OpenSearch: The Reference Architecture for Sovereign Search Engines & Log Analytics (100% Open Source)

TL;DR

For a long time, Elasticsearch was the undisputed standard for log analytics and full-text search. But then Elastic changed its license, effectively excluding the open-source community to block cloud providers. OpenSearch (managed by the Linux Foundation, initiated by AWS) is the answer: A true, Apache-2.0 licensed fork that keeps the original vision alive. Running OpenSearch in your own cluster not only provides a blazing-fast search engine but also all enterprise features (Security, Alerting, Vector Search) that would be costly with Elastic—while maintaining full data sovereignty.

1. The Architectural Principle: True Open Source vs. License Traps

The most significant architectural difference between Elastic and OpenSearch today lies in the DNA of the software.

• The Elastic Lock-in: Elasticsearch now uses a proprietary license (SSPL/Elastic License). Many critical functions (like Single Sign-On, granular access controls, or Machine Learning) are hidden behind an expensive paywall (“Elastic Enterprise”).
• The OpenSearch Promise: OpenSearch is 100% open source (Apache 2.0). There are no premium tiers. Everything the engine can do is available to you for free. You build your architecture on an open foundation that no one can take away from you.

2. Core Feature: Enterprise Security & Alerting “Out of the Box”

What good is a massive pool of logs or customer data if everyone can access it?

In the open-source world of Elastic, security was long a pain point. OpenSearch integrates these features natively.

• Granular RBAC: You can control who can see what at the field or document level. Team A only sees the logs from Namespace A; the HR department does not see sensitive passwords in the raw data.
• Single Sign-On (SSO): OpenSearch Dashboards (the alternative to Kibana) can be seamlessly integrated with OIDC/SAML (e.g., Keycloak, Azure AD).
• Integrated Alerting: You don’t need to cobble together external tools. OpenSearch can define monitors (“If > 50 errors in 5 minutes, send Slack message”).

3. AI-Readiness: Vector Search (k-NN) for Modern RAG Systems

Search engines used to be primarily for keyword matching (“Find the word ‘invoice’”). Today, it’s about semantic search (“Find documents related to payment requests”).

• k-Nearest Neighbor (k-NN): OpenSearch has integrated an extremely performant engine for vector search.
• The RAG Base: If you’re building your own AI applications (Retrieval-Augmented Generation, e.g., in combination with Ollama), OpenSearch serves as your sovereign vector database. It stores the embeddings of your internal documents and quickly provides the relevant context to the AI—without having to rent expensive SaaS vector databases (like Pinecone).

4. Operational Models Compared: AWS OpenSearch vs. ayedo Managed OpenSearch

Here, it is decided whether you pay for infrastructure or accept a cloud premium for a logo.

Scenario A: AWS Managed OpenSearch (The Convenient Premium)

AWS may have initiated OpenSearch, but they charge royally for the managed service.

• Managed Premium: You pay significantly more for the underlying EC2 instances than if you rented them “bare.”
• Limited Control: You have no direct access to the file system of the nodes, cannot install custom plugins that AWS has not approved, and are bound to AWS maintenance windows.
• Egress Costs: If you pump terabytes of logs from on-premise or other clouds into AWS OpenSearch, traffic costs hit mercilessly.

Scenario B: OpenSearch with Managed Kubernetes from ayedo

In the ayedo App Catalog, OpenSearch runs highly available in your own cluster.

• Hardware Efficiency: OpenSearch is memory-hungry. In the ayedo stack, we allocate dedicated node pools (Hot/Warm/Cold Architecture) and use local NVMe SSDs to maximize I/O performance without paying for expensive AWS EBS volumes.
• Full Control: You are the cluster admin. Need an exotic NLP plugin? You can install it.
• Cost Scalability: You only pay for the bare infrastructure. Whether you index 100 GB or 10 TB—the software license costs remain at 0 euros.

Technical Comparison of Operational Models

FAQ: OpenSearch & Analytics Strategy

Is OpenSearch compatible with Elasticsearch?

Yes, to a very large extent. OpenSearch is a fork of Elasticsearch 7.10. Most REST APIs, index structures, and ingestion tools (like Logstash or Filebeat/Fluentd) continue to work seamlessly. Only with extremely specific, newer Elastic 8.x features are there deviations, as the projects evolve independently.

What about Kibana?

Kibana is the frontend for Elasticsearch (and has also become proprietary). OpenSearch provides OpenSearch Dashboards. It looks almost identical, operates the same, and supports all your familiar graphs, dashboards, and discover views.

When do I need OpenSearch instead of Loki?

This is an important distinction in the ayedo catalog! If you only search for errors in logs (“Grep in the Cloud”) and primarily monitor infrastructure, Loki is cheaper and more efficient. But if you need full-text search in application data (e.g., a product search for your webshop), perform complex aggregations, or do vector search for AI features, OpenSearch is the only right choice.

How does it work with backups?

In the ayedo stack, we configure automatic snapshots. OpenSearch regularly backs up its indices to S3-compatible object storage (like AWS S3 or self-hosted MinIO). In the event of a total failure, the cluster can be precisely restored from these snapshots.

Conclusion

Data is your company’s most valuable asset, and the technology to search it should not be hidden behind proprietary paywalls or cloud premiums. Elastic’s license change was a wake-up call for the industry. OpenSearch is the sovereign answer. With the ayedo Managed Stack, you get an enterprise-grade search engine and analytics platform that tames massive data volumes, supports your AI visions, and remains 100% under your control.