ELK to Oodle AI Migration Guide

Risk-Free Migration. 3x Cost Savings. Zero Ops.

Elasticsearch
Oodle AI
Ops Overhead
ILM, shards, JVM tuning, upgrades
→
Zero operational overhead
Query Speed
Slow dashboards & query timeouts
→
Blazing fast search, any time range
Reliability
Data loss, full-disk outages
→
99.9% uptime SLA, zero data loss

Plus, AI-powered debugging helps teams resolve incidents faster.

For detailed comparisons: Elastic Cloud | Self-Hosted ELK | Shared Responsibilities

Olyv
"Oodle is five times faster than our Elasticsearch cluster. Our developers naturally moved to Oodle - they didn't need to be told."

Yashwanth Reddy

Senior DevOps Engineer, Olyv

Fuel
"We'd been juggling CloudWatch and Elastic/OpenSearch - one was reliable but painful, the other easier but flaky. With Oodle, we got the best of both."

Brandon Kite

CTO, Fuel

Cureskin
"Oodle AI debugging has transformed how we debug and make decisions. What used to take hours now takes minutes."

Ramakrishna R

Co-founder and CTO, Cureskin

Reliability

99.9% availability guarantee

< 1 minute ingestion latency for Logs, Metrics and Traces

Status Page →

Security

ISO, SOC2 Type2 & GDPR compliant

RBAC & SSO/SAML integration

Single-tenant deployment option

Trust Center →

24x7 Support

Response SLA:

Critical: 15m | High: ~1h | Other: <1d

Private Slack, Intercom & Email

"The best thing about Oodle is the team. They're always there to help out." - Suman, CTO, Fello AI

Trusted by Public companies, Unicorns and high-growth startups

Cureskin
HappyPathWisdom AIFuelLookoutZaggle
CureFit
Fello
Distacart
Workorb
Effective AI
different.ai
LabraBedrockdata
Cureskin
HappyPathWisdom AIFuelLookoutZaggle
CureFit
Fello
Distacart
Workorb
Effective AI
different.ai
LabraBedrockdata
Cureskin
HappyPathWisdom AIFuelLookoutZaggle
CureFit
Fello
Distacart
Workorb
Effective AI
different.ai
LabraBedrockdata

Migration Journey

Run both systems in parallel, verify everything works, then switch.

No Data Loss
Zero Downtime
Dashboards & Alerts Preserved
1

Ingestion & Setup

⏱️ 2 hours for 1 engineer

✅ Configure dual-ship to Oodle

✅ Import dashboards & alerts

2

Verification

⏱️ 1-2 weeks (calendar time)

✅ Validate log queries & dashboards

✅ Compare search results side-by-side

3

Cutover

⏱️ 2 hours for 1 engineer

✅ Activate Oodle alerts

✅ Decommission Elasticsearch

Step 1

Ingestion & Setup

Effort: 2 hours for 1 engineer

Overview

In this phase, you'll configure your log shippers to send data to both Elasticsearch and Oodle simultaneously. This dual-ship approach ensures zero risk - your existing Elasticsearch setup remains unchanged while you validate Oodle in parallel.

Key benefit: Continue using your existing shippers (Filebeat, Logstash, Vector, OTel, Fluent Bit). No new agents to deploy - just add Oodle as an additional output.

Dual-Ship Architecture - Data flowing to both Elasticsearch and Oodle

Prerequisites

  • Access to update log shipper configurations (Filebeat, Logstash, Vector, etc.)
  • Oodle instance URL and API key (from signup)
  • If using Grafana for alerting: Grafana URL & Service Account Token

1.a Configure Dual-Ship

Add Oodle as an additional destination for your logs. Choose the shipper you're currently using:

Logstash

If you're using the Filebeat → Logstash pipeline (the most common ELK setup), add an Oodle HTTP output to your Logstash pipeline config. Your Filebeat config stays untouched:

input {
  beats {
    port => 5044
  }
}

output {
  # Keep existing Elasticsearch output
  elasticsearch {
    hosts => ["http://elasticsearch:9200"]
    index => "logs"
  }

