22 Best Open Source Splunk Alternatives
A curated collection of the 22 best open source and self-hosted alternatives to Splunk in 2026.
Looking for a free, privacy-friendly, or self-hosted replacement for Splunk in 2026? The top open-source alternative is Apache Superset — Open-source data visualization and exploration platform.
Other highly rated open-source Splunk alternatives include Grafana, Prometheus and SigNoz.
Open-source data visualization and exploration platform
Apache Superset™ is an open-source modern data exploration and visualization platform designed for the cloud-native era. It enables users to create interactive dashboards and charts from any SQL database with a powerful no-code interface or advanced SQL IDE. - 40+ pre-installed visualization types including charts, graphs, and geospatial visualizations - No-code visualization builder for quick chart creation without SQL knowledge - SQL Lab for advanced users to write custom queries, use Jinja templating, and explore database metadata - Scalable architecture that integrates with modern cloud-native databases and engines at petabyte scale - Plug-in architecture for building custom visualizations and extending functionality - Self-serve analytics allowing business users to explore data independently - Dataset management supporting both physical and virtual datasets with unified metric definitions - Security features including role-based access control and database connection management Superset is built on modern web technologies and can be deployed on any infrastructure. It supports containerized deployments (Docker, Kubernetes) and integrates with existing data stacks without requiring additional ingestion layers. The project is Apache 2.0 licensed and welcomes community contributions.
Grafana unifies metrics, logs, traces, profiles, and business data into one OpenTelemetry-native platform with AI-assisted root cause analysis and adaptive cost controls.
Grafana unifies metrics, logs, traces, profiles, and business data into one OpenTelemetry-native platform with AI-assisted root cause analysis and adaptive cost controls.
Open-source monitoring & time series database
Prometheus is an open-source monitoring system and time series database designed for cloud-native environments. It provides a flexible dimensional data model, a powerful PromQL query language, and efficient alerting mechanisms. - Dimensional data model: Identifies time series by metric names and key-value pairs for flexible data organization. - PromQL query language: Enables powerful querying, correlation, and transformation of time series data for visualizations and alerts. - Precise alerting: Uses Alertmanager to handle notifications and silencing based on PromQL rules. - Simple operation: Operates independently with local storage, deployable as statically linked binaries across environments. - Instrumentation libraries: Offers official and community-contributed libraries for metrics instrumentation in major languages. - Ubiquitous integrations: Includes hundreds of official and community integrations for extracting metrics from existing systems. - Cloud-native integration: Works seamlessly with Kubernetes and other cloud/container managers for continuous service discovery and monitoring. - Open governance: A CNCF graduated project with 100% open-source components under the Apache 2 License. Prometheus is 100% open-source and self-hostable, with all components available under the Apache 2 License. It is developed in Go and designed for easy deployment across various environments, including Kubernetes and cloud infrastructures. The system operates independently with local storage, ensuring minimal external dependencies.
Open-source Datadog alternative for full-stack observability
SigNoz is a modern observability platform designed to simplify monitoring, logging, and tracing for applications and AI agents. Built on open standards like OpenTelemetry, it consolidates APM, logs, metrics, exceptions, and AI observability into a single tool. - Full-Stack Observability: Monitor traces, metrics, logs, and exceptions in one platform - OpenTelemetry Native: Built on OpenTelemetry standards for vendor-neutral instrumentation - AI Observability: Specialized tools for monitoring AI agents and applications - Real-Time Alerts: Configurable alerting for anomalies and critical events - Unified Dashboard: Single interface for all observability data - Usage-Based Pricing: Simple, predictable pricing model - Self-Hosting: Deploy on your own infrastructure with Self-Hosted SigNoz - Agent-Native: Optimized for monitoring AI agents and their workflows SigNoz offers both cloud-hosted and self-hosted options. The self-hosted version can be deployed using Docker, Kubernetes, or Helm charts. It supports various data sources and integrates seamlessly with existing monitoring setups. The platform is designed for scalability and can handle high-volume telemetry data from distributed systems.
