LLM Observability Tools: 2025 Comparison
LLM Observability Tools: 2025 Comparison
https://lakefs.io/blog/llm-observability-tools/
As OpenAI unveiled ChatGPT, which swiftly explained difficult problems, carved sonnets, and discovered errors in code, the usefulness and adaptability of LLMs became clear. Soon after, companies across various sectors began exploring new use cases, testing generative AI capabilities and solutions, and incorporating these LLM processes into their engineering environments.
Whether it’s a chatbot, product recommendation engine, or BI tool, LLMs have progressed from proof of concept to production. However, LLMs still pose several delivery challenges, especially around maintenance and upkeep.
Implementing LLM observability will not only keep your service operational and healthy, but it will also help you develop and strengthen your LLM process.
This article dives into the advantages of LLM observability and the tools teams use to improve their LLM applications today.
What Is LLM observability and why should I care?
LLM observability refers to gaining total visibility into all layers of an LLM-based software system, including the application, prompt, and answer.
While the architecture for LLM usage still requires the traditional observability configuration, the LLM is primarily a separately deployed object outside of your code that is accessed in a prompt and response style, calling for qualitative observability.
Each response must be reviewed for cleanliness and relevance. To meet your monitoring objectives, you must set up recording of your LLM prompts and replies, followed by contextual analysis.
Why Is LLM observability important?
Since LLM tools are still in their early stages, various concerns may arise, both from user input and LLM answers. An LLM observability tool helps to keep track of potential concerns with LLM applications, such as:
- Hallucinations – When presented with questions to which they can’t respond, LLM-powered apps may occasionally provide misleading information, a behavior known as “hallucinating”.
- Performance and cost – Many applications designed using LLMs rely on third-party models. This can result in performance deterioration of third-party APIs, inconsistencies due to algorithm changes, and excessive costs, particularly for huge data volumes.
- Prompt hacking – sometimes known as prompt injection, it allows users to direct LLM programs to output specified text, potentially incorrect or dangerous content.
- Security and data privacy – LLMs raise security concerns, including possible data breaches, output biases caused by biased training data, and the danger of unauthorized access. Furthermore, LLMs may generate a response that contains sensitive or personal information. Thus, strict security measures and ethical norms are essential for LLMs.
LLM Monitoring vs. LLM Observability
What is the difference between LLM monitoring and observability?
LLM monitoring involves tracking LLM application performance using a variety of evaluation metrics and methods.
LLM observability is the process that enables monitoring by providing full visibility and tracing in an LLM application system, as well as newer solutions that automatically surface issues.
Requirements for LLM observability and monitoring
Before getting into the metrics and monitoring measures that will increase the yield of our LLM, you must first gather the data required for this type of analysis.
The LLM inputs and outputs are relatively simple: a prompt and a response. To do any meaningful analysis, you need to create a means to save the prompt, answer, and any other relevant metadata or information in a data store that can be readily accessed, indexed, and analyzed.
This extra metadata might include references to vector resources, guardrail tagging, sentiment analysis, or model parameters created outside the LLM. Whether it’s a basic logging method, dumping the data into an S3 bucket or a data warehouse like Snowflake, or utilizing a managed log provider, you must save this vital information in a useful data source before analyzing anything.
From a resource-use and tracking standpoint, LLMs are identical to any other machine learning model or application service you may monitor. They use memory and use CPU and GPU resources.
Several open-source and managed solutions are available to help you track the resource metrics required to monitor your applications, including Prometheus for metric collection, Grafana for visualization and tracing, and Datadog as a managed platform for both collection and APM.
What can you expect from an LLM observability solution?
Here are several capabilities an LLM observability tool should provide:
- Monitoring model performance – An observability solution should be capable of tracking and monitoring an LLM’s performance in real time using metrics like accuracy, precision, recall, and F1 score (and more specialized ones such as perplexity or token costs in language models).
- Model health monitoring – The solution should be able to monitor the model’s overall health, detecting and alerting it to abnormalities or possibly harmful trends in its behavior.
- Debugging and error tracking – If anything goes wrong, the tool should provide debugging and error tracking tools to assist developers in identifying, tracking, and resolving issues.
- Bias and safety Evaluation – Given the risk of bias and ethical difficulties in AI, any observability solution should contain capabilities for assessing fairness and safety, therefore ensuring that the model’s outputs are impartial and morally sound.
- Interpretability – LLMs can frequently become “black boxes,” providing outputs without obvious logic. A good observability solution should help make the model’s decision-making process more transparent by explaining why a specific output was produced.
- Integration – The tooling should integrate with existing LLMOps tools and workflows, including model building, training, deployment, and maintenance.
8 LLM observability tools 2025
1. Lunary

Lunary is a model-independent tracking tool compatible with Langchain and OpenAI agents. Its cloud service allows you to assess models and prompts against your desired replies.
It also comes with a tool called Radar, which helps categorize LLM answers based on pre-defined criteria, allowing you to revisit them later for analysis.
Pricing
Lunary is offered as free source under the Apache 2.0 license. Note that the free tier only allows for 1,000 daily events. Explore their docs and Github.
2. Langsmith LLM Observability

Langsmith is a commercial offering from Langchain, one of the fastest-growing LLM orchestration projects in its early stages. It was released in July 2023 and has over 100,000 members, making it one of the largest communities for an LLM tool.
Langsmith is a tracing tool built-in with Langchain, so no adjustments are required if you’re a Langchain user. It uploads traces from your LLM calls to its cloud. You can rate your replies manually or with an LLM. It also works with agents that don’t use the blockchain.
Note that Langsmith doesn’t offer a self-hosting option in the self-serve module. The tool offers some cost analysis and analytics, but only for OpenAI usage.
Pricing
While Langchain is open-source, a Github repository for Langsmith is only available for SDKs. This means the tool provides a cloud SaaS service with a free tier of 5K traces monthly. Self-hosting is only offered as an add-on for Enterprise plans.
3. Portkey