  # Add Oodle output
  http {
    url => "https://<OODLE_INSTANCE>-logs.collector.oodle.ai/ingest/v1/logs"
    http_method => "post"
    format => "json_batch"
    content_type => "application/json"
    headers => {
      "X-OODLE-INSTANCE" => "<OODLE_INSTANCE>"
      "X-API-KEY" => "<OODLE_API_KEY>"
    }
  }
}
See docs for Logs ingestion →

Vector

Add an Oodle sink alongside your existing Elasticsearch sink:

sinks:
  # Keep existing Elasticsearch sink
  elasticsearch:
    type: elasticsearch
    inputs:
      - parse_json
    endpoints:
      - http://elasticsearch:9200
    mode: bulk
    bulk:
      index: logs-%Y.%m.%d

  # Add Oodle sink
  oodle:
    type: http
    inputs:
      - parse_json
    uri: "https://<OODLE_INSTANCE>-logs.collector.oodle.ai/ingest/v1/logs"
    encoding:
      codec: json
    compression: gzip
    request:
      headers:
        X-OODLE-INSTANCE: "<OODLE_INSTANCE>"
        X-API-KEY: "<OODLE_API_KEY>"
      retry_attempts: 3
      timeout_secs: 60
See docs for Vector ingestion →

OpenTelemetry Collector

Add Oodle as an additional exporter in your OTel Collector config. Logs flow to both Elasticsearch and Oodle:

exporters:
  # Keep existing Elasticsearch exporter
  elasticsearch:
    endpoints:
      - http://elasticsearch:9200
    logs_index: logs

  # Add Oodle exporter
  otlphttp/oodle:
    logs_endpoint: "https://<OODLE_INSTANCE>-logs.collector.oodle.ai/ingest/otel/v1/logs"
    headers:
      "X-OODLE-INSTANCE": "<OODLE_INSTANCE>"
      "X-API-KEY": "<OODLE_API_KEY>"

service:
  pipelines:
    logs:
      receivers: [otlp]
      processors: [batch]
      exporters: [elasticsearch, otlphttp/oodle]
See docs for OTel ingestion →

Fluent Bit

Add an Oodle HTTP output alongside your existing Elasticsearch output:

# Keep existing Elasticsearch output
[OUTPUT]
    Name                es
    Match               *
    Host                elasticsearch
    Port                9200
    Index               logs
    Suppress_Type_Name  On

# Add Oodle output
[OUTPUT]
    Name                http
    Match               *
    Host                <OODLE_INSTANCE>-logs.collector.oodle.ai
    Port                443
    URI                 /ingest/v1/logs
    Header              X-OODLE-INSTANCE <OODLE_INSTANCE>
    Header              X-API-KEY <OODLE_API_KEY>
    Format              json
    Compress            gzip
    Json_date_key       timestamp
    Json_date_format    iso8601
    TLS                 On
See docs for Fluent Bit ingestion →

1.b Migration

Migrate your existing dashboards, saved searches, and alerts to Oodle.

Dashboards & Saved Searches

Familiar UI: Oodle uses the OpenSearch UI - the same interface your team already knows from Kibana. Your saved queries, visualizations, and dashboard layouts are easily imported into Oodle.

Oodle provides white-glove migration for all Elasticsearch customers. The Oodle team handles the end-to-end migration of your:

  • Kibana dashboards and visualizations
  • Saved searches and queries
  • Index patterns and field mappings

Lucene query syntax works exactly the same in Oodle - no need to rewrite your queries.

Your team won't need retraining. The OpenSearch UI in Oodle looks and feels just like Kibana - same Discover view, same query bar, same visualization types.

Oodle Logs UI Overview

Alerts

What gets imported: Alert rule definitions and thresholds, evaluation intervals, notification policies, severity levels, labels, and annotations.

Most Elasticsearch customers use Grafana for alerting. Oodle migrates all Grafana alerts using the 1-click Grafana import workflow:

  1. Navigate to Settings → Integrations → Grafana in Oodle
  2. Enter your Grafana instance URL and Service Account Token
  3. Run the import - all alert rules, notification policies, and thresholds are migrated

If you are using Prometheus AlertManager:

  1. Navigate to Settings → Integrations → AlertManager Alerts
  2. Share prometheus.yml & alertmanager.yml files
  3. Alternatively, share a Prometheus instance URL & AlertManager URL for Oodle to scrape the alerts

For Kibana Watcher rules or Elastic alerting, the Oodle team provides hands-on migration assistance.

