How to Migrate from ELK Stack to Parseable (Step-by-Step Guide)

Updated: February 2026 | Reading time: 18 min

Introduction

The ELK Stack — Elasticsearch, Logstash, Kibana, and Beats — has been the default open-source log analytics platform for over a decade. But in 2026, running ELK at scale means managing seven or more components, tuning JVM heaps, babysitting shard allocations, and watching your infrastructure bill climb with every gigabyte of retention. There is a better way.

This guide walks you through a complete, production-tested migration from ELK Stack to Parseable — a unified observability platform that replaces every ELK component with a single Rust binary backed by S3 storage. Parseable Cloud starts at $0.37/GB ingested ($29/month minimum) with a free tier, and a self-hosted deployment is also available. We cover planning, data export, collector reconfiguration, dual-shipping, validation, cutover, and decommissioning. By the end, you will have a clear, step-by-step playbook you can execute this week.

TL;DR: Parseable replaces Elasticsearch + Logstash + Kibana + Beats with one binary. Storage drops from $0.10-0.30/GB (Lucene indexes on SSDs) to ~$0.023/GB (S3). You query with SQL instead of KQL. You get logs, metrics, events, and traces in a single platform with native OTLP support. This guide shows you exactly how to make the switch.

Why Teams Need an ELK Stack Alternative

Before diving into the migration steps, it is worth understanding why so many teams are actively searching for an ELK stack alternative in 2026.

1. Component Sprawl

A production ELK deployment is not three components — it is seven or more:

Each component requires its own deployment, monitoring, upgrades, and capacity planning. That is a full-time job for a dedicated team.

2. Storage Costs Are Unsustainable

Elasticsearch stores data in Lucene indexes that require fast SSD or NVMe storage. At scale, this gets expensive fast:

• Elasticsearch on SSD: $0.10–$0.30 per GB/month
• Parseable on S3: ~$0.023 per GB/month

For a team retaining 10TB of logs, that is $1,000–$3,000/month on Elasticsearch storage vs. ~$230/month on S3. Multiply by 12 months and the savings fund an entire engineering hire.

3. JVM Tax

Both Elasticsearch and Logstash run on the JVM. This means:

• Minimum 4–8GB heap per node (often 30GB+ in production)
• Garbage collection pauses that can spike query latency
• JVM version compatibility headaches across upgrades
• Memory overhead that inflates your compute costs

Parseable is built in Rust. No JVM. No garbage collector. Sub-50MB memory footprint at idle.

4. KQL Is a Dead End

Kibana Query Language (KQL) is proprietary to the Elastic ecosystem. Your team's query knowledge does not transfer to any other tool. SQL, on the other hand, is the most widely known query language on the planet. Parseable uses standard SQL via Apache Arrow DataFusion, so your team is productive on day one.

5. Logs-Only Limitation

ELK is fundamentally a logging stack. Adding metrics requires Metricbeat + Elasticsearch. Adding traces requires Elastic APM + Elasticsearch. Each addition multiplies the component count and operational burden. Parseable is a unified observability platform that handles logs, metrics, events, and traces (MELT) natively — with a single OTLP endpoint.

ELK Stack vs. Parseable: Architecture Comparison

Understanding the architectural difference is the key to understanding why the migration simplifies your stack so dramatically.

ELK Stack Architecture (7+ Components)

┌──────────────┐     ┌──────────────┐     ┌──────────────┐
│   Filebeat   │────▶│   Logstash   │────▶│Elasticsearch │
│  (per host)  │     │   (JVM)      │     │   (JVM)      │
└──────────────┘     └──────────────┘     │  Data Nodes  │
                                          │  Master Nodes│
┌──────────────┐                          │  Ingest Nodes│
│  Metricbeat  │─────────────────────────▶│              │
│  (per host)  │                          └──────┬───────┘
└──────────────┘                                 │
                                          ┌──────▼───────┐
                                          │    Kibana     │
                                          │   (Node.js)  │
                                          └──────────────┘

• Components: 7+ (Filebeat, Metricbeat, Logstash, ES data, ES master, ES ingest, Kibana)
• Languages: Java (ES, Logstash), Go (Beats), Node.js (Kibana)
• Storage: Lucene indexes on SSD/NVMe
• Query: KQL (proprietary)
• Memory: 30–100GB+ across the cluster

