Best Self Hosted Alternatives to Axiom

Prometheus is an open-source systems and service monitoring platform built around a time-series database. It collects metrics from instrumented targets, lets you query them with PromQL, and supports alerting based on rules.

Key Features

• Multi-dimensional time series data model using labels for flexible filtering and aggregation
• PromQL query language for ad-hoc analysis, dashboards, and alert conditions
• Pull-based metric scraping over HTTP with support for static configs and service discovery
• Alert rule evaluation with alert generation (commonly paired with Alertmanager)
• Federation support for hierarchical and cross-environment aggregation
• Remote write/read integrations for long-term storage and interoperability

Use Cases

• Monitoring Kubernetes clusters and cloud-native services via dynamic service discovery
• Application and infrastructure telemetry for SRE/DevOps dashboards and alerting
• Central metrics collection for microservices, batch jobs (via push gateway patterns), and exporters

Limitations and Considerations

• Built-in storage is optimized for a single-node TSDB; long-term retention and global scale typically require external remote storage integrations

Prometheus is a strong fit when you want a reliable, standards-based metrics platform with powerful querying and a broad ecosystem of exporters and integrations. It is widely used for cloud-native monitoring and alert-driven operations.

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.

Dozzle is a lightweight web application for live viewing and searching container logs. It focuses on real-time monitoring and does not store log files, making it suitable for quick troubleshooting of running workloads.

Key Features

• Real-time streaming log viewer with a web UI
• Works with Docker and Docker Swarm, with support for Kubernetes environments
• Split-screen view for monitoring multiple container logs at once
• Fuzzy search for container names and filtering using regex
• SQL-based log querying for more structured searches
• Live container stats such as CPU and memory usage
• Optional multi-user authentication, including forward-auth support via a reverse proxy
• Agent mode for monitoring containers across multiple Docker hosts

Use Cases

• Debugging and monitoring logs for containers during development and operations
• Quick investigation of production issues without deploying a full log aggregation stack
• Centralized log viewing for multiple Docker hosts using agent mode

Limitations and Considerations

• Not designed for long-term log retention or offline log search; it is intended for live monitoring only

Dozzle is well-suited for homelabs and teams that want a small, low-overhead log viewer with a clean UI and practical search options. For compliance, retention, and deep historical analysis, it is typically used alongside dedicated log storage and indexing systems.

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.

Parseable is a full-stack observability platform built to ingest, analyze and extract insights from all types of telemetry (MELT) data. It can run locally, in the cloud, or as a managed service, providing a unified way to explore signals across the stack.

Key Features

• Unified signals across MELT data for a single source of truth
• Predictive analytics and anomaly forecasting to anticipate issues
• Natural language and SQL querying across telemetry
• Hybrid execution engine with columnar storage and indexing for fast queries
• Granular access control and federated IAM
• Open standards and vendor-neutral design (OTel, Parquet compatibility)
• Cloud-ready with BYOC options

Use Cases

• Full-stack observability of applications, databases, infrastructure and networks
• AI workloads observability for telemetry from AI models and LLMs
• Product observability to analyze user behavior, feature adoption, and performance

Conclusion Parseable provides predictive observability with a unified data model, enabling faster insights and proactive incident response across the full telemetry stack.