10 Best LLM Monitoring Tools for Businesses: Reduce Risk, Boost Accuracy

Large Language Models, or LLMs, are changing the world. Tools such as ChatGPT or the AI that drives your customer service chatbot. They are too intelligent, but not flawless. They do not work at times, are slow, or even more expensive than you would think. Here, the tools of the LLM monitoring come in. They do a health check on your AI applications. They keep a watch over your LLM, inform you about when you have done something wrong, and assist you in correcting it.

This paper will take you through the top LLM monitoring tools of today. We will tell you why you should have them, what to seek and we will give you our top 10. We shall simplify it and then you can select the appropriate tool for your business.

Why LLM Monitoring Tools Matter for Your Business

Suppose that you have already deployed a new AI chatbot on your website. Everything initially appears wonderful. However, shortly, the customers begin to complain that the chatbot is not providing correct answers or it is too slow. In the absence of a LLM monitoring tools, you would not know what is going on. You would be flying blind.

llm monitoring tools provide you with the ears and eyes. The following is the reason why they are so important:

• They Catch Mistakes: A monitoring tool can raise a red flag on these wrong responses to correct the situation. This safeguards your brand against the transmission of poor information.
• They are Cost-Effective: LLMs are not cheap to run. Every question that your user poses costs you some money. These costs are monitored using a monitoring tool that indicates to you the queries that cost the most and allows you to discover ways of being more efficient.
• They Enrich User Experience: These are tools that display the interaction of the users with your AI. You are also able to know what questions are often asked, where users become disoriented, and the speed at which your AI is responding. This is information that will assist you in improving your application and making it more useful to your users.
• They Are Safe: You would like to know that your AI will not be abused and produce unacceptable content. Such behavior can be identified and prevented by LLM monitoring tools, and this will ensure that your application and your users are safe.
• They Accelerate the Process of Problem-Solving: When things are bad, you must work with them quickly. Monitoring tools notify you whenever there is an error or a cost boom. This implies that you will be able to solve issues within minutes as opposed to days.

Types of LLM Monitoring Tools You Should Know

The number of various kinds of LLM monitoring tools is limited; each applies to distinct needs.

• All-in-One AI Observability Platforms: Are potent tools that are targeted at bigger teams and companies. They range all the way through to performance and cost monitoring, to the end-of-the-line troubleshooting and analytics.
• Developer-First Tools: These are developer-specific tools. They can be installed and built into the code with ease. They concentrate on providing the developers with the precise information that they require to troubleshoot and enhance the application within a short time.
• Open-Source Tools: These are free of cost, and the code is publicly accessible. They are ideal with startups, individual developers or when complete control and flexibility is desired. More technical expertise may be required to install them; however, they are very powerful at a lower price.

Key Features to Look For while Choosing the Right Tool

The most significant features to take into account when you are about to choose a tool are the following:

• Simple Dashboards: The tool is to provide information straightforwardly. You must be able to view your most important metrics, such as the cost, the speed, and the error rates.
• Detailed Conversation Tracking (Tracing): You should view the entire path of the request made by a user. It implies viewing the prompt created at the beginning, all actions in the middle, and the final response of the AI. This will make you know precisely where you have gone astray.
• Performance Metrics: The tool should be able to monitor key measures such as the speed of the AI (latency), the number of requests that it can serve, and the price of each interaction.
• Alerts and Notifications: The tool is expected to notify you when something is not right automatically. To use an example, it would email you or send you a Slack message when your costs abruptly increase or when there are high levels of error.
• User Feedback Analysis: A nice tool will enable you to gather and review feedback from your users easily. This is how you can tell whether your AI can be of real use.
• Easy Integration: The tool must be easy to integrate into your application. It must be compatible with your existing programming languages and LLM providers (such as OpenAI or Google).