Parseable Architecture (1 Binary)

┌──────────────────┐     ┌──────────────┐     ┌──────────────┐
│  OTel Collector  │────▶│   Parseable  │────▶│  S3 / MinIO  │
│  or FluentBit    │     │ (single Rust │     │  (Parquet)   │
│  (lightweight)   │     │   binary)    │     │              │
└──────────────────┘     │              │     └──────────────┘
                         │  Console     │
                         │  (built-in)  │
                         └──────────────┘

• Components: 1 (Parseable binary with built-in Console)
• Language: Rust
• Storage: Apache Parquet on S3-compatible object storage
• Query: Standard SQL
• Memory: Sub-50MB at idle

The operational difference is stark. You go from managing a distributed JVM cluster with shard rebalancing, heap tuning, and index lifecycle policies to running a single binary that writes Parquet files to S3. For teams that want zero infrastructure management, Parseable Cloud eliminates even the single binary — starts at $0.37/GB ingested ($29/month minimum) with a free tier, and you get a managed endpoint ready to receive data immediately.

Pre-Migration Checklist

Before you touch any infrastructure, complete this checklist. Skipping these steps is the number-one cause of failed migrations.

Inventory Your Current ELK Setup

• List all Elasticsearch indexes: GET _cat/indices?v&h=index,docs.count,store.size,creation.date
• Document index patterns: Which applications write to which indexes?
• Record daily ingestion volume: GET _cat/nodes?v&h=name,heap.percent,disk.used,disk.total
• Export Logstash pipeline configs: These define your parsing and enrichment logic
• List Kibana dashboards and saved searches: Export via Kibana Saved Objects API
• Document alerting rules: Kibana Alerting or ElastAlert configurations
• Identify retention requirements: How long must data be retained per index?
• Map data sources: Which Filebeat/Metricbeat instances ship to which indexes?

Define Success Criteria

• All log sources shipping to Parseable
• Query latency comparable to or better than Elasticsearch
• All critical dashboards recreated in Parseable Console
• Alerting rules migrated and validated
• Data completeness verified (no dropped logs during transition)
• Team trained on SQL querying (usually minimal given SQL familiarity)

Allocate Resources

Using Parseable Cloud? Skip the first two items — infrastructure is fully managed. You only need network access to the Cloud endpoint and an agreed parallel-run timeline.

• S3-compatible storage bucket created (AWS S3, MinIO, Tigris, or any S3-compatible store)
• Compute for Parseable server (2 vCPU, 4GB RAM is sufficient for most workloads under 500GB/day)
• Network access between log sources and Parseable endpoint
• Parallel-run timeline agreed with stakeholders (recommended: 1–2 weeks)

Step-by-Step Migration Guide

Step 1: Audit Your Current ELK Setup

Start by getting a complete picture of what you are running. SSH into your Elasticsearch cluster and run the following.

Get index inventory:

curl -s "http://elasticsearch:9200/_cat/indices?v&h=index,docs.count,store.size&s=store.size:desc" | head -20

Example output:

index                       docs.count   store.size
app-logs-2026.02.17          48293821       12.4gb
app-logs-2026.02.16          51029384       13.1gb
infra-logs-2026.02.17        12847291        3.2gb
metrics-system-2026.02.17     8293012        1.8gb

Get Logstash pipeline details:

# List all pipeline files
ls -la /etc/logstash/conf.d/
 
# Review each pipeline
cat /etc/logstash/conf.d/app-logs.conf

Export Kibana dashboards:

# Export all saved objects
curl -s "http://kibana:5601/api/saved_objects/_export" \
  -H "kbn-xsrf: true" \
  -H "Content-Type: application/json" \
  -d '{"type":["dashboard","visualization","index-pattern","search"]}' \
  > kibana-export.ndjson

Record everything in a migration spreadsheet. You need to know:

1. How many unique log sources you have
2. Daily ingestion volume per source
3. Which Logstash filters apply to which pipelines
4. Which Kibana dashboards are actively used (check Kibana's Usage Statistics)
5. Current retention periods per index

Step 2: Deploy Parseable

Parseable can be deployed in four ways. Choose the one that matches your infrastructure.