Tip: Configure Oodle alerts to send to a separate Slack channel so you can compare alert behavior side-by-side before switching over.

Set up Log Alerts in Oodle
Step 2

Verification

Effort: 1-2 weeks calendar time, ~1 day per team

In this phase, teams verify that all logs are flowing correctly, dashboards render as expected, and queries return consistent results. Usually, the DevOps/Platform team verifies ingestion and critical dashboards first, then passes verification to individual application teams.

The bulk of the time is coordination and asynchronous review, not hands-on work.

P.S. For larger companies with dozens of teams, it can be done under a month.

Week 1: DevOps & Platform Teams

  • Compare log volume and event counts between Elasticsearch and Oodle
  • Spot-check high-priority dashboards and saved searches
  • Run sample Lucene queries in both systems and compare results
  • Review if alert rules are being evaluated correctly (in test mode)

Week 2: Application Teams

  • Each app team reviews their dashboards and saved searches in Oodle
  • Verify log queries and field mappings
  • Test incident workflows using Oodle's AI debugging
  • Check if alerts fire correctly (in parallel with existing alerting)
Step 3

Turn Off Elasticsearch

Effort: 2 hours for 1 engineer

Once verification is complete and your team is confident, it's time for the final cutover. This is straightforward and reversible if needed.

Prerequisites

  • Alert notifications are working as expected
  • All critical dashboards and saved searches validated in Oodle
  • Ensure sufficient historical data is available by
    • Dual-shipping for longer before cutover.
    • Request Oodle for a back-fill.

1. Activate Oodle Alerts

Change alert status by updating the notifiers and state

2. Deactivate Existing Alerts

In Grafana/AlertManager or Kibana, silence or disable alert rules to prevent alert storms during transition

3. Switch to Single-Write

Remove the Elasticsearch output from your log shippers - keep only the Oodle destination

4. Decommission Elasticsearch

Shut down your Elasticsearch cluster and cancel your Elastic Cloud subscription. No more ILM policies, shard rebalancing, or JVM tuning - ever again.

Experience AI-Native Unified Observability

Everything Unified

  • Logs, Metrics, Traces, and Service Graph in a single interface
  • Familiar OpenSearch UI - no retraining needed
  • All your dashboards and alerts, preserved and working
  • No more managing separate tools for logs, metrics, and traces
  • Automatic correlation across all data types

AI-Native Debugging

  • Ask questions in plain English: "Which services are impacted?"
  • AI surfaces relevant logs, traces, and metric anomalies automatically
  • Proactive anomaly detection catches issues before alerts fire
  • It's like having Cursor for debugging production
Cursor-like Debugging with Oodle Logs

Frequently Asked Questions

Common questions about migrating from Elasticsearch to Oodle AI

We're using Elastic Cloud (managed). Why switch?

Elastic Cloud is hosted, not truly managed - you still manage ILM policies, index templates, shard sizing, and performance tuning. Oodle eliminates all of that with a serverless architecture that has no clusters, no shards, and no ILM. For a detailed breakdown, see our Shared Responsibilities comparison.

We use Lucene query syntax heavily. Will it work in Oodle?

Yes. Oodle's log search supports Lucene query syntax exactly as you know it from Kibana. Your existing queries, saved searches, and filter expressions work without modification.

We're using Filebeat. Can we keep it?

Yes. The most common setup is Filebeat → Logstash → Oodle. Your Filebeat configuration stays untouched - you only add an Oodle HTTP output to your Logstash pipeline. If you're using Filebeat without Logstash, you can switch the output to Oodle's HTTP endpoint or use Vector/OTel as a drop-in shipper. See our ingestion docs.

What about our Kibana dashboards?

Oodle uses the same OpenSearch UI that Kibana is built on, so your team gets the same Discover experience, visualization types, and dashboard layouts. The Oodle team provides white-glove migration assistance to import all your Kibana dashboards and saved searches.

How can I migrate out of Oodle?

It's equally easy to migrate out. Oodle is built entirely on open standards - Lucene for logs, PromQL for metrics, OTel for traces, and standard dashboard formats (OpenSearch JSON, Grafana JSON). The migration-out process mirrors migration in: dual-write, verify, cutover. See the Migration Out section above for full details.

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