipeline needs to be written, tested, and maintained. Grok patterns are notoriously difficult to debug. When a pipeline fails or slows down, diagnosing whether the bottleneck is in the input, filter, or output stage requires deep Logstash expertise.

Parseable takes a direct ingestion approach. Applications and collectors send data via HTTP POST or through the native OTLP endpoint. No pipeline configuration is needed for basic ingestion:

# Send logs directly via HTTP
curl -X POST https://your-parseable:8000/api/v1/logstream/app-logs \
  -H "Content-Type: application/json" \
  -H "Authorization: Basic <credentials>" \
  -d '[
    {
      "timestamp": "2026-02-18T10:30:00Z",
      "level": "error",
      "service": "payment-api",
      "message": "Transaction timeout after 30s",
      "trace_id": "abc123",
      "duration_ms": 30042
    }
  ]'

For teams using OpenTelemetry, Parseable's native OTLP endpoint accepts logs, metrics, and traces without any custom pipeline configuration:

# otel-collector-config.yaml
exporters:
  otlphttp/parseable:
    endpoint: "http://parseable:8000/v1"
    headers:
      Authorization: "Basic <credentials>"
 
service:
  pipelines:
    logs:
      receivers: [otlp, filelog]
      exporters: [otlphttp/parseable]
    traces:
      receivers: [otlp]
      exporters: [otlphttp/parseable]
    metrics:
      receivers: [otlp, prometheus]
      exporters: [otlphttp/parseable]

No Grok patterns. No filter chains. No output plugin configuration. Data flows in through standard protocols and is immediately queryable.

Data Processing and Transformation

Logstash is built around the concept of filter plugins that transform events as they flow through the pipeline. The most common filters include:

• grok - Pattern matching for unstructured log lines
• mutate - Field renaming, type conversion, string manipulation
• date - Timestamp parsing and normalization
• geoip - IP address geolocation enrichment
• ruby - Custom transformation logic in Ruby code
• dissect - Simpler alternative to grok for structured delimiters

These filters are powerful, but they introduce processing overhead and latency. Each filter consumes CPU cycles, and complex pipelines with multiple grok patterns can become significant bottlenecks. Debugging a pipeline that processes events through 10-15 filters is time-consuming, and a single misconfigured grok pattern can cause events to be dropped or malformed.

Parseable handles processing differently. Rather than transforming data in a pipeline before storage, Parseable uses a schema-on-read approach combined with automatic field detection at ingest time. JSON data is automatically parsed into columnar fields. Structured and semi-structured data is indexed for fast filtering. Complex transformations can be performed at query time using SQL:

-- Extract fields and transform at query time
SELECT
  json_extract(message, '$.request.path') as endpoint,
  json_extract(message, '$.response.status') as status_code,
  CASE
    WHEN json_extract(message, '$.response.status') >= 500 THEN 'server_error'
    WHEN json_extract(message, '$.response.status') >= 400 THEN 'client_error'
    ELSE 'success'
  END as status_category,
  count(*) as request_count
FROM app_logs
WHERE p_timestamp > NOW() - INTERVAL '1 hour'
GROUP BY endpoint, status_code, status_category
ORDER BY request_count DESC

This approach has a significant advantage: you do not need to know what transformations you want before ingesting the data. With Logstash, if you forgot to add a grok pattern or mutate filter, that data is stored without the transformation and you cannot go back. With Parseable, the raw data is preserved and you apply transformations at query time as needed.

Storage and Cost

This is where the architectural difference creates the most dramatic impact on total cost of ownership.

Logstash itself does not store data. It passes data to Elasticsearch, which stores it in Lucene-based inverted indexes on local SSDs. Elasticsearch's storage requirements are substantial:

• Each Elasticsearch data node needs fast SSD storage (not HDD, performance degrades severely)
• Replica shards double storage requirements for high availability
• Index lifecycle management rotates data through hot, warm, and cold tiers, each requiring provisioned infrastructure
• At 100 GB/day with 30-day retention, expect 6-10 TB of provisioned SSD storage across the cluster

The infrastructure cost for running Elasticsearch at 100 GB/day with 30-day retention:

And this is self-managed. Elastic Cloud (managed service) costs significantly more, typically $80,000-$150,000/year at this volume.