Option A: Parseable Cloud (Recommended — Zero Infrastructure)

The fastest path to production. Sign up for Parseable Cloud and get a managed endpoint in under a minute. Starts at $0.37/GB ingested ($29/month minimum) with a free tier available. No servers to provision, no S3 buckets to configure, no binaries to download. Once your Cloud instance is ready, skip directly to Step 3 and point your collectors at your Parseable Cloud endpoint URL.

Option B: Docker (Fastest for Testing)

# With local storage (evaluation only)
docker run -p 8000:8000 \
  -e P_USERNAME=admin \
  -e P_PASSWORD=admin \
  parseable/parseable:latest
 
# With S3 backend (production)
docker run -p 8000:8000 \
  -e P_S3_URL=https://s3.amazonaws.com \
  -e P_S3_BUCKET=parseable-logs \
  -e P_S3_REGION=us-east-1 \
  -e P_S3_ACCESS_KEY=YOUR_ACCESS_KEY \
  -e P_S3_SECRET_KEY=YOUR_SECRET_KEY \
  -e P_USERNAME=admin \
  -e P_PASSWORD=admin \
  parseable/parseable:latest

Option C: Binary (Simplest for Production VMs)

# Download and install
curl https://sh.parseable.com | sh
 
# Configure environment
export P_S3_URL=https://s3.amazonaws.com
export P_S3_BUCKET=parseable-logs
export P_S3_REGION=us-east-1
export P_S3_ACCESS_KEY=YOUR_ACCESS_KEY
export P_S3_SECRET_KEY=YOUR_SECRET_KEY
export P_USERNAME=admin
export P_PASSWORD=admin
 
# Start Parseable
parseable

Option D: Kubernetes with Helm (Production Clusters)

# Add Parseable Helm repo
helm repo add parseable https://charts.parseable.com
helm repo update
 
# Create values file
cat > parseable-values.yaml <<EOF
parseable:
  store: "s3-store"
  s3store:
    s3Url: "https://s3.amazonaws.com"
    s3Bucket: "parseable-logs"
    s3Region: "us-east-1"
    s3AccessKey: "YOUR_ACCESS_KEY"
    s3SecretKey: "YOUR_SECRET_KEY"
  auth:
    username: "admin"
    password: "admin"
  resources:
    requests:
      cpu: "1"
      memory: "2Gi"
    limits:
      cpu: "2"
      memory: "4Gi"
EOF
 
# Install
helm install parseable parseable/parseable \
  -f parseable-values.yaml \
  -n parseable --create-namespace

Verify Deployment

After deploying, verify Parseable is running:

# Health check
curl http://parseable:8000/api/v1/liveness
 
# Open the Console
# Navigate to http://parseable:8000 in your browser

You should see the Parseable Console — this replaces Kibana entirely.

Step 3: Configure Log Shipping to Parseable

This is the critical step. You need to redirect your log sources from ELK to Parseable. The approach depends on what collectors you are currently running.

Option A: Replace Filebeat + Logstash with OpenTelemetry Collector

This is the recommended approach. The OpenTelemetry Collector replaces both Filebeat (collection) and Logstash (transformation) while also supporting metrics and traces.

Install OTel Collector:

# Download the contrib distribution (includes all receivers/exporters)
curl -LO https://github.com/open-telemetry/opentelemetry-collector-releases/releases/download/v0.98.0/otelcol-contrib_0.98.0_linux_amd64.tar.gz
tar xzf otelcol-contrib_0.98.0_linux_amd64.tar.gz

Create OTel Collector config (otel-collector-config.yaml):

receivers:
  # Collect log files (replaces Filebeat)
  filelog:
    include:
      - /var/log/app/*.log
      - /var/log/syslog
    start_at: beginning
    operators:
      - type: json_parser
        if: body matches "^\\{"
        timestamp:
          parse_from: attributes.timestamp
          layout: "%Y-%m-%dT%H:%M:%S.%LZ"
      - type: regex_parser
        if: body matches "^\\d{4}-\\d{2}-\\d{2}"
        regex: "^(?P<timestamp>\\d{4}-\\d{2}-\\d{2}T\\d{2}:\\d{2}:\\d{2}\\.\\d+Z)\\s+(?P<level>\\w+)\\s+(?P<message>.*)"
        timestamp:
          parse_from: attributes.timestamp
          layout: "%Y-%m-%dT%H:%M:%S.%LZ"
 