AI-powered security testing for continuously shipped software
Neo applies reasoning, memory, and real execution across the delivery lifecycle — so security keeps up with continuously shipped software. From the creators of Nuclei, trusted by over 100,000 security professionals. - Continuous Testing: Maps and tests all exposed assets (domains, APIs, endpoints, ports, cloud services) in real-time as infrastructure changes - Parallel Layer Testing: Specialist workflows test applications, APIs, code, and infrastructure simultaneously, drawing context from code, tickets, and past findings - Verified Findings: Every issue is reproduced before reporting, with exploitable vulnerabilities routed directly to engineering teams - Memory & Learning: Each run deepens the map of your codebase, dismissed false positives stay dismissed, and new community detections are integrated daily - Community-Powered Coverage: Tracks new CVEs, CISA KEVs, public exploits, and internet exposure for fastest detection of emerging threats - Benchmark Accuracy: Achieves 3x more critical findings with 5x fewer false positives compared to traditional scanners - Triage Efficiency: Cuts triage time by 95% and enables 3x faster remediation - Real-Time Discovery: Provides continuous monitoring of every domain, API, and endpoint - Open Source Foundation: Built on Nuclei and other open-source tools from ProjectDiscovery - Cloud & Self-Hosted Options: Available via ProjectDiscovery Cloud or self-hosted deployments - Integration Ready: Connects with existing security workflows, ticketing systems, and development pipelines - Documentation: Comprehensive guides available at docs.neo.projectdiscovery.io Neo is designed for offensive security teams running security as infrastructure, with AI specifically engineered for security workflows rather than general-purpose applications.
Open source data visualization and dashboarding tool
Redash is a powerful open-source platform for connecting to databases and APIs, writing SQL queries, and creating interactive dashboards. It helps teams transform raw data into actionable insights through collaborative visualization and sharing. - Multi-source data connections: Supports PostgreSQL, MySQL, Redshift, BigQuery, MongoDB, and more - Collaborative SQL editor: Browse schemas, create reusable snippets, and write queries with team collaboration - Drag-and-drop dashboards: Visualize data with customizable charts and graphs - Scheduled refreshes: Automatically update dashboards with fresh data - Alerts system: Get notified when data meets specific conditions - User management: Control access and permissions for teams - API access: Integrate with other tools and systems - Open source: Self-hostable with no vendor lock-in Redash is fully open-source and can be self-hosted on your own infrastructure. The project provides comprehensive setup guides for Docker, Kubernetes, and bare-metal installations. It's built with Python, JavaScript, and PostgreSQL, making it suitable for most modern environments.
Open source observability platform for AI era
OpenObserve is a high-performance, unified observability platform designed for the AI era. It delivers 140x lower storage costs while maintaining enterprise-grade reliability and scalability. - Unified Observability: Monitor logs, metrics, traces, and AI workloads in a single platform - Petabyte-Scale Scalability: Handle massive data volumes efficiently - 90% Cost Savings: Significantly reduce infrastructure and storage expenses - AI-Powered Features: Includes AI SRE and AI assistant capabilities - Kubernetes Monitoring: Native support for containerized environments - LLM Observability: Specialized monitoring for large language models - Visualization & Dashboards: Advanced data visualization tools - Incident Management: Built-in incident tracking and alerting - Open Source Core: Fully open source with AGPL-3.0 license - Self-Hosting: Deployable via Docker or Kubernetes OpenObserve can be self-hosted with a single command: bash docker run \ -p 5080:5080 \ -e ZOROOTUSEREMAIL="[email protected]" \ -e ZOROOTUSERPASSWORD="Complexpass#123" \ o2cr.ai/openobserve/openobserve:latest The platform supports both cloud and on-premises deployments, with enterprise-grade security and compliance features. It's trusted by over 9,000 companies including Fortune 500 giants and innovative startups.
