ChaosSearch
Live analytics on streaming data at scale
About ChaosSearch
ChaosSearch is a fully managed log analytics and data lake platform that turns your existing cloud object storage (Amazon S3, Google Cloud Storage) into a hot, searchable analytical data lake. It offers Elasticsearch API and Kibana compatibility, SQL access, and unlimited retention without re-indexing, sharding, or data movement.
Pros & Cons
Pros
- Major cost savings (50-80%) vs hosting your own ELK or using Elastic Cloud
- Data stays in your own S3 or GCS bucket, no migration required
- Quick setup since data is already in cloud object storage
- Unlimited retention with all data remaining hot and queryable
- Strong customer support and account management reported by users
- Drop-in Elasticsearch and Kibana compatibility eases migration
Cons
- User interface and search experience can feel less polished than native Kibana
- Query performance may be slower than raw Elasticsearch for some workloads
- Pricing is custom and not transparent on the website
- Best suited for AWS or GCP users; less relevant for Azure-only stacks
- Learning curve for teams new to Elasticsearch query language
Key Features
Elasticsearch API Compatibility
Query data using the familiar Elasticsearch API and visualize with Kibana, no code rewrites needed.
Cloud Object Storage Native
Indexes data directly in Amazon S3 or Google Cloud Storage so customers retain ownership and avoid data movement.
Unlimited Data Retention
Keep all historical data hot and queryable without tiered storage or rehydration steps.
SQL and Search APIs
Run SQL, full-text search, and ML queries against the same indexed data through open APIs.
Auto Schema Detection
Automatically detects and maintains schemas as new data arrives, eliminating manual mapping work.
Virtual Views
Create governed virtual views with role-based access controls to expose data to different teams without duplication.
Fully Managed Service
Eliminates operational toil like sharding, re-indexing, load balancing, and capacity management.
Databricks Lakehouse Integration
Deploy as SaaS or directly within the Databricks Lakehouse for unified analytics.
Pricing
- $3,000 per tenant-region base
- From $0.21/GB at 10,000 GB/day commit
- Pay for data ingested
- Predictable for steady ingest volumes
- $1,000 per tenant base
- $0.20 per worker-hour (US regions)
- Minimum 12 workers per region
- Better for variable query workloads
- Volume discounts
- Multi-region deployments
- Dedicated support and SLA
- Databricks Lakehouse deployment option
Best For
Cost-Efficient Log Analytics
Replace expensive hosted ELK or Splunk deployments while keeping all logs queryable in S3.
Security and SIEM Analytics
Run real-time security investigations across long-retention log data without rehydration.
Cloud Observability at Scale
Centralize application, infrastructure, and audit logs from large cloud environments.
Data Lake Search
Make existing S3 or GCS data lakes searchable for analysts using SQL and search APIs.
Compliance and Long-Term Retention
Keep years of log data hot for compliance and forensic analysis without storage tiering.
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