  # Collect host metrics (replaces Metricbeat)
  hostmetrics:
    collection_interval: 30s
    scrapers:
      cpu:
      disk:
      filesystem:
      load:
      memory:
      network:
 
  # Accept OTLP from instrumented applications
  otlp:
    protocols:
      grpc:
        endpoint: 0.0.0.0:4317
      http:
        endpoint: 0.0.0.0:4318
 
processors:
  batch:
    send_batch_size: 1000
    timeout: 5s
 
  resourcedetection:
    detectors: [env, system]
    system:
      hostname_sources: ["os"]
 
  resource:
    attributes:
      - key: service.environment
        value: production
        action: upsert
 
exporters:
  otlphttp:
    endpoint: "http://parseable:8000/api/v1"
    headers:
      Authorization: "Basic YWRtaW46YWRtaW4="
    tls:
      insecure: true
 
service:
  pipelines:
    logs:
      receivers: [filelog, otlp]
      processors: [batch, resourcedetection, resource]
      exporters: [otlphttp]
    metrics:
      receivers: [hostmetrics, otlp]
      processors: [batch, resourcedetection]
      exporters: [otlphttp]
    traces:
      receivers: [otlp]
      processors: [batch, resourcedetection]
      exporters: [otlphttp]

Start the collector:

./otelcol-contrib --config otel-collector-config.yaml

This single OTel Collector replaces Filebeat, Metricbeat, and Logstash. It collects logs from files, gathers host metrics, accepts OTLP from instrumented applications, and ships everything to Parseable's native OTLP endpoint.

Option B: Replace Filebeat + Logstash with Fluent Bit

If your team prefers Fluent Bit (CNCF graduated, extremely lightweight), here is the equivalent configuration.

Create Fluent Bit config (fluent-bit.conf):

[SERVICE]
    Flush        5
    Log_Level    info
    Parsers_File parsers.conf
    HTTP_Server  On
    HTTP_Listen  0.0.0.0
    HTTP_Port    2020
 
# Collect application logs
[INPUT]
    Name         tail
    Path         /var/log/app/*.log
    Tag          app.*
    Parser       json
    Mem_Buf_Limit 5MB
    Skip_Long_Lines On
    Refresh_Interval 10
 
# Collect syslog
[INPUT]
    Name         tail
    Path         /var/log/syslog
    Tag          syslog.*
    Parser       syslog-rfc3164
    Mem_Buf_Limit 5MB
 
# Collect container logs (if on Docker/K8s)
[INPUT]
    Name         tail
    Path         /var/lib/docker/containers/*/*.log
    Tag          docker.*
    Parser       docker
    Mem_Buf_Limit 10MB
    Docker_Mode  On
 
# Add hostname metadata
[FILTER]
    Name         record_modifier
    Match        *
    Record       hostname ${HOSTNAME}
    Record       environment production
 
# Ship to Parseable
[OUTPUT]
    Name         http
    Match        app.*
    Host         parseable
    Port         8000
    URI          /api/v1/ingest
    Format       json
    Header       X-P-Stream app-logs
    Header       Authorization Basic YWRtaW46YWRtaW4=
    Json_date_key    timestamp
    Json_date_format iso8601
    Retry_Limit  5
 
[OUTPUT]
    Name         http
    Match        syslog.*
    Host         parseable
    Port         8000
    URI          /api/v1/ingest
    Format       json
    Header       X-P-Stream syslog
    Header       Authorization Basic YWRtaW46YWRtaW4=
    Json_date_key    timestamp
    Json_date_format iso8601
    Retry_Limit  5
 
[OUTPUT]
    Name         http
    Match        docker.*
    Host         parseable
    Port         8000
    URI          /api/v1/ingest
    Format       json
    Header       X-P-Stream docker-logs
    Header       Authorization Basic YWRtaW46YWRtaW4=
    Json_date_key    timestamp
    Json_date_format iso8601
    Retry_Limit  5

Parsers file (parsers.conf):

[PARSER]
    Name         json
    Format       json
    Time_Key     timestamp
    Time_Format  %Y-%m-%dT%H:%M:%S.%LZ
 
[PARSER]
    Name         docker
    Format       json
    Time_Key     time
    Time_Format  %Y-%m-%dT%H:%M:%S.%LZ
 
[PARSER]
    Name         syslog-rfc3164
    Format       regex
    Regex        ^\<(?<pri>[0-9]+)\>(?<time>[^ ]* {1,2}[^ ]* [^ ]*) (?<host>[^ ]*) (?<ident>[a-zA-Z0-9_\/\.\-]*)(?:\[(?<pid>[0-9]+)\])?(?:[^\:]*\:)? *(?<message>.+)$
    Time_Key     time
    Time_Format  %b %d %H:%M:%S

Step 4: Dual-Ship Logs During Transition

This is the safety net. During the transition period (recommended 1–2 weeks), send logs to both ELK and Parseable simultaneously. This allows you to validate data completeness and build confidence before cutting over.