LLM Monitoring Tools List Table

Tool Name	Type	Free/Open-Source	Key Features	GitHub/Website
Arize AI	All-in-One Platform	Free Tier Available	Troubleshooting, drift detection, evaluations	Website
WhyLabs	All-in-One Platform	Yes (LangKit is open-source)	Data drift, model monitoring, data quality	Website
Fiddler AI	All-in-One Platform	No	Explainable AI, analytics, fairness checks	Website
Langfuse	Developer-First	Yes (Fully open-source)	Tracing, debugging, usage dashboards, evaluations	Website
LangSmith	Developer-First	Free Tier Available	Debugging, tracing, prompt playground	Website
Weights & Biases	All-in-One Platform	Free Tier for individuals	Experiment tracking, prompt engineering	Website
Datadog	All-in-One Platform	No	Integrates with existing infra, cost tracking	Website
New Relic	All-in-One Platform	Free Tier Available	Unified observability, tracks full app stack	Website
Helicone	Developer-First	Yes (Open-source proxy)	Caching, cost management, user analytics	Website
Galileo	Developer-First	No	Hallucination detection, prompt quality checks	Website

10 Best LLM Monitoring Tools (Top Picks)

The following is a closer examination of our top 10 picks of LLM monitoring tools.

1. Arize AI

Arize AI is a leading observability tool for machine learning and it has great capabilities regarding LLMs. It helps teams troubleshoot their AI applications and find and fix issues. Arize is designed to be used on an enterprise level, which means that you can explore your data to the bottom to see the root cause of a problem. It is particularly good at detecting drift or when your model slowly gets worse as time goes by due to the change in the input data.

The platform provides easy visualization, which makes it easy to compare between good and bad responses, and you know why you have an LLM acting the way it is. When your chatbot suddenly begins to give weird responses, Arize is the kind of tool that helps you to figure out what exactly is causing the problem, be it a prompt issue, data issue or model issue. 

Key Features:

• Effective root cause analysis and troubleshooting.
• Good in identifying data and model drift.
• Monitors and alerts that are automated.
• Scoring of the quality of the LLM responses.
• Effective performance visualization dashboards.

Best For: Large organizations and teams that require a profound and analytical instrument to identify and rectify complicated issues in their AI applications.

Pros:

• Very detailed analytics.
• Good at knowing the cause of a problem.
• Strong emphasis on model appraisal.

Cons:

• Can be complex for beginners.
• High pricing may be used in large-scale applications.

Pricing: Pricing is free on small projects. Premium plans are determined by usage and functionality.

Website: https://arize.com/

2. WhyLabs

WhyLabs offers a strong AI and data pipeline monitoring platform, and it pays a lot of attention to LLMs.Its key advantage is that it helps to avoid premature issues by the time they impact your users. WhyLabs is great for detecting data quality problems, data drift and model performance degradation. It works by creating a “profile” of your normal data and then alerting you whenever something is different from the profile.

In the case of LLMs, it runs an open-source library named LangKit to derive key signals of your text data, such as toxicity, sentiment and text quality. This way it will be possible to monitor not only the performance but the safety and relevance of the results produced by your LLM. It is an excellent option to the team that wishes to be proactive regarding AI health and makes sure their models act as anticipated on a daily basis.

Key Features:

• Predictive data drift and anomaly monitoring.
• Open source LangKit for LLM specific metrics.
• Data quality and security auditing.
• Highly customized dashboards and alerts.
• Not only the model, but works throughout the AI pipeline.

Best For: Teams that prefer to automate the process of monitoring data quality and model health in order to identify problems at their initial stages.

Pros:

• Strong focus on prevention.
• Powerful open source component (LangKit).
• Easy to set up and get started.

Cons:

• The user interface can be heavy with information at times.
• May is a tool that has to be configured to be the most valuable.

Pricing: Has many free plans. The number of models and data volume determines paid plans.

Website: https://whylabs.ai/

3. Fiddler AI

Fiddler AI is an AI Observability platform, and it is unique because it has a focus on “Explainable AI” (XAI). This means it does not just tell you what your model is doing but it will also help you understand why as well! For LLMs, this is of incredible value. Fiddler can be used to analyze your prompts and responses to explain which parts of the input led to a particular output. This assists you in troubleshooting complex problems and instills confidence in your AI system. It offers rich analytics on the model performance, drift and data quality.

Fiddler also puts a lot of emphasis on fairness and bias and helps you to ensure that your LLM is not producing biased or unfair responses. It is a comprehensive platform that has been designed for businesses that want to ensure that they have a responsible, transparent, and trustworthy AI available.