Parseable writes data to S3-compatible object storage in compressed Apache Parquet format. The economics are fundamentally different:

That is a 86% cost reduction over self-managed ELK and a 90%+ reduction over Elastic Cloud. The difference comes from two factors: S3 storage at $0.023/GB/month is dramatically cheaper than provisioned SSDs, and Parquet columnar compression reduces data volume by 80-90%.

Parseable Cloud Pricing: For teams that prefer managed infrastructure, Parseable Cloud starts at $0.37/GB ingested with 30-day retention, minimum $29/month. No infrastructure to manage, no S3 bills to track, no compute to provision.

At higher volumes, the gap widens further:

Query Capabilities

Logstash does not provide querying. Queries go through Kibana, which translates them into Elasticsearch DSL. The primary query interfaces are:

• KQL (Kibana Query Language) - Simplified search syntax for Kibana's Discover view
• Lucene query syntax - Full-text search with Boolean operators
• Elasticsearch DSL - JSON-based query API for programmatic access

Elasticsearch excels at full-text search. Its inverted index makes it extremely fast for keyword lookups and pattern matching. However, analytical queries involving aggregations, time-series calculations, and cross-field correlations require the Elasticsearch aggregations API, which uses a verbose JSON syntax that is difficult to write and maintain.

Parseable uses standard SQL powered by Apache Arrow DataFusion. This means sub-second query performance on Parquet data stored in S3, with the full expressiveness of SQL:

-- Error rate by service over the last 24 hours, in 1-hour buckets
SELECT
  service_name,
  date_trunc('hour', p_timestamp) as hour,
  count(*) as total_requests,
  count(*) FILTER (WHERE level = 'error') as error_count,
  round(count(*) FILTER (WHERE level = 'error')::float / count(*)::float * 100, 2) as error_rate
FROM application_logs
WHERE p_timestamp > NOW() - INTERVAL '24 hours'
GROUP BY service_name, hour
ORDER BY hour DESC, error_rate DESC

-- Join logs with traces to find slow requests with errors
SELECT
  t.trace_id,
  t.service_name,
  t.duration_ms,
  l.message as error_message,
  l.p_timestamp as error_time
FROM traces t
JOIN error_logs l ON t.trace_id = l.trace_id
WHERE t.duration_ms > 5000
  AND l.level = 'error'
  AND t.p_timestamp > NOW() - INTERVAL '1 hour'
ORDER BY t.duration_ms DESC
LIMIT 50

SQL is universally understood. There is no proprietary query language to learn, no Lucene syntax to memorize, and no JSON DSL to construct. Your entire engineering team can query observability data on day one.

Observability Scope

Logstash and the ELK stack handle logs. That is their primary purpose. Elastic has added APM (Application Performance Monitoring) and Elastic Agent for metrics collection, but these are bolt-on additions rather than native capabilities. Running full observability with Elastic requires deploying and managing additional components:

• Elastic APM Server for traces
• Metricbeat for infrastructure metrics
• Elastic Agent for unified collection (replacing individual Beats)
• Fleet Server for managing Elastic Agents at scale

Each addition increases operational complexity and infrastructure cost.

Parseable is a unified observability platform from the ground up. It handles all four MELT signal types natively:

• Metrics - System metrics, application metrics, custom metrics via OTLP
• Events - Application events, audit events, security events
• Logs - Structured and unstructured log data from any source
• Traces - Distributed traces via OpenTelemetry OTLP

All signal types share the same storage backend, the same query interface, and the same visualization layer. Cross-signal correlation is a SQL JOIN, not a separate integration. This unified approach eliminates tool sprawl and gives engineers a single place to investigate incidents across all telemetry types.

AI-Native Analysis

Logstash and the ELK stack do not include AI-powered analysis capabilities. Elastic has introduced Elastic AI Assistant in recent versions, but it is focused on security workflows within Elastic SIEM rather than general observability analysis.