Open source log collection and processing pipeline
Logstash is a flexible, open-source data processing pipeline that integrates data from multiple sources, formats, and locations in real-time. As part of the Elastic Stack, it enables centralized log collection, parsing, and enrichment before indexing into Elasticsearch for storage and analysis. - Multi-source data ingestion: Collect logs from files, syslog, HTTP, databases, and cloud services - Real-time processing: Parse and transform logs as they arrive with customizable filters - Extensible plugin ecosystem: Over 200 plugins for input, filter, and output operations - Data enrichment: Add context to logs using geo-IP, user agent parsing, and custom lookups - Scalable architecture: Handle high-volume log streams with distributed processing - Integration with Elastic Stack: Seamless connection to Elasticsearch and Kibana for visualization - Flexible deployment: Run as standalone, in containers, or as part of Kubernetes clusters - Security features: TLS encryption, authentication, and role-based access control Logstash is open-source software that can be self-hosted on any infrastructure. It supports: - Standalone deployment: Single-node setup for development or small-scale use - Containerized deployment: Docker images available for easy container orchestration - Kubernetes integration: Helm charts for deploying in Kubernetes environments - Cloud deployment: Available as part of Elastic Cloud for managed hosting The software is written in JRuby and requires Java runtime environment. Configuration is done through a declarative pipeline definition file.
Open source observability platform for logs, traces and errors
HyperDX is an open source observability platform designed to help engineering teams resolve production issues faster. It unifies session replays, logs, traces, metrics, and errors into a single interface, eliminating the need for multiple tools. - End-to-End Tracing: Automatically trace requests from browser to backend servers and async workers - Blazing Fast Search: Search terabytes of events in seconds using ClickHouse - Intuitive Search Syntax: Simple full-text search without complex syntax requirements - Automatic Pattern Clustering: Aggregate log patterns and condense billions of events into distinctive clusters - Correlated Session Replays: Link session replays with backend logs and traces automatically - Visualization Tools: Build and share intuitive charts for logs, metrics, and traces - Automatic Instrumentation: Trace logs, API requests, DB queries with minimal code - Alerts & Notifications: Get notified via Slack, Email, or PagerDuty when issues occur - Intercom Integration: Jump directly from Intercom to user sessions and logs - Live Tail: View and search log streams in real-time - Native JSON Parsing: Search and graph structured JSON logs without configuration - Vendor Agnostic: Powered by OpenTelemetry for no vendor lock-in - Agent-Free Install: No additional agents, sidecars, or containers required HyperDX is built on ClickHouse and OpenTelemetry, making it highly scalable and compatible with various deployment environments. The platform offers both cloud-hosted and self-hosted options, with automatic instrumentation supporting multiple languages including Node.js, Python, Java, Go, Elixir, and React Native. Deployment is supported on Kubernetes, AWS EC2, Vercel, Fly.io, Heroku, and Cloudflare.
Open-source observability for LLMs with OpenTelemetry
OpenLLMetry is an open-source observability platform designed specifically for Large Language Model (LLM) applications. It provides comprehensive tracking, monitoring, and debugging capabilities using the OpenTelemetry standard, enabling developers to gain deep insights into their LLM workflows. - LLM-Specific Instrumentation: Automatically captures and traces LLM interactions, including prompts, completions, and tool usage - OpenTelemetry Integration: Seamlessly integrates with existing OpenTelemetry-based observability stacks - Multi-Provider Support: Works with major LLM providers including OpenAI, Anthropic, Mistral AI, Cohere, and more - Multi-Platform Compatibility: Supports Python and TypeScript implementations - Trace Visualization: Provides detailed trace views for debugging complex LLM workflows - Performance Monitoring: Tracks latency, token usage, and error rates across LLM calls - Cost Optimization: Helps identify expensive LLM operations and optimize usage - Extensible Architecture: Modular design allows for custom instrumentation and extensions OpenLLMetry is open-source software that can be self-hosted. The project provides comprehensive documentation for deployment and configuration. It requires an OpenTelemetry-compatible backend for storing and visualizing traces, with support for popular platforms like Instana, Dynatrace, Grafana Tempo, Honeycomb, New Relic, Datadog, and Splunk.
