Table of Contents
Overview
In the rapidly evolving landscape of AI, gaining deep insights into your Large Language Models (LLMs) is no longer a luxury—it’s a necessity. OpenLIT emerges as a powerful solution, offering a drag-and-drop Dashboard Builder for comprehensive LLM observability. Designed for full control and flexibility, OpenLIT is self-hosted and OpenTelemetry-native, ensuring it integrates effectively with OpenTelemetry-compatible SDKs and observability tools. It empowers developers and teams to monitor critical metrics like cost and accuracy through custom, vendor-neutral views, with dashboards that are fully importable and exportable as JSON.
Key Features
OpenLIT is packed with functionalities designed to give you unparalleled control over your LLM observability.
- Drag-and-drop Dashboard Builder: Create intuitive and powerful dashboards with ease, allowing you to visualize your LLM data exactly how you need it through an intuitive interface that requires no coding expertise.
- Custom Views for LLM Metrics: Tailor your monitoring experience to focus on the specific performance indicators that matter most to your AI models, such as token usage, latency, response quality, and cost tracking across multiple model providers.
- JSON Import/Export: Share, backup, and version control your custom dashboards effortlessly, promoting collaboration and consistency across teams while enabling easy migration between environments.
- Self-hosted and OpenTelemetry-native: Maintain full control over your data and infrastructure, integrating smoothly with your existing OpenTelemetry pipelines without vendor lock-in while ensuring data sovereignty.
- Vendor-neutral Monitoring: Enjoy the flexibility to monitor LLMs from any provider, ensuring your observability solution adapts to your evolving technology stack while supporting standardized telemetry formats.
How It Works
OpenLIT simplifies the process of setting up and managing your LLM observability through a streamlined workflow designed for maximum user control. First, you begin by utilizing the intuitive drag-and-drop builder to assemble your desired dashboard layout, organizing widgets into folders and applying custom filters. Next, you connect your LLM data to these dashboards via OpenTelemetry instrumentation, leveraging its robust and standardized data collection capabilities. Once your data streams are established, you can then customize visualizations to represent your metrics in the most insightful way, from charts and graphs to tables and gauges. Finally, for sharing or version control, your fully configured dashboards can be exported or imported as JSON files, making collaboration and deployment across different environments seamless.
Use Cases
OpenLIT’s flexible and powerful observability features make it suitable for a wide range of applications in the AI development lifecycle.
- Monitoring AI Model Performance: Gain real-time insights into the operational efficiency and responsiveness of your LLMs, identifying bottlenecks and areas for optimization across your entire AI stack.
- Cost Tracking in LLM Deployments: Keep a close eye on token usage and API calls to manage and optimize expenditures associated with your LLM services, with detailed cost breakdowns by model, user, or application component.
- Accuracy Analysis for Developers: Developers can deeply analyze model responses, pinpointing areas where accuracy can be improved and iterating on model versions more effectively through comprehensive evaluation metrics.
- Custom Observability in Self-hosted Environments: For organizations prioritizing data sovereignty and control, OpenLIT provides a robust, self-hosted solution for tailored monitoring without relying on third-party cloud services.
Pros \& Cons
Advantages
- Full Customization: Design dashboards and views precisely to your specifications, ensuring you monitor exactly what you need with complete flexibility in widget arrangement and data visualization.
- Open-source Compatibility: Integrates seamlessly with the broader open-source ecosystem, particularly through OpenTelemetry, enabling compatibility with existing observability infrastructure.
- Self-hosted Security: Maintain complete control over your data and infrastructure, enhancing security and compliance while avoiding data transmission to external services.
Disadvantages
- Setup Required for Self-hosting: While offering control, the self-hosted nature means initial setup and ongoing maintenance are required, including infrastructure management and updates.
- AI-focused Scope: While OpenLIT now supports broader AI stack monitoring including Vector DBs and GPUs, it’s not a general-purpose observability platform for all infrastructure components.
How Does It Compare?
When placed alongside established observability platforms like Datadog and New Relic, OpenLIT carves out a distinct niche in the rapidly evolving LLM observability space. While Datadog and New Relic have recently introduced LLM monitoring capabilities as part of their broader APM offerings in 2025, OpenLIT’s dashboards are purpose-built for AI applications. This focused approach means it’s specifically designed to capture and visualize the unique metrics and performance indicators crucial for Large Language Models, offering deeper, more relevant insights into AI operations.
Current Competitive Landscape (2025):
- Traditional APM Tools: Datadog LLM Observability, New Relic AI Monitoring – offer LLM features within general APM platforms
- Specialized LLM Tools: LangSmith, Langfuse, Phoenix (Arize), Helicone – focus on specific aspects like tracing or evaluation
- Open Source Solutions: OpenLLMetry (Traceloop), Lunary, TruLens – provide various approaches to LLM monitoring
- Enterprise Platforms: Coralogix AI Observability, Maxim AI – comprehensive commercial solutions
OpenLIT’s self-hosted model provides a significant advantage for organizations that prioritize data sovereignty and wish to avoid vendor lock-in, particularly relevant for enterprises with strict compliance requirements. This gives users more flexibility for AI monitoring, ensuring their observability strategy aligns perfectly with their LLM development and deployment needs without being tied to a specific vendor’s ecosystem or data retention policies.
Enhanced EEAT Considerations
Expertise: Built specifically for AI/LLM observability with deep understanding of GenAI monitoring requirements and OpenTelemetry standards compliance.
Experience: Active open-source project with documented deployments across various AI applications, regular updates, and community contributions.
Authoritativeness: Recognized in the OpenTelemetry community for GenAI semantic conventions compliance and featured in multiple technical publications and conferences.
Trustworthiness: Open-source transparency with publicly auditable code, self-hosted deployment option for complete data control, and adherence to established observability standards.
Final Thoughts
OpenLIT presents a compelling solution for anyone serious about understanding and optimizing their Large Language Models and broader AI applications. Its commitment to self-hosting, OpenTelemetry-native integration, and a highly customizable drag-and-drop dashboard builder positions it as a powerful, vendor-neutral tool for comprehensive AI observability. While it requires some setup for self-hosting and focuses primarily on AI applications rather than general infrastructure, its advantages in customization, security, and specialized insights make it an invaluable asset for developers and teams navigating the complexities of modern AI systems. If you’re looking for granular control and deep, AI-specific insights without vendor lock-in, OpenLIT offers a mature and actively developed solution worth serious consideration.