Parseable includes AI-native analysis capabilities that help engineers investigate incidents faster. Natural language queries, anomaly detection, pattern recognition, and intelligent alerting reduce mean time to resolution (MTTR) without requiring engineers to manually construct complex queries for every investigation. For teams monitoring AI and LLM workloads, Parseable provides purpose-built capabilities for tracking token usage, inference latency, and model performance.

ELK Stack Component Count vs Parseable

One of the most compelling reasons teams search for logstash alternatives is the operational burden of managing the full ELK stack. Here is a side-by-side comparison of what each deployment looks like in production.

ELK Stack Production Deployment (100 GB/day)

┌──────────────────────────────────────────────────────────┐
│                    ELK Stack (6+ components)              │
│                                                          │
│  ┌─────────┐   ┌──────────┐   ┌──────────────────────┐  │
│  │Filebeat │──▶│ Logstash  │──▶│   Elasticsearch      │  │
│  │(50-200  │   │ (2-4     │   │   (3 master +        │  │
│  │ agents) │   │ instances)│   │    3-6 data nodes)   │  │
│  └─────────┘   └──────────┘   └──────────┬───────────┘  │
│                                           │              │
│                                    ┌──────▼──────┐       │
│                                    │   Kibana     │       │
│                                    │ (1-2 nodes)  │       │
│                                    └─────────────┘       │
│                                                          │
│  Additional for full observability:                       │
│  ┌───────────┐  ┌────────────┐  ┌──────────────┐        │
│  │ APM Server │  │ Metricbeat │  │ Fleet Server │        │
│  └───────────┘  └────────────┘  └──────────────┘        │
│                                                          │
│  Total processes: 60-215+                                │
│  Total RAM: 200-500 GB across cluster                    │
│  JVM heaps to tune: 3-10                                 │
│  Config files to maintain: 50-200+                       │
│  Upgrade coordination: 4-6 component versions            │
└──────────────────────────────────────────────────────────┘

Parseable Production Deployment (100 GB/day)

┌──────────────────────────────────────────────────────────┐
│              Parseable (1 component + S3)                 │
│                                                          │
│  ┌──────────────────┐      ┌──────────────────────────┐  │
│  │    Parseable      │────▶│    S3 / Object Storage   │  │
│  │  (1-3 instances   │     │    (managed service,     │  │
│  │   for HA, <50MB   │     │     no provisioning)     │  │
│  │   RAM each)       │     │                          │  │
│  └──────────────────┘      └──────────────────────────┘  │
│                                                          │
│  Full MELT observability included:                       │
│  Logs + Metrics + Events + Traces                        │
│                                                          │
│  Total processes: 1-3                                    │
│  Total RAM: <150 MB                                      │
│  JVM heaps to tune: 0                                    │
│  Config files to maintain: 1                             │
│  Upgrade coordination: 1 binary                          │
└──────────────────────────────────────────────────────────┘

The contrast is stark. Every hour your team spends tuning Elasticsearch JVM heap sizes, debugging Logstash pipeline bottlenecks, managing Filebeat deployments, or coordinating version upgrades across the ELK stack is an hour not spent on shipping product features. Parseable eliminates this entire category of operational work.

Why Parseable Leads Logstash Alternatives in 2026

The shift from ELK/Logstash to Parseable is driven by a confluence of factors that go beyond any single feature comparison. Here is why engineering organizations are making the switch in 2026.

Eliminate the Entire ELK Stack with One Binary

The most immediate benefit is operational simplicity. Replacing Filebeat + Logstash + Elasticsearch + Kibana with a single Parseable binary is not just a reduction in component count. It eliminates entire categories of operational work:

• No JVM tuning. Parseable is built in Rust, compiled to a native binary. There is no garbage collector to tune, no heap size to configure, no GC pause storms to diagnose. The binary starts in milliseconds and uses less than 50 MB of RAM.
• No shard management. Elasticsearch requires careful shard sizing, rebalancing, and lifecycle management. Too many shards and the cluster degrades. Too few and queries slow down. Parseable writes Parquet files to S3 with no shard concept at all.
• No pipeline debugging. Logstash pipeline failures, whether from grok pattern mismatches, codec errors, or output buffer overflows, are a constant source of operational pain. Parseable's direct HTTP ingest eliminates the pipeline entirely.
• No version coordination. Upgrading an ELK stack requires careful version compatibility checks across four or more components. Parseable is one binary. Upgrade means deploying one new version.