Key Features:

• Competent Explainable AI (XAI) properties.
• The performance, drift and data quality monitors.
• A firm emphasis on model bias and fairness.
• Building block registry Manage all your models centrally.
• Rich Analytics and Business Dashboards.

Best For: Companies with regulated environments (such as finance or health care) or any other company that has to justify the actions of their AI.

Pros:

• Leader in Explainable AI.
• Helps to build trust and transparency.
• Comprehensive Features of Monitoring

Cons:

• It may be costlier than other alternatives.
• It might be more complex than is needed for simple applications

Pricing: Fiddler is an enterprise-focused product, and therefore, you need to contact them for pricing details.

Website: https://www.fiddler.ai/

4. Langfuse

The open-source tool Langfuse is an excellent tool designed to assist developers of applications that utilize the LLM. Its core strength is “tracing.” It provides you with a stepwise picture of each request that your application makes. In case your application makes many calls to an LLM or any other tool, Langfuse presents you with the overall chain of events as a single, clean timeline. This renders debugging very easy.

The detailed inputs, outputs, speed, and cost of every step are visible. Langfuse also offers gorgeous dashboards to monitor the general usage, expenditures, and quality metrics over time. Since it is open-source, you can also host it and have it at no cost, allowing you to have complete control over your data. It is an easy-to-use but powerful tool that is fast gaining popularity with developers.

Key Features:

• Elaboration of the complex LLM chains.
• Self-hostable and open-source.
• Cost, latency, and usage dashboards.
• Gives you the opportunity to gather the user responses and scores.
• Simple support of popular libraries such as LangChain and LlamaIndex.

Best For: Developers and startups require highly powerful, open-source debugging and LLM monitoring tools for their LLM applications.

Pros:

• Full-fledged and open-source.
• Excellent for debugging.
• Straightforward user interface.

Cons:

• Fields that you are required to host yourself (however, they are also available on the cloud).
• Paid platforms have very advanced capabilities that Lacks is permitting.

Pricing: Free and open-source. They have also provided a managed version of the cloud that is free and has paid options when using it by a larger team.

Website: https://langfuse.com/

5. LangSmith

The team that developed LangChain, one of the popular frameworks for developing LLM applications, developed LangSmith. This is why it is the ideal companion of any project made with LangChain. LangSmith is configured to assist in debugging and testing applications and monitoring them with ease. Similar to Langfuse, it provides detailed tracing, which lets you see what is actually occurring inside your LLM chains.

It is possible to view all prompts, responses, and tools in real-time. It is also featured with a feature known as the Hub, where you can save and share your prompts and version them, which is useful in collaboration with the team. LangSmith is created to simplify the development lifecycle, starting with experimenting with new prompts to manage your app after it is online. 

Key Features:

• Fully integrated with the LangChain framework.
• Complex tracing and debugging tools.
• A prompt versioning and management hub.
• Data creation and evaluation tools.
• Live tracking of application performance.
• Best use: This is best used with developers and teams who are working on their applications with the LangChain framework.

Pros:

• Stress-free integration with LangChain.
• Good debugging and visualization applications.
• Good at quick engineering and management.

Cons:

• Not as advantageous otherwise.
• It is a relatively new tool, and more features are being added.

Pricing: Has a free plan available to developers. Teams that have a higher usage requirement can purchase paid plans.

Website: https://www.langchain.com/langsmith

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6. Weights & Biases (W&B)

Weights & Biases is a popular machine learning tool, mostly utilized in experiment monitoring. It has, however, extended its features to be a robust platform for developing and monitoring LLM, which they refer to as W&B Prompts. It assists you in the management of the whole lifecycle of your LLM application, including testing various prompts and models and measuring performance in the production phase.

You are able to store all your prompts, responses, and costs and then visualize them in beautiful, customizable dashboards. W&B particularly suits a team that engages much in prompt engineering because it allows you to put the results of various prompts side-by-side. 

Key Features:

• Good at experiment tracking and fast engineering.
• Records and displays all the inputs, outputs, and metadata of LLM monitoring tools.
• Team collaborative tools.
• Write beautiful reports to report your findings.
• Measures performance and costs in production.

Best For: Machine learning teams and engineers who desire to have one tool to experiment with and monitor their LLM applications.

