ChaosSearch Data Lake Platform
Big data analytics software
Log analysis software
DevSecOps software
Database software
Big data software
- Features
- Ease of use
- Ease of management
- Quality of support
- Affordability
- Market presence
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$2,752 per month
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What is ChaosSearch Data Lake Platform
ChaosSearch Data Lake Platform is a log and event analytics platform that indexes data stored in cloud object storage so teams can search and analyze it without moving it into a separate analytics cluster. It is used by DevOps, security, and engineering teams for observability, troubleshooting, and security investigations across large volumes of machine data. The platform emphasizes decoupled storage and compute, using object storage as the system of record while providing search and aggregation capabilities through its indexing layer. It commonly integrates with log shippers and analytics/visualization tools to support interactive queries and dashboards.
Object storage as system of record
The platform is designed to query and analyze data directly in cloud object storage, which can reduce the need to duplicate large log datasets into separate analytics stores. This architecture can simplify retention strategies by keeping long-term data in the same storage tier used for other data lake workloads. It also aligns with organizations standardizing on cloud object storage for durability and lifecycle management.
Search-oriented log analytics
ChaosSearch focuses on fast search and aggregations over machine data, which fits incident response and troubleshooting workflows. It supports interactive exploration patterns (filtering, faceting, time-bounded queries) that are common in log analysis and security investigations. This makes it suitable for teams that need search-first workflows rather than batch-only analytics.
Integrates with existing pipelines
The product typically fits into existing log collection and data lake pipelines, allowing teams to keep current ingestion tools and storage locations. It can complement BI and analytics layers by providing a search and investigation interface over the same underlying data. This can reduce operational friction compared with adopting a full end-to-end analytics stack replacement.
Not a general-purpose warehouse
While it supports analytics over large datasets, it is not positioned as a full-featured cloud data warehouse with broad SQL semantics, workload management, and extensive governance features. Organizations needing complex transformations, multi-statement SQL workflows, or tight integration with enterprise BI semantic models may still require a separate warehouse or lakehouse layer. This can lead to a multi-system architecture for different query types.
Indexing adds operational considerations
To achieve interactive performance, the platform relies on indexing, which introduces additional configuration and lifecycle management compared with purely scan-based query engines. Teams must plan index coverage, refresh cadence, and storage overhead for indexes. Misaligned indexing strategies can affect query performance and cost predictability.
Ecosystem depth varies by use case
Compared with broad data science and analytics platforms, the surrounding ecosystem for notebooks, ML workflows, and end-to-end data preparation may be less comprehensive. Some advanced use cases may require integrating additional tools for ETL/ELT, feature engineering, or model operations. This increases integration and governance work across the stack.
Plan & Pricing
Pricing model: Usage-based (two primary models: Ingest-based and Worker-based)
Ingest-based (SaaS, predictable volume pricing)
- Price: $0.15 - $0.30 per ingested GB (range shown on official pricing page for 1-year upfront contracts in US/CA regions).
- Additional fixed tenant cost: $3,000 per tenant-region per month.
- Notes/Key features:
- Includes analytics via embedded OpenSearch Dashboards, unlimited users/queries/data retention, SOC2-Type2 security, SSO integration.
- Ingest pricing shown for annual contract paid upfront (US/CA). Consumption-based pricing available; committed-rate on-demand overage charged at 30% above committed rate.
- Regional markups: ~25% extra for EU regions; ~50% for other regions; 30% markup for usage above committed rate across regions.
- Guaranteed savings messaging: “Guaranteed 50% savings for spend above $20k/month or 1TB/day” (as shown on pricing page / comparisons).
Worker-based (compute-hour pricing for flexibility)
- Pricing model: Pay-as-you-go by worker-hour
- Price: $0.20 per worker-hour (US/CA regions) + $1,000 per tenant-region per month.
- Minimum workers: Minimum of 12 workers must be run at all times per tenant-region (per ChaosSearch documentation/blog).
- Notes/Key features:
- Supports Elasticsearch API, OpenSearch Dashboards, Trino API, Superset; unlimited users/queries/data retention.
- Ingest-only effective cost can be very low (example: ingest-only cost as low as $0.005/GB under some worker configurations per official docs/blog) and worker-count depends on ingest/query patterns.
- Worker-hour pricing assumes 1-year contract with upfront payment in US/CA regions; regional markups apply similar to ingest model.
Other pricing details & examples provided on official site
- Examples on official blog/doc pages show how ingest- vs worker-based models compare at different ingest volumes (sample calculations included by ChaosSearch).
- Consumption-based/contracted variations, and Databricks deployment options are referenced; enterprise/custom quotes encouraged via contact/sales.
Free tier/trial: Official site advertises a free trial (trial sign-up page). No evidence of a permanently free plan/tier on official site.
Seller details
ChaosSearch, Inc.
Boston, MA, USA
2017
Private
https://www.chaossearch.io/
https://x.com/chaossearch
https://www.linkedin.com/company/chaossearch/