S3-Native Storage Changes the Economics

Parseable's decision to use S3-compatible object storage as its primary storage backend is an architectural choice that fundamentally changes the cost equation. S3 storage costs $0.023/GB/month for standard tier. With Parseable's Parquet compression achieving 80-90% ratios, the effective storage cost for observability data drops to approximately $0.003-$0.005/GB/month of raw data.

Compare this to Elasticsearch, which requires provisioned SSD storage at $0.08-$0.10/GB/month (gp3 EBS), needs replica shards that double storage requirements, and demands over-provisioning for performance headroom. The storage cost difference alone accounts for most of the 80-90% total cost reduction.

But cost is only part of the story. S3-native storage also provides:

• Unlimited retention without infrastructure changes. Want to keep 2 years of data? Just update the lifecycle policy. No new nodes, no tier migrations.
• Durability guarantees. S3 provides 99.999999999% (11 nines) durability. Elasticsearch cluster failures can lose data if replication is misconfigured.
• No capacity planning. S3 scales automatically. No need to predict storage growth and provision nodes in advance.
• Data portability. Your data is in standard Parquet format on your S3 buckets. You can query it with any Parquet-compatible tool (Spark, DuckDB, Athena, Trino) independent of Parseable.

SQL Replaces Proprietary Query Languages

The ELK stack requires engineers to learn KQL for Kibana searches, Lucene syntax for advanced queries, and Elasticsearch DSL for programmatic access. That is three query syntaxes for one platform, none of which transfer to any other tool.

Parseable uses standard SQL. Every engineer already knows it. There is no training budget for a new query language, no specialized Elasticsearch expertise required on the team, and no queries that become worthless if you switch platforms. SQL is the most portable query language in existence.

This matters for hiring too. Finding engineers with SQL skills is trivial. Finding engineers with deep Elasticsearch DSL expertise is expensive and competitive.

Full MELT Observability in One Place

With the ELK stack, you get logs. For metrics, you add Metricbeat or Prometheus. For traces, you add Elastic APM or Jaeger. For events, you build custom integrations. Each addition introduces new components, new configurations, and new failure modes.

Parseable handles all four MELT signal types natively. Logs, Metrics, Events, and Traces flow through the same ingest endpoint, are stored in the same Parquet files on S3, and are queried with the same SQL interface. When investigating an incident, you move from a metric anomaly to related traces to specific log lines without switching tools or query languages.

Native OpenTelemetry Means No Vendor Lock-in

Parseable's native OTLP endpoint accepts telemetry from any OpenTelemetry-instrumented application or collector. This is important because it means your instrumentation is decoupled from your observability backend. If you ever need to switch platforms again, you change one collector configuration, not your entire application instrumentation.

Logstash has added OpenTelemetry support through plugins, but it remains a bolt-on rather than a native capability. The OTel plugin ecosystem for Logstash is less mature than the native OTLP support in purpose-built observability platforms.

AI-Native Analysis for Modern Workloads

As organizations deploy more AI and LLM workloads in production, the observability requirements shift. Token usage monitoring, inference latency tracking, prompt pattern analysis, and agent behavior tracing are not use cases that the ELK stack was designed for. Parseable's AI-native analysis capabilities address these requirements directly, providing purpose-built tooling for the workloads that are growing fastest in 2026.

Migration Guide: ELK/Logstash to Parseable

Migrating from the ELK stack to Parseable is straightforward because Parseable accepts data through standard protocols. You do not need a big-bang migration. The recommended approach is to run Parseable alongside ELK during a transition period, gradually moving log streams until you are confident in the new platform.

Step 1: Deploy Parseable

Option 1: Parseable Cloud (Recommended)

Sign up at app.parseable.com — free tier available, production-ready in under a minute. Starts at $0.37/GB ingested. Your OTLP endpoint and console are available immediately — no Docker, no binary, no S3 configuration. Skip to Step 2.

Option 2: Self-Hosted

Docker (quickest start):

docker run -p 8000:8000 \
  parseable/parseable:latest \
  parseable local-store

Docker with S3 backend (production):

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

Install script:

curl https://sh.parseable.io | sh

Parseable is now running and ready to receive data. The web console is accessible at http://localhost:8000.