Pros:

• Development and LLM monitoring tools all-in-one platform.
• Efficient visualization and reporting systems.
• Its community and superior documentation.

Cons:

• Is capable of doing more than is required in simple monitoring tasks.
• The user interface is very feature-rich, and it can be difficult to use initially.

Pricing: No charge for personal use and scholarly projects. Teams and businesses can have paid plans.

Website: https://wandb.ai/site/

7. Datadog

Datadog is an industry leader in cloud monitoring. It is a single platform which many companies are already using to monitor their servers, databases and applications. In the recent past, Datadog has also developed features to track LLM applications. The greatest benefit of it is that you can view everything at a single location. You are able to view your LLM metrics performance right along with your server CPU usage and your application error rates.

This unified perception is very useful in getting the complete picture of a problem. In case your chatbot is slow, Datadog can be used to identify whether the issue is the LLM, your code, or your server. It is the ideal option when companies are already committed to the Datadog ecosystem and would like to add LLM monitoring tools to the already existing configuration.

Key Features:

• Single point of access to all your technology.
• Measures LLM expenses, performance, and errors.
• Easy integration with key cloud vendors and services.
• Intense alerting and dashboarding.
• Performance Correlates the performance of the LLM with the health of the application in general.

Best For: The companies that already use Datadog to monitor the infrastructure and applications.

Pros:

• One platform that will meet all your monitoring requirements.
• Powerful and reliable.
• Long list of integrations.

Cons:

• It can be very expensive.
• The features of an LLM cannot be compared with a dedicated tool such as Arize or Langfuse.

Pricing: According to an elaborate utilization model of various products. It is mostly believed to be a company-priced tool.

Website: https://www.datadoghq.com/

8. New Relic

Similar to Datadog, another significant application performance monitoring (APM) competitor is New Relic. It gives you a full observability platform to assist you in monitoring your entire software stack. New Relic has also added the monitoring of LLM to its platform, and you can monitor the performance of your AI application in the same tool you use to monitor everything.

You are able to track important measures such as response time, token consumption, and error rates of providers such as OpenAI. New Relic is powerful because of the possibility to follow a user request throughout your services, including your website, across your backend services to the LLM monitoring tools, and vice versa. This end-to-end monitoring allows the bottlenecks of performance to be found and resolved much easier regardless of their location.

Key Features:

• Tracing through your entire application.
• Efforts Monitors LLM-specific measurements, such as the number of tokens and response time.
• Components of a complete observability platform.
• Effective alerting and artificial intelligence.
• Relates LLM performance with business performance.

Best For: New Relic is the most appropriate to use when the company is already using it or wants to have a single and unified application to monitor their entire technology infrastructure.

Pros:

• Full transparency in your entire system.
• Strong and developed platform.
• Large free tier to be used.

Cons:

• The characteristics of LLM may not be as specialized as special tools.
• As you use more, it may become costly.

Pricing: Has a free plan that has a decent data allowance. Paid plans increase based on the level of data and the number of users.

Website: https://newrelic.com/instant-observability/openllm

9. Helicone

Helicone is an open-source developer-friendly tool that aims to simplify and streamline the process of monitoring your application to the LLM, and it works as a proxy, i.e. your application makes requests to the LLM via Helicone. This is as simple as changing a single line of code to set up. As soon as it is configured, Helicone provides you with a clean dashboard where you may view all your requests, costs, users, and errors.

Caching is one of its best attributes. Helicone is capable of automatically saving the solution to frequently asked queries, and thus, when a user poses a question, it can answer it immediately without involving the LLM monitoring tools. It makes your application quicker and will save you a lot of money. 

Key Features:

• Simple proxy Easy to set up an open-source proxy.
• Large-scale caching to minimize both cost and latency.
• Monitoring the usage, costs and errors with a clean dashboard.
• Tracking and rate limiting of users.
• Filtering and analyzing custom properties.

Best For: Developers and startups that want to have a tool that is easy to implement and has high emphasis on cost and performance.

Pros:

• Very easy to set up.
• A massive money-saving feature is the caching feature.
• Self-host and Open-source.

Cons:

• Concentrates more on request tracking rather than high-level analysis of the model.
• Favors the troubleshooting capabilities of bigger platforms.

Pricing: Helicone is open-source. They also provide a free tier and pa aid plan of a generous cloud version depending on the volume of requests.

