What is LLM Observability and Monitoring

Lina Lam· March 13, 2025

Building reliable LLM applications in production is incredibly challenging.

Today's LLM applications are customer-facing and handle sensitive information, make important decisions, and represent your brand. With many emerging Large Language Model (LLM) applications, effective monitoring is a competitive advantage.

Today, we'll walk you through the importance of observability for production applications and how to use observability tools to monitor LLM performance.

Let's dive in!

What is LLM Observability?

LLM observability refers to the comprehensive monitoring, tracing, and analysis of LLM-powered applications. It involves gaining deep insights into every aspect of the system, from prompt engineering, to monitoring model responses, to testing prompts and evaluating the LLM outputs.

The Benefits of LLM Observability

• Understand model behavior: you can get visibility into how the model processes inputs and generates outputs.
• Diagnose and debug errors: you can quickly identify and resolve errors, bottlenecks, and anomalies.
• Improve your user's experience with AI: you can improve latency on time-sensitive tasks. Your users can have the joy of instant responses that they might not be getting from your competitors.

As you build your product from prototype to production, monitoring LLM metrics helps you to detect prompt injections, hallucinations and poor user experience, allowing you to improve your prompts for better performance on the go.

LLM Observability vs. Traditional Observability

LLMs are highly complex and contain billions of parameters, making it challenging to understand how prompt changes affect the model's behavior.

While traditional observability like Datadog focuses on system logs and performance metrics, LLM observability deals with model inputs/outputs, prompts, and embeddings.

Another difference is the non-deterministic nature of LLMs. Traditional systems are often deterministic with expected behaviors, whereas LLMs frequently produce variable outputs, making evaluation more nuanced.

In summary:

The Pillars of LLM Observability

1. Request and Response Logging

At the core of LLM observability is the detailed logging of requests and their corresponding responses. Logging them allows you to analyze patterns and understand the context which influenced the outputs.

LLM monitoring tools typically capture other useful metrics like latency, costs, Time to First Token (TTFT), and more. Tracking conversation histories, especially in multi-step workflows like an AI agent or a RAG application, helps you understand the user behavior and model's performance over time.

2. Online and Offline Evaluation

Assessing the quality of the model's outputs is vital for continuous improvement. Defining clear metrics—such as relevance, coherence, and correctness—enables monitoring of how well the model meets user expectations.

Collecting feedback directly from users offers valuable insights, while automated evaluation methods provide consistent assessments when human evaluation isn't practical.

3. Performance Monitoring and Tracing

Once your model's output accuracy reaches an acceptable level, the next thing to focus on should be improving its performance.

For example, tracking latency helps identify any bottlenecks in response generation. Tracking errors such as API failures or exceptions tells you how reliable your AI application is.

Tracing your multi-step workflows helps you debug faster and gives you a deeper understanding of your user's journey.

Here are some useful examples on debugging agents with Sessions.

4. Anomaly Detection and Feedback Loops

Detecting anomalies, like unusual model behaviors or outputs indicating hallucinations or biases, is essential for maintaining application integrity.

Implementing mechanisms to scan for inappropriate or non-compliant content helps prevent ethical issues. Feedback loops, where users can provide input on responses, facilitate iterative improvement over time.

5. Security and Compliance

Ensuring the security of your LLM application involves implementing strict access controls to regulate who can interact with model inputs and outputs. Protecting sensitive data requires compliance with regulations like GDPR or HIPAA.

Maintaining detailed audit trails promotes accountability and aids in meeting compliance requirements, building user trust.

Best Practices for Monitoring LLM Performance

Deploying LLMs in production comes with its set of challenges. We'll walk through some of the most common ones and how you can address them with Helicone.

1. Use Prompting Techniques to Reduce Hallucinations

LLMs sometimes generate inaccurate outputs that sound plausible - also known as hallucination. Hallucinations can happen frequently and undermine your user's trust as your usage goes up.

The good news is, you can mitigate this by using the right prompting techniques, or by evaluating your LLM outputs in Helicone.

2. Preventing Prompt Injections

Malicious users can manipulate their inputs to trick your model into revealing sensitive information or take risky actions. We dive deeper into this topic in the how to prevent prompt injections blog.

On a high-level, you can prevent injections by:

• Implementing strict validation of user inputs.
• Blocking inappropriate or malicious responses.
• Using tools like Helicone or PromptArmor for detection.

3. Caching to Improve Performance and Latency

Caching stores previously generated responses, allowing applications to quickly retrieve data without additional computation.

Latency can have the most impact on the user experience. Helicone allows you to cache responses on the edge, so that you can serve cached responses immediately without invoking the LLM API, reducing costs at the same time.

4. Tracking API Usage and Costs

It's important to know exactly what might be drilling a hole in your operational cost. LLM monitoring can improve cost savings by tracking expenses for every model interaction, from the initial prompt to the final response.

You can mitigate this by:

• Monitoring LLM costs by project or user to understand spending.
• Optimizing infrastructure and usage.
• Fine-tuning smaller, open-source models to reduce costs.

We wrote about effective cost optimization strategies in this blog.

5. Iterating on the Prompt

As models evolve, it's important to continuously test and audit your prompts to ensure they're performing as expected.

You should experiment with different variations of your prompt, switch models or set up different configurations to find the best performing prompt. You should also evaluate against key metrics that's important to your business.

import OpenAI from "openai";

const openai = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY,
  baseURL: `https://oai.helicone.ai/v1/${HELICONE_API_KEY}/`
});

Effective LLM Observability Tools

As companies rush to integrate LLMs into their business functions, observability platforms have transitioned from basic logging to comprehensive platforms that support the entire LLM lifecycle.

Helicone is an open-source alternative to LangSmith with impressive 2.3 billion processed requests, 3.2 trillion logged tokens, and 18.3 million tracked users. Other popular tools include Portkey, Langfuse, and LangSmith.

These monitoring tools provide the visibility developers need to monitor, debug, and continuously improve their AI applications.

Bottom Line

Now that you have a good understanding of how to implement monitoring strategies, it's time to put them into practice! We recommend signing up with a platform mentioned above, start logging, and see how users are interacting with your LLM app.

We are here to help you every step of the way! If you have any questions, please reach out to us via email at support@helicone.ai or through the chat feature in our platform.

You might find these useful:

• 5 Powerful Techniques to Slash Your LLM Costs
• Debugging Chatbots and AI Agents with Sessions
• How to Test Your LLM Prompts (with Helicone)