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Large Language Models, AI Visualization: Unveiling Insights with OpenLit
Large language models (LLMs) and AI visualization are revolutionizing how we interact with technology. Enter OpenLit, an open-source application observability tool specifically designed for LLM and GenAI infrastructures. OpenLit seamlessly integrates with OpenTelemetry, enabling effortless data collection through simple command setups. This powerful tool provides granular insights into resource usage, allowing you to analyze the performance of LLMs, vector databases, and GPUs for improved operational efficiency. With real-time data streaming capabilities and a user-friendly interface, OpenLit empowers you to explore costs, token consumption, and performance metrics with ease. Furthermore, it connects with popular observability platforms like Datadog and Grafana, streamlining the data export process for enhanced monitoring and analysis.
Pricing
Openlit offers a flexible pricing structure based on the usage of Large Language Models (LLMs). They provide a default JSON file with pricing information for various standard LLMs, which is regularly updated. However, users can also customize their pricing by providing their own JSON file specifying costs for custom or fine-tuned models not covered in the default file. This allows for tailored pricing based on specific business needs. Here are the key points: Default Pricing: Openlit provides a default JSON file with up-to-date pricing information for common LLMs. Custom Pricing: Users can define their own pricing structure using a custom JSON file, particularly for fine-tuned or unique models. Pricing Structure: Both the default and custom pricing files specify costs per 1000 tokens for chat-based models in USD. Initialization: Openlit's SDK initializes by pulling pricing data from either the provided custom file or the default file. Ensure accessibility during initialization. Updates: Custom pricing files need to be updated manually if prices change, whether using a URL or local file path.
Hybrid Models
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