Best Self Hosted Alternatives to OpenObserve Cloud

Grafana Loki is a horizontally scalable, highly available log aggregation system inspired by Prometheus. It stores logs efficiently by indexing only metadata labels for each log stream, rather than performing full-text indexing.

Key Features

• Label-based log indexing and querying aligned with Prometheus-style labels
• Horizontally scalable architectures (single binary or microservices) with multi-tenancy support
• Cost-efficient storage by keeping logs compressed and indexing only metadata
• Native integration with Grafana for exploration, dashboards, and correlation with metrics
• Multiple ingestion options via agents and clients (including Grafana Alloy and legacy Promtail)

Use Cases

• Centralized aggregation of Kubernetes and container logs with label-based filtering
• Incident investigation by correlating metrics and logs using shared labels
• Multi-team or multi-environment log collection with tenant isolation

Limitations and Considerations

• Not designed for full-text indexing; queries are primarily optimized around labels and structured metadata

Loki is a strong fit when you want an operationally simpler, Prometheus-like approach to logs with efficient storage and fast label-based queries. It is commonly deployed as part of a Grafana-centric observability stack for monitoring and troubleshooting.

OpenSearch is an Apache 2.0-licensed, community-driven distributed search and analytics engine designed for indexing and querying large volumes of data. It provides a RESTful API and is commonly used as the core search backend for applications and as a foundation for log and event analytics.

Key Features

• Distributed indexing and search for horizontal scalability and high availability
• RESTful API for indexing, querying, and cluster operations
• Full-text search and relevance scoring for unstructured and semi-structured data
• Aggregations for analytical queries over large datasets
• Extensible architecture with plugins for additional capabilities

Use Cases

• Powering application search for websites, product catalogs, and documentation
• Centralized log search and analytics for infrastructure and applications
• Building analytics experiences over event, text, and time-based datasets

Limitations and Considerations

• Operational complexity can be significant for large clusters (sizing, tuning, shard management)
• Query performance and cost depend heavily on index design and workload patterns

OpenSearch is a strong fit when you need scalable search and analytics with an open ecosystem and a well-known REST interface. It can serve as a primary search backend or as a core component in broader observability and analytics pipelines.

Quickwit is a cloud-native open-source search engine built for observability data, including logs and traces. It runs compute separately from storage and supports querying data directly on object storage for scalable, cost-efficient search.

Key Features

• Full-text search and aggregation queries
• Elasticsearch-compatible API, use Quickwit with Elasticsearch or OpenSearch clients
• Jaeger-native and OTEL-native support for logs and traces
• Schemaless indexing and analytics
• Sub-second search on cloud storage (e.g., S3, Azure Blob, Google Cloud Storage)
• Decoupled compute and storage with stateless indexers & searchers
• Grafana data source
• Kubernetes-ready with a Helm chart
• RESTful API

Use Cases

• Log management across large-scale deployments
• Distributed tracing analytics for microservices
• Real-time search and exploration of observability data to troubleshoot incidents

Conclusion

Quickwit is a scalable, open-source solution designed to search and analyze vast observability datasets directly on cloud storage. Its architecture emphasizes decoupled compute/storage, compatibility with popular tooling, and ease of deployment on Kubernetes.