If you've used Amazon CloudWatch Logs, you know the struggle. It's often the first place we check when debugging issues, but let’s be honest, it’s not the smoothest experience.
Here’s my journey with CloudWatch Logs:
Step 1️⃣: I started using CloudWatch Log Insights & filters to search for errors and set up alerts. But filtering through logs was still tedious.
Step 2️⃣: To make it easier, I exported logs to the ELK stack (Elasticsearch, Logstash, and Kibana) for better visualization and searchability.
Step 3️⃣: Even then, something was missing. So, I integrated third-party solutions like Splunk to streamline log analysis.
But after trying all these approaches, I realized one thing: I was still doing the heavy lifting manually. All these solutions just centralized logs—they didn’t analyze them for me.
🤖 Enter LLMs , Since we’re in the era of Large Language Models (LLMs), why not let AI do the hard work?
💡 So, I integrated DevOps-GPT with CloudWatch Logs.
✅ Now, the AI agent automatically:
🔹 Scans CloudWatch logs for errors
🔹 Sends them to an LLM of your choice (OpenAI, Llama, DeepSeek)
🔹 Delivers actionable recommendations directly to Slack for faster resolution
Instead of spending hours manually searching through logs, LLMs can help us debug smarter, not harder.
🔗 GitHub link: https://github.com/thedevops-gpt/devops-gpt/
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