eBPF-powered AI observability platform
Coroot is an eBPF-powered and AI-guided observability platform designed to provide full-stack visibility with minimal setup. It automatically collects metrics, logs, traces, and profiles across your entire infrastructure—from applications and databases to cloud and network layers—without requiring any code changes. - AI-Powered Root Cause Analysis: Instantly identifies and explains issues using complete telemetry data, acting like an experienced engineer to trace dependencies and suggest fixes. - Zero-Code Setup: Deploys with no instrumentation or configuration changes, leveraging eBPF for automatic data collection. - Full-Stack Visibility: Monitors applications, databases (PostgreSQL, MongoDB), Kubernetes, cloud services, and network components in a unified view. - Continuous Profiling: Captures performance profiles to identify bottlenecks and optimize resource usage. - Service Mapping: Visualizes service dependencies and interactions to simplify troubleshooting. - OpenTelemetry Integration: Supports OpenTelemetry standards for seamless data ingestion and compatibility. - Alerting & Insights: Converts raw telemetry into actionable alerts and recommendations. - Enterprise Edition: Offers advanced features for large-scale deployments, including enhanced security and scalability. Coroot is open-source and can be self-hosted using Docker or Kubernetes. The platform is designed for ease of deployment, with pre-built images and Helm charts available for quick setup. It supports integration with existing observability stacks and adheres to open standards like OpenTelemetry. For more details, visit the documentation or explore the GitHub repository.
Cloud-native observability database unifying metrics, logs, and traces with sub-second queries, 50x cost reduction, and seamless OpenTelemetry integration.
Cloud-native observability database unifying metrics, logs, and traces with sub-second queries, 50x cost reduction, and seamless OpenTelemetry integration.
Open-source LLM observability and monitoring platform
Helicone is the LLMOps platform powering the fastest-growing AI companies. It enables developers to route, debug, and analyze their AI applications with real-time observability, monitoring, and analytics. - LLM Observability: Real-time monitoring and debugging of LLM interactions. - AI Gateway: Route requests to multiple LLM providers seamlessly. - Analytics Dashboard: Track performance, usage, and costs across models. - Prompt Management: Store, version, and optimize AI prompts. - Rate Limiting & Alerts: Prevent abuse and get notified of anomalies. - Multi-Provider Support: Integrate with OpenAI, Anthropic, Azure, and more. - HQL (Helicone Query Language): Query and analyze AI interactions efficiently. - Datasets & Playground: Test and refine AI models with interactive tools. Helicone is open-source and can be self-hosted. It supports integration with major LLM providers and offers a cloud-based solution for ease of use. The platform is designed for scalability and reliability, making it suitable for both small projects and enterprise-level AI applications.
