• Understand Helicone’s role in OpenAI observability

• Install and configure Helicone as a proxy server

• Monitor OpenAI prompt traffic in real time

• Analyze token usage and estimate API costs

• Visualize latency, errors, and prompt behavior

• Organize logs by user, feature, or project

• Track performance metrics across endpoints

• Debug slow or failed requests quickly

• Optimize prompts for cost-efficiency and reliability

• Apply Helicone to both dev and production use cases

1. Introduction to API Observability for OpenAI

2. What is Helicone? Architecture and Use Cases

3. Installing Helicone and Configuring the Proxy

4. Sending OpenAI API Requests through Helicone

5. Real-Time Monitoring and Log Inspection

6. Token Tracking and Cost Analysis

7. Grouping Logs by App, Endpoint, or User

8. Handling Errors and Debugging Failures

9. Measuring and Improving Latency

10. Prompt Optimization with Helicone Insights

11. Case Study: Monitoring a Chatbot Using Helicone

12. Best Practices for Secure and Scalable Deployment

Upon completing the course, learners will receive a Uplatz Certificate of Completion certifying their ability to monitor and optimize OpenAI API usage with Helicone. This certification validates your understanding of LLM observability, token management, and cost monitoring—critical for developers managing production AI workflows. This credential is useful for MLOps engineers, LLM developers, and prompt engineers aiming to improve performance and accountability in their applications.

With LLMs becoming core infrastructure for many applications, managing OpenAI APIs efficiently is a key job skill. Helicone equips you with the observability and debugging capabilities needed to support large-scale AI apps.

Career roles include:

• LLM API Engineer

• AI Platform Reliability Engineer

• Prompt Infrastructure Developer

• Cost Optimization Analyst (AI Systems)

• AI Performance Engineer

• ML DevOps Specialist

These positions are vital in AI startups, SaaS platforms, customer support automation, and enterprise AI deployments. With Helicone, you gain the tools to run lean, fast, and reliable LLM applications at scale.

1. What is Helicone used for?
   Helicone is a monitoring proxy for OpenAI API calls, enabling developers to observe, debug, and optimize prompt traffic.

2. How does Helicone collect data?
   It intercepts requests to OpenAI’s API and logs them with metadata like latency, tokens, and status codes.

3. Can Helicone help reduce costs?
   Yes, by showing token usage per request and allowing prompt optimization, it helps cut unnecessary API spend.

4. Is Helicone open-source?
   Yes, Helicone is fully open-source and can be self-hosted or deployed on cloud environments.

5. What kind of metrics does Helicone provide?
   Latency, token usage, success/failure rates, endpoint breakdowns, and per-user stats.

6. Can I use Helicone with non-OpenAI models?
   Currently, Helicone is optimized for OpenAI APIs, but community support for other providers is growing.

7. How is Helicone different from PromptLayer?
   PromptLayer logs prompt versions and analysis; Helicone focuses on real-time API traffic and observability.

8. Can Helicone group logs by project or user?
   Yes, Helicone supports grouping and tagging logs for better organization and debugging.

9. Is Helicone safe to use in production?
   Yes, with proper configuration, it supports secure proxying and data privacy best practices.

10. Why is API-level observability important?
    It allows teams to identify latency spikes, debug failed requests, and control LLM usage costs.