Measure AI Cost Metrics from the Beginning
AI costs can spiral quickly if you’re not tracking them. Learn how to log and monitor the spending tied to your LLM, workflow, and storage usage, so you can scale with confidence and stay within budget.
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Log LLM and Workflow Performance Metrics
Speed, reliability, and consistency are critical for production AI. Learn how to log response times, success rates, and throughput for your LLM and automation workflows, turning performance data into insights you can use to optimize and scale.
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Run Your Logging Platform on a Separate Server
LLM-powered automations often log prompts, responses, and user metadata, but raw logs can expose sensitive info like emails and user IDs. This post shows how to build a simple Python logger that anonymizes data before writing it to disk, protecting privacy without sacrificing observability.
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Anonymize Logs Before You Regret It
LLM-powered automations often log prompts, responses, and user metadata, but raw logs can expose sensitive info like emails and user IDs. This post shows how to build a simple Python logger that anonymizes data before writing it to disk, protecting privacy without sacrificing observability.
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Build an Admin Portal Before You Need One
AI demos often skip admin tools—but in production, they’re essential. This post shows how to build a simple Flask-based admin portal that can restart services, display system status, and show logs. Perfect for managing your LLM stack with tools like N8N, OpenWebUI, and Ollama.
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Aggregate Logs and Set Alerts Early
Local logs won’t cut it in production. In this post, you’ll learn how to forward structured logs from your AI automation to a central system like Graylog, and set up real-time alerts to catch failures before users do. A simple Fluent Bit config and JSON logging format make it easy to get started.
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Metrics Matter: Log What the LLM Says
Make your LLM automations more reliable by logging both prompts and responses. This simple change gives you visibility into what your model is doing, crucial for debugging, auditing, and improving over time. In this post, we’ll show you how to log structured JSON output to a file, making it easy to parse and monitor.
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Run Your No-Code AI Stack in Docker
Moving your no-code AI automation into containers makes it more stable, portable, and ready for production. In this post, we walk through running OpenWebUI, N8N, Ollama, and Postgres together using Docker Compose, so your entire automation stack works like one unified app.