Open-source OLAP database for real-time analytics
Apache Pinot™ is an open-source distributed OLAP database designed for real-time analytics, serving both user-facing applications and AI agents with sub-second query latency on fresh data at petabyte scale. - Sub-Second Queries: Filter and aggregate petabyte datasets with P90 latencies in the tens of milliseconds, enabling interactive live results in UIs. - High Concurrency: Serve hundreds of thousands of concurrent queries per second for applications and AI agents. - Real-Time Streaming Ingestion: Ingest data in real-time from Apache Kafka, Apache Pulsar, AWS Kinesis, or batch sources like Hadoop, Spark, and AWS S3. - Upserts Support: Ingest the same record multiple times while only seeing the latest value at query time (production-tested since version 0.6). - Versatile Joins: Perform arbitrary fact/dimension and fact/fact joins on petabyte-scale datasets. - Rich Indexing Options: Pluggable indexes including timestamp, inverted, StarTree, Bloom filter, and range indexes for optimized query performance. - Multitenant Isolation: Enable customers to explore their own data in real time without pre-aggregation. - Embedded Analytics: Ship interactive dashboards inside products with support for 100K+ concurrent users querying live data. Apache Pinot is an open-source project hosted by the Apache Software Foundation. It can be self-hosted on any infrastructure, including cloud, on-premises, or hybrid environments. The project provides comprehensive documentation for deployment, scaling, and management, with support for containerized deployments via Docker and Kubernetes. Primary Use Cases: - User-facing analytics (dashboards, metrics APIs, customer-facing reports) - AI agent-facing analytics (RAG systems, decision engines, agentic observability) - Real-time decision-making platforms requiring sub-second latency Supported Data Sources: - Streaming: Kafka, Pulsar, AWS Kinesis - Batch: Hadoop, Spark, AWS S3, local files Deployment Options: - Self-hosted (bare metal, VMs, containers) - Cloud-agnostic (AWS, GCP, Azure, etc.) - Kubernetes-ready with Helm charts available Performance Characteristics: - Sub-second query latency on petabyte-scale data - Horizontal scalability through distributed architecture - High availability with automatic failover and recovery
AI observability platform for LLMs and apps
Pydantic Logfire is a production-grade observability platform designed specifically for AI applications, agents, APIs, and databases. Built on OpenTelemetry, it provides comprehensive monitoring across your entire stack, not just LLM calls. - Whole-App Tracing: Trace model calls, agent steps, API requests, and database queries in a single timeline - Multi-Language Support: SDKs for Python, JavaScript/TypeScript, and Rust with OpenTelemetry compatibility for other languages - Evals & Scoring: Built-in evaluation framework that runs alongside your application logic - Cost Tracking: Monitor and optimize AI application costs in real-time - PII Scrubbing: Automatic sensitive data redaction in SDKs - Postgres-Flavored SQL: Query spans directly using familiar SQL syntax - Live Debugging: Real-time monitoring of production services with error rates, latency, and traces - Self-Hosting Option: Deployable on your own infrastructure - OpenTelemetry Foundation: Built on industry-standard observability protocols - Self-Hosted Deployment: Available for on-premises or private cloud deployments - Compliance: SOC 2 Type II, HIPAA, and EU data residency compliant - Integration: Seamless integration with existing monitoring and logging systems
Open source SOAR for AI-native security teams
Tracecat is an open source Security Orchestration, Automation, and Response (SOAR) platform designed for AI-native security teams. It enables organizations to build custom agents, automate repetitive tasks, and manage security cases with workflows they fully control. - Custom AI Agents: Build and deploy security agents tailored to your needs, from phishing triage to vulnerability response. - Workflow Automation: Create automated runbooks that connect tools like Wiz, CrowdStrike Falcon, Okta, and Slack. - Case Management: Centralize security investigations with structured cases and approval workflows. - Model Context Protocol (MCP): Integrate with security tools via MCP for real-time data enrichment and actions. - Self-Hosting: Deploy Tracecat on your own infrastructure for full data ownership and compliance. - Developer-Friendly: Built for security teams that want to code their own solutions rather than rely on rigid, out-of-the-box tools. Tracecat is open source and can be self-hosted using Docker or Kubernetes. The platform supports MCP for tool integration and provides extensive documentation for deployment and customization. Ideal for teams prioritizing data privacy and control over their security automation stack.