Step 2: Create a Log Stream

curl -X PUT http://localhost:8000/api/v1/logstream/app-logs \
  -H "Authorization: Basic <credentials>"

Step 3: Replace Logstash Output with Parseable

If you are currently running Logstash, the simplest migration path is to add Parseable as an output alongside Elasticsearch, letting you compare both systems in parallel.

Option A: Add Parseable as a Logstash HTTP output

# logstash.conf - add Parseable as an additional output
output {
  # Keep existing Elasticsearch output during migration
  elasticsearch {
    hosts => ["http://es-node1:9200"]
    index => "logs-%{+YYYY.MM.dd}"
  }
 
  # Add Parseable output
  http {
    url => "http://parseable:8000/api/v1/logstream/app-logs"
    http_method => "post"
    format => "json_batch"
    headers => {
      "Authorization" => "Basic <credentials>"
    }
  }
}

Option B: Replace Logstash entirely with OTel Collector (recommended)

This is the recommended approach because it eliminates Logstash and its JVM overhead entirely, replacing it with a lightweight OpenTelemetry Collector:

# otel-collector-config.yaml
receivers:
  filelog:
    include:
      - /var/log/app/*.log
    operators:
      - type: json_parser
        timestamp:
          parse_from: attributes.timestamp
          layout: '%Y-%m-%dT%H:%M:%SZ'
 
  otlp:
    protocols:
      grpc:
        endpoint: 0.0.0.0:4317
      http:
        endpoint: 0.0.0.0:4318
 
processors:
  batch:
    timeout: 5s
    send_batch_size: 1000
 
exporters:
  otlphttp/parseable:
    endpoint: "http://parseable:8000/v1"
    headers:
      Authorization: "Basic <credentials>"
 
service:
  pipelines:
    logs:
      receivers: [filelog, otlp]
      processors: [batch]
      exporters: [otlphttp/parseable]
    traces:
      receivers: [otlp]
      processors: [batch]
      exporters: [otlphttp/parseable]
    metrics:
      receivers: [otlp]
      processors: [batch]
      exporters: [otlphttp/parseable]

Option C: Direct HTTP ingest from your application

For applications that currently log to stdout/files for Filebeat to collect, the simplest path is to send logs directly to Parseable's HTTP API. Most logging libraries support HTTP log appenders:

# Python example using requests
import requests
import json
from datetime import datetime
 
def send_to_parseable(log_entry):
    requests.post(
        "http://parseable:8000/api/v1/logstream/app-logs",
        headers={
            "Content-Type": "application/json",
            "Authorization": "Basic <credentials>"
        },
        json=[{
            "timestamp": datetime.utcnow().isoformat() + "Z",
            "level": log_entry["level"],
            "service": "my-service",
            "message": log_entry["message"],
            "trace_id": log_entry.get("trace_id", "")
        }]
    )

Step 4: Verify and Query

Once data is flowing into Parseable, verify it through the web console or via SQL:

# Query via API
curl -u admin:admin \
  -X POST http://localhost:8000/api/v1/app-logs/query \
  -H "Content-Type: application/json" \
  -d '{
    "query": "SELECT level, count(*) as count FROM app_logs WHERE p_timestamp > NOW() - INTERVAL '\''1 hour'\'' GROUP BY level",
    "startTime": "2026-02-18T00:00:00Z",
    "endTime": "2026-02-18T23:59:59Z"
  }'

Step 5: Decommission ELK Components

Once you have validated that Parseable is receiving and correctly storing all your data, decommission the ELK stack in reverse order:

1. Remove the Elasticsearch output from Logstash (or remove Logstash entirely if using OTel Collector)
2. Shut down Kibana instances
3. Shut down Elasticsearch data nodes (after ensuring retention period has passed or data has been archived)
4. Shut down Elasticsearch master nodes
5. Remove Filebeat agents from hosts (if using OTel Collector instead)
6. Remove Logstash instances

At this point, your entire observability stack is a Parseable binary and an S3 bucket. The infrastructure savings begin immediately.

