Llog — Analytics for LLM applications
Llog is a post-production analytics and monitoring platform for LLM application teams
Llog is an observability and analytics platform designed to provide comprehensive insights into Large Language Model (LLM) applications after they go live. It is built specifically for production teams and product managers who need to monitor how their AI models perform in real-world scenarios. By tracking every interaction, Llog helps businesses understand user behavior and identify where their AI applications might be failing or providing suboptimal responses. The platform features a customizable dashboard that visualizes performance metrics through a user-friendly interface. Users can monitor real-time data to catch hallucinations or latency issues the moment they occur. This level of oversight ensures that companies can maintain high standards for their AI-driven customer experiences. Beyond simple monitoring, the tool provides end-to-end analysis that allows teams to optimize their post-production processes based on factual usage patterns. As more companies integrate AI into their core operations, having a reliable way to audit these systems becomes a necessity. Llog provides the secure data storage and reporting tools required for data-driven decision-making and compliance. You can find more specialized solutions like this in the verified listings on surfai.app. The platform integrates with existing developer stacks to ensure that monitoring does not interrupt existing workflows. Llog operates on a freemium model, providing a generous free tier for early-stage projects and startup teams. Scaling businesses can upgrade to paid plans as their request volume and data storage needs increase in 2026.
- Pricing: freemium
Specifications
| Pricing | freemium |
|---|---|
| Integrations | OpenAI, Anthropic, LangChain, Python SDK, Slack, Vercel |
| Best for | product-managers, software-engineers, data-scientists, ai-founders |
| Website status checked | 2026-11-02T18:00:49.153+00:00 |
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|---|---|---|---|
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