Dual-Shipping with OTel Collector

Update your OTel Collector config to export to both backends:

exporters:
  # Parseable (new)
  otlphttp/parseable:
    endpoint: "http://parseable:8000/api/v1"
    headers:
      Authorization: "Basic YWRtaW46YWRtaW4="
 
  # Elasticsearch (existing - keep during transition)
  elasticsearch:
    endpoints: ["http://elasticsearch:9200"]
    logs_index: "app-logs"
    sending_queue:
      enabled: true
      num_consumers: 10
      queue_size: 1000
 
service:
  pipelines:
    logs:
      receivers: [filelog, otlp]
      processors: [batch, resourcedetection]
      exporters: [otlphttp/parseable, elasticsearch]
    metrics:
      receivers: [hostmetrics, otlp]
      processors: [batch, resourcedetection]
      exporters: [otlphttp/parseable]
    traces:
      receivers: [otlp]
      processors: [batch, resourcedetection]
      exporters: [otlphttp/parseable]

Dual-Shipping with Fluent Bit

Add both outputs to the same configuration:

# Ship to Parseable (new)
[OUTPUT]
    Name         http
    Match        *
    Host         parseable
    Port         8000
    URI          /api/v1/ingest
    Format       json
    Header       X-P-Stream app-logs
    Header       Authorization Basic YWRtaW46YWRtaW4=
    Json_date_key    timestamp
    Json_date_format iso8601
 
# Keep shipping to Elasticsearch (existing)
[OUTPUT]
    Name         es
    Match        *
    Host         elasticsearch
    Port         9200
    Index        app-logs
    Type         _doc
    Suppress_Type_Name On
    Retry_Limit  5

Leave dual-shipping active for your agreed-upon transition period. This is not optional — it is your rollback safety net.

Step 5: Recreate Dashboards and Alerts in Parseable Console

Parseable includes a built-in Console that replaces Kibana entirely. You do not need to install or manage a separate visualization tool.

Mapping Kibana Concepts to Parseable Console

Translating Common KQL Queries to SQL

KQL (Kibana):

status:500 AND service.name:"payment-api" AND @timestamp >= "2026-02-17"

SQL (Parseable):

SELECT *
FROM "app-logs"
WHERE status = 500
  AND "service.name" = 'payment-api'
  AND p_timestamp >= '2026-02-17T00:00:00Z'
ORDER BY p_timestamp DESC
LIMIT 100;

KQL (Kibana) — Aggregation:

service.name:* | stats count() by service.name, status

SQL (Parseable):

SELECT "service.name",
       status,
       COUNT(*) as request_count
FROM "app-logs"
WHERE "service.name" IS NOT NULL
GROUP BY "service.name", status
ORDER BY request_count DESC;

KQL (Kibana) — Time-based analysis:

level:ERROR | timechart count() interval=5m

SQL (Parseable):

SELECT DATE_TRUNC('minute', p_timestamp) AS time_bucket,
       COUNT(*) AS error_count
FROM "app-logs"
WHERE level = 'ERROR'
  AND p_timestamp >= NOW() - INTERVAL '1 hour'
GROUP BY time_bucket
ORDER BY time_bucket;

Setting Up Alerts

In the Parseable Console, navigate to Alerts and create rules based on SQL conditions. For example, to alert on error rate spikes:

-- Alert condition: more than 100 errors in 5 minutes
SELECT COUNT(*) AS error_count
FROM "app-logs"
WHERE level = 'ERROR'
  AND p_timestamp >= NOW() - INTERVAL '5 minutes'
HAVING COUNT(*) > 100;

Parseable supports alert destinations including Slack, PagerDuty, webhooks, and email.