Frequently Asked Questions

Is Logstash still relevant in 2026?

Logstash remains functional and continues to receive updates, but its relevance has declined significantly. The rise of OpenTelemetry has reduced the need for a dedicated pipeline processor, since OTel Collectors handle the same data collection and transformation use cases with a lighter footprint and vendor-neutral design. For a broader look at the landscape, see our guide to open source log management tools. Many teams that previously relied on Logstash are migrating to either OTel Collectors (for pipeline processing) or unified platforms like Parseable (which eliminate the need for a separate pipeline entirely). Logstash's JVM overhead, Grok-based configuration complexity, and dependency on Elasticsearch for storage make it a less attractive choice for new deployments in 2026.

Can Parseable replace the entire ELK stack?

Yes. Parseable replaces all four ELK components: Filebeat (collection), Logstash (processing), Elasticsearch (storage and indexing), and Kibana (visualization and querying). Applications send data directly to Parseable's HTTP API or OTLP endpoint, eliminating the need for collection agents and pipeline processors. Parseable stores data on S3 in Parquet format, eliminating Elasticsearch. Parseable's built-in web console and SQL query interface replace Kibana. Beyond replacing ELK, Parseable adds capabilities that ELK does not provide natively: metrics, traces, events, AI-native analysis, and OpenTelemetry-native ingestion.

How does Parseable compare to Logstash for data transformation?

Logstash transforms data in-flight using filter plugins (grok, mutate, ruby, etc.) before sending it to Elasticsearch. This means you must define all transformations before data is stored, and if you miss a transformation, the data is stored without it. Parseable takes a schema-on-read approach: raw data is stored in its original form, and transformations are applied at query time using SQL. This is more flexible because you can apply new transformations to historical data without re-ingesting it. For teams that need complex real-time enrichment (GeoIP lookups, external API calls), an OTel Collector in front of Parseable can handle these transformations while keeping Parseable focused on storage and query.

What is the performance difference between Elasticsearch and Parseable for queries?

Elasticsearch excels at full-text keyword searches due to its inverted index architecture. If your primary use case is searching for specific strings across billions of log lines, Elasticsearch is fast at that specific task. However, for analytical queries involving aggregations, time-series analysis, and cross-field correlations, Parseable's columnar Parquet format with Apache Arrow DataFusion delivers sub-second performance that matches or exceeds Elasticsearch. The difference is architectural: Elasticsearch reads entire documents for every query, while Parseable's columnar format reads only the fields needed for a given query, reducing I/O dramatically for analytical workloads.

Does Parseable support all the data sources that Logstash supports?

Parseable does not replicate Logstash's 200+ input plugins directly. Instead, it accepts data through standard protocols: HTTP API, OTLP (OpenTelemetry Protocol), and syslog. For data sources that require specialized collection (Windows Event Log, JDBC, cloud provider APIs), the recommended approach is to use an OpenTelemetry Collector with the appropriate receiver plugin, which then exports to Parseable's OTLP endpoint. The OTel Collector ecosystem covers the vast majority of Logstash input plugin use cases while providing a vendor-neutral, actively maintained collection layer.

How long does migration from ELK to Parseable take?

A basic migration, deploying Parseable and redirecting one log stream, can be completed in under an hour. A full production migration for a mid-sized organization (50-200 hosts, 10-20 log streams) typically takes 1-2 weeks, including a parallel-run period where both ELK and Parseable receive the same data for validation. The migration complexity depends primarily on how many custom Logstash pipelines you have and whether they contain complex transformation logic that needs to be replicated. Teams that use Logstash primarily as a pass-through to Elasticsearch (minimal filtering) experience the fastest migrations.

Start Evaluating Logstash Alternatives Today

If you are running the ELK stack and spending engineering time on Logstash pipeline management, Elasticsearch cluster tuning, and Kibana maintenance, Parseable offers a path to dramatically simpler and cheaper 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

Replace your entire ELK stack with one binary. Your infrastructure budget and your on-call engineers will thank you.

Looking for more comparisons? Read our guides on Splunk alternatives, Datadog alternatives for logs, and ELK to Parseable migration for comprehensive evaluations of the observability landscape in 2026.