OpenTelemetry-native observability platform
Uptrace is an OpenTelemetry-native observability platform that unifies traces, metrics, and logs in a single platform. Built on OpenTelemetry and ClickHouse, it processes billions of spans at a fraction of the cost of traditional observability tools like Datadog or New Relic. - All-in-one observability: Unified platform for traces, metrics, and logs with no need to stitch data together manually. - OpenTelemetry-native: Ingest data from OpenTelemetry, Prometheus, Vector, FluentBit, CloudWatch, and more. - Cost-effective pricing: Pay per GB ingested and active timeseries with no per-seat fees or hidden charges. - Self-hosting: Download from GitHub and own your data completely with no feature limits. - APM & Infrastructure Monitoring: Service graphs, RED metrics, latency percentiles, and top errors with infrastructure monitoring capabilities. - Alerting & MCP: Built-in alerting and MCP (Model Context Protocol) support for AI-driven observability. - Predictable pricing: Set monthly caps to avoid billing surprises and budget effectively. - Compliance-ready: Offers on-premise installations for organizations with strict compliance requirements. Uptrace is built on OpenTelemetry and ClickHouse, offering both cloud and self-hosted options. The self-hosted version is available as a free download from GitHub, allowing users to deploy on their own infrastructure. Uptrace Cloud provides a fully managed experience with enterprise-grade support and compliance options. The platform is designed to scale from small applications to enterprise-level deployments, handling high-volume data ingestion efficiently.
Unified IT observability for hybrid and cloud environments
Checkmk is a unified IT observability platform designed to monitor hybrid and cloud-native environments. It provides full-stack visibility across servers, networks, containers, applications, and more, bridging the gap between legacy systems and modern infrastructure. Trusted by industry leaders, Checkmk delivers scalable, secure, and high-performance monitoring solutions. - Unified Monitoring: Full-stack observability for hybrid and cloud-native IT environments - Multi-Platform Support: Monitor servers, networks, databases, cloud services, Kubernetes, storage, IoT, and SNMP devices - Scalable Architecture: Designed for high performance and enterprise-grade scalability - Multiple Editions: Available as SaaS (Checkmk Cloud), self-hosted (Ultimate, Pro, Community), and virtual/physical appliances - Extensive Integrations: 2,000+ integrations with third-party systems and applications - Automated Discovery: Intelligent service and host discovery with minimal configuration - Alerting & Notifications: Advanced alert management with escalation policies and integrations - Reporting & Dashboards: Customizable dashboards and comprehensive reporting capabilities - Enterprise Support: Professional services, training, and 24/7 support options Checkmk offers multiple deployment options: - SaaS: Checkmk Cloud for fully managed monitoring - Self-Hosted: Checkmk Ultimate, Pro, and Community editions - Appliances: Virtual or physical appliance options for simplified deployment - Open Source: Checkmk Community edition available under GPL license The platform supports Linux-based environments and provides comprehensive documentation for self-hosting configurations. Enterprise features include advanced security, compliance tools, and dedicated support. Explore Editions | Download | Documentation
Open-source observability for traces, logs & metrics
Maple is an open-source observability platform designed for modern applications. Built on OpenTelemetry and powered by ClickHouse, it enables teams to collect, visualize, and analyze distributed traces, logs, and metrics with sub-second query performance. - Unified Observability: Collect and correlate traces, logs, and metrics from all services in one platform - AI-Powered Diagnostics: Leverage AI agents to automatically analyze incidents and propose fixes - Real-Time Incident Response: Follow requests end-to-end with complete context and correlated data - Service Catalog & Maps: Visualize live request flows and service dependencies - Error Tracking: Group errors by type with trends, affected services, and sample traces - Metrics & Dashboards: Customizable dashboards for request rate, error rate, and latency percentiles - Alerting with Context: Receive alerts with attached traces, logs, and relevant context - MCP Server Integration: First-class MCP support for AI agents to interact with your observability data Maple is open-source and can be self-hosted. The platform is built on OpenTelemetry standards and uses ClickHouse for high-performance data storage and querying. Local deployment is available as a single binary for easy setup and management.
Unified platform for logs, metrics, traces and profiles with native compatibility for popular tools like OpenTelemetry, Prometheus, and Loki. No data silos, no usage limits.
Unified platform for logs, metrics, traces and profiles with native compatibility for popular tools like OpenTelemetry, Prometheus, and Loki. No data silos, no usage limits.