Step 6: Validate Data Completeness

Before cutting over, you must verify that Parseable is receiving all the data that Elasticsearch is receiving. Run these validation checks during the dual-ship period.

Count Comparison

Run count queries on both systems for the same time window:

On Elasticsearch:

curl -s "http://elasticsearch:9200/app-logs-2026.02.17/_count" | jq .count

On Parseable:

SELECT COUNT(*) FROM "app-logs"
WHERE p_timestamp >= '2026-02-17T00:00:00Z'
  AND p_timestamp < '2026-02-18T00:00:00Z';

The counts should be within 1–2% of each other. Small discrepancies are normal due to timing differences in dual-ship delivery.

Spot-Check Specific Events

Pick 10 specific log entries from Elasticsearch and verify they exist in Parseable:

-- Search for a specific request ID
SELECT * FROM "app-logs"
WHERE body LIKE '%req-abc-12345%'
LIMIT 5;

Verify All Sources Are Reporting

-- List all unique sources sending logs
SELECT "service.name", COUNT(*) AS log_count
FROM "app-logs"
WHERE p_timestamp >= NOW() - INTERVAL '1 hour'
GROUP BY "service.name"
ORDER BY log_count DESC;

Compare this list against your pre-migration inventory to confirm no sources are missing.

Test Query Performance

Run your most common queries on both systems and compare latency. Parseable's columnar Parquet storage typically matches or outperforms Elasticsearch for analytical queries, while Elasticsearch may still be faster for single-document lookups with known IDs (a rare pattern in log analytics).

Step 7: Cut Over Production Traffic

Once validation is complete and your team is confident, remove the Elasticsearch exporter from your collector configuration.

For OTel Collector — remove the Elasticsearch exporter:

exporters:
  otlphttp/parseable:
    endpoint: "http://parseable:8000/api/v1"
    headers:
      Authorization: "Basic YWRtaW46YWRtaW4="
  # elasticsearch exporter REMOVED
 
service:
  pipelines:
    logs:
      receivers: [filelog, otlp]
      processors: [batch, resourcedetection]
      exporters: [otlphttp/parseable]   # only Parseable now
    metrics:
      receivers: [hostmetrics, otlp]
      processors: [batch, resourcedetection]
      exporters: [otlphttp/parseable]
    traces:
      receivers: [otlp]
      processors: [batch, resourcedetection]
      exporters: [otlphttp/parseable]

For Fluent Bit — remove the Elasticsearch output:

# Only Parseable output remains
[OUTPUT]
    Name         http
    Match        *
    Host         parseable
    Port         8000
    URI          /api/v1/ingest
    Format       json
    Header       X-P-Stream app-logs
    Header       Authorization Basic YWRtaW46YWRtaW4=
    Json_date_key    timestamp
    Json_date_format iso8601
    Retry_Limit  5

Restart your collectors. Monitor Parseable's ingestion metrics for the first 24 hours to confirm stable operation.

Step 8: Decommission ELK Infrastructure

After running exclusively on Parseable for at least one week with no issues:

1. Stop Logstash instances and remove from process managers (systemd, supervisor, etc.)
2. Stop Kibana — Parseable Console replaces it entirely
3. Stop Filebeat/Metricbeat agents on all hosts (OTel Collector or Fluent Bit replaces them)
4. Snapshot Elasticsearch indexes for compliance if required:

   # Create a snapshot repository
   curl -X PUT "elasticsearch:9200/_snapshot/migration_backup" -H 'Content-Type: application/json' -d '{
     "type": "s3",
     "settings": {
       "bucket": "elk-backup",
       "region": "us-east-1"
     }
   }'
   # Take a final snapshot
   curl -X PUT "elasticsearch:9200/_snapshot/migration_backup/final_snapshot?wait_for_completion=true"

5. Stop Elasticsearch nodes — data nodes first, then master nodes
6. Reclaim infrastructure — terminate VMs, release disk volumes, delete Kubernetes resources
7. Update monitoring — remove ELK health checks from your monitoring system
8. Update runbooks — replace ELK references with Parseable procedures

Cost Savings Calculator

Use this table to estimate your annual savings after migrating from ELK to Parseable:

ELK cost assumptions: Includes compute (EC2/VMs for Elasticsearch data nodes, master nodes, Logstash, Kibana), SSD storage at $0.10–$0.30/GB/month, operational overhead (staff time), and 30-day retention.