Portkey became well-known for its open-source LLM Gateway, which helped abstract 100+ LLM endpoints using a single API. Following that, the team started developing the LLM observability tool.
Portkey is a proxy that lets you keep a prompt library and supply variables in the template to access your LLM. The tool maintains all of your integration’s fundamental parameters, including temperature. It provides tools for caching responses, creating load balancing between models, and configuring fallbacks.
Note that Portkey only logs requests and answers – it doesn’t track requests.
Pricing
Portkey’s free tier allows for up to 10,000 monthly requests.
4. Helicone

Helicone is an open-source LLM observability startup from the YCombinator W23 batch.
Helicone setup involves only two code changes to configure it as a proxy. It supports OpenAI, Anthropic, Anyscale, and a few OpenAI-compatible endpoints. Explore their Github here.
Note that Helicone only logs the requests and answers.
Pricing
Helicone provides a free tier of 50K monthly logs and is open-source under the MIT License.
5. TruLens

TruLens is more focused on the qualitative analysis of LMM responses, providing a feedback feature that carries out the analysis after each LLM call. These feedback features work as a model, evaluating the response to the initial call.
Note that TruLens is only available for Python.
Pricing
TruLens is free source under the MIT License. Cloud services are not self-serviceable. Watch their demo here.
6. Phoenix (by Arize)

Arize is an ML observability platform that supports all ML and LLM model assessment, observability, and analytics. It’s a robust tracking tool compatible with Langchain, LlamaIndex, and OpenAI agents.
You can access Phoenix only as open source with the ELv2 License. It contains a built-in hallucination-detecting tool for your preferred LLM. It also includes an OpenTelemetry-compatible tracing agent. Watch their demo here.
Pricing
Freemium model.
7. Traceloop OpenLLMetry

Traceloop is another YC W23 batch startup that helps monitor LLM models. Instead of relying on a single tool, their SDK OpenLLMetry allows teams to transmit LLM observability data to 10+ various tools.
Traceloop extracts traces straight from an LLM provider or framework, such as Langchain or LLamaIndex, and publishes them in OTel format. Because of its OpenTelemetry format, Traceloop is compatible with various visualization and tracing applications and accessible in several languages. Read their documentation and explore Traceloop Github.
Pricing
Additionally, it provides a backend for accepting these traces. The free-tier product offers 10,000 monthly traces. It’s also open source under the Apache 2.0 license.
8. Datadog

Datadog is an infrastructure and application monitoring software that has expanded its integrations into the world of LLMs and associated tools. It provides out-of-the-box dashboards for LLM observability.
If you currently use Datadog for tracing, you can enable OpenAI usage tracing with a simple flag modification.
Note that Datadog currently supports OpenAI integrations. There is simply a cloud option, like the standard Datadog product. Unlike the other tools described above, Datadog doesn’t support LLM experimentation or iteration.
Their OOB compatibility with integrations, frameworks, and endpoints is restricted to the top few possibilities, such as OpenAI and Langchain.
Pricing
The price you pay depends on the metrics/traces consumption in Datadog. Learn more about how to set up Datadog here.
How to choose the right LLM observability tool?
If you are a startup that has just started experimenting and needs a rapid start to log your LLM integrations, consider the free tier of Langsmith or Portkey to begin tracking. When you go into production and don’t want to transfer your data outside of your environment, you can configure PortKey locally.
If you’re a large organization with high LLM integration volumes and require a dependable tracking system, Langsmith or Datadog are good picks. However, if vendor neutrality matters, use OpenLLMetry for tracing and set your preferred destination.
Wrap up
As LLM tools rapidly evolve, organizations implementing comprehensive observability can significantly enhance their application performance. Continuous tracking of critical metrics like latency, throughput, and response quality allows for quick detection and correction of performance issues. This proactive approach improves model performance and enhances the overall user experience by ensuring smooth and reliable operations.
In-depth observability also boosts explainability and security. Observability increases transparency by providing insights into the inner workings of LLM applications, such as visualizing request-response pairs and internal processes. This helps stakeholders better understand and trust the model’s decisions while quickly identifying errors.
Additionally, continuous monitoring of model behavior can detect potential security threats, enabling organizations to proactively safeguard sensitive data and maintain the integrity of their applications.
【推荐】编程新体验,更懂你的AI,立即体验豆包MarsCode编程助手
【推荐】凌霞软件回馈社区,博客园 & 1Panel & Halo 联合会员上线
【推荐】抖音旗下AI助手豆包,你的智能百科全书,全免费不限次数
【推荐】博客园社区专享云产品让利特惠,阿里云新客6.5折上折
【推荐】轻量又高性能的 SSH 工具 IShell:AI 加持,快人一步
· DeepSeek “源神”启动!「GitHub 热点速览」
· 我与微信审核的“相爱相杀”看个人小程序副业
· 微软正式发布.NET 10 Preview 1:开启下一代开发框架新篇章
· 如何使用 Uni-app 实现视频聊天(源码,支持安卓、iOS)
· C# 集成 DeepSeek 模型实现 AI 私有化(本地部署与 API 调用教程)
2021-02-23 Ensemble methods of sklearn
2020-02-23 Vue2最低支持Node版本调查