Distributed search & analytics engine
Elasticsearch is a distributed, RESTful search and analytics engine designed for speed, horizontal scalability, reliability, and easy management. It enables fast and efficient data search, analysis, and visualization across large datasets. - Distributed & Scalable: Horizontally scalable architecture for handling petabytes of data. - Full-Text Search: Advanced search capabilities with relevance scoring and fuzzy matching. - Vector Database: Efficiently store and search vector embeddings for AI applications. - Real-Time Analytics: Process and analyze data in real-time with low latency. - Security & Observability: Built-in security features, log monitoring, and infrastructure insights. - AI Integration: Native support for AI-driven workflows, LLM observability, and automated threat detection. - Multi-Deployment Options: Available as self-managed, hosted cloud, or serverless. - Kibana Integration: Visualize and explore data with interactive dashboards and reports. - Self-Managed: Deploy on local servers, Kubernetes, or cloud infrastructure. - Elastic Cloud: Fully managed cloud service with serverless and hosted options. - Open Source: Core engine is open-source under the Elastic License.
Cloud-native log management and SIEM platform
Sumo Logic is a cloud-native platform providing comprehensive log management, monitoring, and security operations (SecOps) solutions. Designed for modern cloud and SaaS environments, it delivers real-time insights through advanced log analytics, infrastructure monitoring, and AI-powered threat detection. - Log Management & Analytics: Centralized collection, storage, and analysis of logs from multiple sources with powerful search and visualization capabilities. - Infrastructure Monitoring: Real-time monitoring of cloud infrastructure, applications, and Kubernetes clusters to detect and resolve issues quickly. - Security Information and Event Management (SIEM): AI-driven threat detection, compliance monitoring, and security analytics to identify and respond to cyber threats. - Multi-Cloud Support: Native integrations with AWS, Google Cloud Platform, and Microsoft Azure for seamless cloud operations. - AI/ML-Powered Insights: Proprietary algorithms and generative AI for predictive analytics, anomaly detection, and automated response. - Compliance & Security Certifications: Trusted and certified for security standards including SOC 2, ISO 27001, and FedRAMP. - OpenTelemetry Integration: Native support for OpenTelemetry for standardized observability data collection. - Scalable & Enterprise-Grade: Designed to handle high-volume data ingestion and processing for large-scale deployments. Sumo Logic is a fully managed SaaS platform and does not offer self-hosting options. The service is hosted on cloud infrastructure with global data centers to ensure high availability and compliance with regional data residency requirements.
About Splunk
Splunk is a leading enterprise platform designed to bolster digital resilience by enabling organizations to prevent major issues, absorb shocks, and accelerate digital transformation. The platform provides unified security and observability capabilities, integrating AI-driven tools to enhance operational efficiency and threat detection.
Key Features of Splunk
- Splunk Platform: Centralized data platform for trusted AI and agentic operations.
- Enterprise Security: AI-powered SecOps platform for unified Threat Detection, Investigation, and Response (TDIR).
- Observability Cloud: Real-time visibility across any environment with application performance, infrastructure monitoring, and digital experience analytics.
- Artificial Intelligence: Agentic operations and AI toolkit for building, testing, and deploying custom AI models.
- Data Management: Optimize pipelines and control costs with advanced data handling capabilities.
- SIEM & SOAR: Detect and stop threats while accelerating incident response through automation.
- Compliance & Auditing: Easy auditing and reporting for regulatory compliance.
- Advanced Threat Detection: Uncover insider threats and sophisticated attacks with AI-driven analytics.
Tech / Self-Hosting
Splunk primarily operates as a cloud-based SaaS platform with enterprise-grade security and compliance features. While Splunk offers on-premises deployment options, it is not traditionally self-hosted in the open-source sense. The platform is designed for large-scale enterprise use with robust data ingestion, AI integration, and real-time analytics capabilities.