Parseable cost assumptions: S3 storage at $0.023/GB/month with 30-day retention, single Parseable server on a modest VM (m5.large or equivalent). For higher ingest volumes, add cluster mode nodes. Alternatively, Parseable Cloud offers managed pricing at $0.37/GB ingested ($29/month minimum) with no infrastructure to manage.

The savings come from two sources: (1) S3 storage is an order of magnitude cheaper than SSD, and (2) Parseable's single-binary architecture eliminates the compute overhead of running 7+ JVM-based components.

Common Migration Pitfalls and How to Avoid Them

Pitfall 1: Skipping the Dual-Ship Phase

Problem: Teams cut over to Parseable immediately and discover missing data sources or parsing issues with no rollback path.

Solution: Always dual-ship for at least one week. The S3 storage cost for running both systems in parallel is negligible compared to the risk of data loss.

Pitfall 2: Not Mapping All Data Sources

Problem: You migrate the main application logs but forget about sidecar containers, cron jobs, or legacy applications still shipping directly to Logstash.

Solution: Before migration, run GET _cat/indices?v to see every index in Elasticsearch. Cross-reference this with your Filebeat registry to identify every data source.

Pitfall 3: Hardcoding Elasticsearch Endpoints

Problem: Some applications send logs directly to Elasticsearch via the REST API instead of going through a collector.

Solution: Audit your codebase for direct Elasticsearch client usage. Replace with OpenTelemetry SDK instrumentation or HTTP-based log shipping to Parseable's /api/v1/ingest endpoint.

Pitfall 4: Ignoring Logstash Transformation Logic

Problem: Logstash grok filters, mutate processors, and conditional routing logic get lost in the migration.

Solution: Replicate parsing logic in the OTel Collector's transform processor or Fluent Bit's filter plugins. Document each Logstash pipeline and map it to its equivalent in your new collector.

Pitfall 5: Not Training the Team on SQL

Problem: Engineers accustomed to KQL struggle with the switch.

Solution: SQL is far more widely known than KQL, so this is rarely a real problem. Provide a cheat sheet mapping common KQL patterns to SQL equivalents (see the table in Step 5 above). Parseable also supports AI-powered natural language to SQL generation, which further reduces the learning curve.

Pitfall 6: Trying to Migrate Historical Data

Problem: Teams try to export all historical data from Elasticsearch and re-ingest into Parseable, creating a massive one-time load.

Solution: In most cases, it is better to let historical data age out in Elasticsearch snapshots and start fresh in Parseable from the cutover date. If you truly need historical data, export it as JSON and batch-ingest it to Parseable over time, not all at once.

Frequently Asked Questions

Can I migrate from ELK to Parseable without downtime?

Yes. The dual-shipping approach described in Step 4 ensures zero downtime. During the transition period, both systems receive the same data. You switch off ELK only after validating that Parseable is receiving and processing all data correctly.

How does Parseable handle the search capabilities that Elasticsearch provides?

Parseable uses SQL via Apache Arrow DataFusion for querying. While the query syntax is different from KQL/Lucene, SQL is actually more expressive for analytical queries (JOINs, window functions, CTEs). Parseable's columnar Parquet storage is optimized for the scan-heavy query patterns typical in log analytics. For full-text search across unstructured log fields, use LIKE or ILIKE operators.

What happens to my Kibana dashboards?

Kibana dashboards cannot be directly imported into Parseable Console. However, the translation is straightforward: each KQL query becomes a SQL query, and each visualization becomes a SQL query with a chart type. Most teams recreate their top 10–15 dashboards in a single day. Parseable's AI query assistant can help generate the SQL from natural language descriptions of what each dashboard shows.

Does Parseable support role-based access control?

Yes. Parseable supports RBAC with configurable roles, OIDC-based SSO integration, and stream-level access control. You can restrict users to specific log streams, which maps directly to how most ELK deployments use index-level security. For detailed SSO setup, see the Parseable SSO guide.

How does Parseable handle high availability?

Parseable supports cluster mode for high availability. Multiple Parseable nodes share the same S3 backend with smart caching. Because the source of truth is S3 (which is designed for 99.999999999% durability), losing a Parseable node is a temporary compute issue, not a data loss event. Compare this to Elasticsearch, where losing a data node without sufficient replicas can mean data loss.