Website: https://www.helicone.ai/

10. Galileo

Galileo is a strong instrument that puts significant emphasis on one of the largest issues with LLMs: the quality and reliability of their answers. It is created to assist in identifying and resolving problems such as hallucinations, toxicity, and data leakage prior to the launch of your application. Galileo possesses a distinct set of metrics that are used to assess the output of LLM monitoring tools.

It is able to identify when your model provides a wrong answer automatically, does not answer, or provides an inappropriate response. This is up to a T when developing and testing it. By testing your prompts and responses on Galileo, you can easily find out the areas of your prompts or your model that are weak so that you can create a far more robust and reliable AI application.

Key Features:

• Experts in the detection of response errors and hallucinations.
• Measures of quality of LLM output that are automated.
• Assists you in narrowing and sharpening your prompts.
• Data privacy and security monitors.
• Good at testing and evaluating before production.

Best For: Teams that are extremely concentrated on the creation of correct, faithful, and dependable LLM applications and need to remove hallucinations.

Pros:

• Good at locating and rectifying the quality of responses.
• Saves a lot of time during manual testing.
• Gives practical suggestions on how to do better prompts.

Cons:

• Less concerned with real-time production monitoring than with evaluation.
• It is a high-quality, dedicated tool.

Pricing: Galileo is a business product. Their sales team will have to be contacted to get pricing information.

Website: https://galileo.ai/

How to Use LLM Monitoring Tools for a Better AI and Brand

There is more than simply chart gazing with LLM monitoring tools. It is about doing something to enhance your AI, and in the process, your brand image. Customers are pleased with a smart and dependable AI assistant. When one is broken, it does the reverse.

These tools can be used to make a real difference in the following way:

• Enhance the User Experience: Review the data to identify areas that the users are struggling with. Do they frequently rephrase their questions? Is the AI too slow? Enter this data in order to make the instructions of your AI easier or faster. A smooth experience will result in satisfied users.
• Manage Your Costs: The cost dashboard will help you know the most expensive operations. Perhaps there is one category of question that consumes a significant amount of resources. Then you can optimize the prompt of that question to make it more cost-effective, which will save your company money directly.
• Optimize and Hone Your Prompts: The answer provided by your AI is determined by how well you provide it with a prompt. The monitoring tool helps view what prompts give the best answers (according to the user feedback or ratings). You are able to test various versions of a prompt and determine which one works better.
• Assure Safety and Quality: Install warning systems for malicious or substandard material. In case the tool indicates a toxic response, you may fix the underlying cause immediately. This secures your users and your brand is professionally and safely represented.
• Prevent Problems Before They Happen: Monitor errors or performance spikes with real-time alerts. This enables your development team to jump onto a problem and fix it in a short amount of time, before there is even a significant number of users being impacted.

Conclusion

The excitement of creating an application using a Large Language Model is one thing, yet the release is only the first step. To achieve success in the long term, one must pay attention to it, learn about it and keep on improving it. The LLM monitoring tools do not seem a luxury anymore, but a mandatory tool of any serious AI developer or business.

Regardless of whether you adopt a potent open-source solution such as Langfuse or an all-inclusive enterprise platform such as Arize AI, the implementation of monitoring will be reimbursed many times over. It will make you save money, sort out issues more rapidly, and most importantly, develop an AI application that your users will adore.

Also Read: AI Tools to Create Custom Agents Without Coding

FAQs

In simple terms, what are LLM monitoring tools? 

The act of keeping an eye on your AI application to ensure that it is operating as expected is known as LLM monitoring. It monitors such aspects as performance, cost, errors, and the quality of answers of the AI.

Why should LLMs be monitored?

The reason is that LLMs are erratic. They are also capable of error, are slow, or can be extremely costly. Monitoring assists you in detecting and correcting such issues early enough so that you can have good user experience.

Is the implementation of LLM monitoring tools challenging? 

No, most current tools are configured to be user-friendly. Usually, it entails the inclusion of a few lines of code in your application.

Is it possible to begin with free LLM monitoring tools? 

Yes! Most of the most popular tools, such as Langfuse, Helicone and WhyLabs, have open-source versions or free editions of their cloud products, which are free to start with.