Can I send metrics and traces alongside logs?

Absolutely. This is one of Parseable's key advantages over ELK. Parseable is a unified observability platform with native OTLP endpoints for logs, metrics, events, and traces. Point your OpenTelemetry Collector at Parseable's OTLP endpoint and it handles all telemetry types. No need for separate Metricbeat, Elastic APM, or Jaeger deployments. See the OpenTelemetry Demo with Parseable for a hands-on example.

Docker Compose: Complete Migration Stack

For teams that want to test the full migration locally, here is a Docker Compose file that runs Parseable with an OTel Collector and MinIO (S3-compatible storage):

version: "3.8"
services:
  minio:
    image: minio/minio:latest
    command: server /data --console-address ":9001"
    environment:
      MINIO_ROOT_USER: minioadmin
      MINIO_ROOT_PASSWORD: minioadmin
    ports:
      - "9000:9000"
      - "9001:9001"
    volumes:
      - minio-data:/data
 
  minio-setup:
    image: minio/mc:latest
    depends_on:
      - minio
    entrypoint: >
      /bin/sh -c "
      sleep 5;
      mc alias set local http://minio:9000 minioadmin minioadmin;
      mc mb local/parseable-logs --ignore-existing;
      exit 0;
      "
 
  parseable:
    image: parseable/parseable:latest
    depends_on:
      minio-setup:
        condition: service_completed_successfully
    environment:
      P_S3_URL: "http://minio:9000"
      P_S3_BUCKET: "parseable-logs"
      P_S3_REGION: "us-east-1"
      P_S3_ACCESS_KEY: "minioadmin"
      P_S3_SECRET_KEY: "minioadmin"
      P_S3_PATH_STYLE: "true"
      P_USERNAME: "admin"
      P_PASSWORD: "admin"
    ports:
      - "8000:8000"
 
  otel-collector:
    image: otel/opentelemetry-collector-contrib:0.98.0
    depends_on:
      - parseable
    volumes:
      - ./otel-collector-config.yaml:/etc/otelcol-contrib/config.yaml
      - /var/log:/var/log:ro
    ports:
      - "4317:4317"   # OTLP gRPC
      - "4318:4318"   # OTLP HTTP
    command: ["--config", "/etc/otelcol-contrib/config.yaml"]
 
volumes:
  minio-data:

Save the OTel Collector config from Step 3 (Option A) as otel-collector-config.yaml alongside this Compose file, then run:

docker compose up -d

You will have a complete observability stack running locally in under a minute: MinIO for S3-compatible storage, Parseable for unified observability, and OTel Collector for telemetry collection. Open http://localhost:8000 to access the Parseable Console.

Related Reading

• Elasticsearch vs Parseable: Architecture Deep Dive — detailed comparison of storage engines, query languages, and total cost of ownership
• Parseable Console vs Kibana — compare the built-in Console with Kibana's visualization capabilities
• Logstash vs Parseable — why teams are replacing Logstash pipelines with a single binary
• Best Splunk Alternatives for Log Analytics — if you are also evaluating Splunk alongside ELK
• Kubernetes Logs with OTel Collector and Parseable — detailed guide for K8s-specific log collection
• Fluent Bit Demo with Parseable — hands-on Fluent Bit integration tutorial
• OpenTelemetry Demo with Parseable — full OTLP integration walkthrough

Your ELK Stack Alternative Awaits

Migrating from ELK Stack to Parseable is not just a cost optimization — it is an architectural upgrade. You replace seven or more JVM-based components with a single Rust binary. You replace expensive SSD-backed Lucene indexes with S3 storage at a fraction of the cost. You replace proprietary KQL with universal SQL. And you gain native support for metrics, events, and traces alongside logs — turning a logging stack into a unified observability platform.

The migration itself is straightforward when you follow the dual-ship approach outlined in this guide. Deploy Parseable alongside ELK, redirect your collectors to ship to both, validate completeness, and cut over when confident. Most teams complete the full migration in one to two weeks.

The infrastructure you decommission at the end — Elasticsearch nodes, Logstash instances, Kibana servers, Beats agents — represents both an immediate cost saving and a permanent reduction in operational complexity. That is time and money your team can redirect toward building your product instead of managing your observability stack.

Ready to start your migration?

• 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