Introduction

Observability in general is the ability to understand the internal state of a software system by simply analysing the output. It enables developers to diagnose issues, understand performance bottlenecks and to ensure that the system is functioning as expected.

In context to LLM Observability, it is the ability to continuously monitor, analyse and assess the quality of the outputs produced by an LLM application in a production environment. Since LLMs exhibits non-deterministic behavior, observability becomes important to track and analyze the LLM model’s output over time, detect performance regressions, latency issues, failures, or evaluate the quality and consistency of the responses.

For a deeper understanding of how observability is evolving alongside LLM deployment follow our in-depth guide on LLM observability and monitoring in 2025 which outlines the core principles, technical challenges, and implementation patterns shaping the field today [1]. This conceptual foundation is the brought to life in our customer support benchmarking case study, where multiple LLMs like GPT‑4o and Claude 3.5 were monitored in a real-world chatbot deployment [2]. Complementing to these operational insights is our overview of the top 5 LLM evaluation tools, which highlights how observability and structured evaluation together enable continuous improvement in LLM performance across diverse use cases [3].

Core Components of LLM Observability

In order to effectively monitor and debug LLM application, it is important to understand the building blocks of observability. Below are those core building blocks that defines how information is captured, structured and analysed across the lifecycle of an LLM application:

• Spans: It is the single unit of work executed by an LLM Application. Such as single call to a chain.

• Traces: It is the collection of spans that are required for a single operation. For example, let say you call a chain, that chain then calls an LLM, and that LLM calls a tool, all these would be part of a single trace.

• Project: Collection of traces. It provides a structured way to manage multiple traces, ensuring observability is organized across different applications, use cases, or deployments.

Why LLM Observability is Needed?

Following points highlights the key reason as to why the observability is critical when working with LLM applications, especially in production environments:

• LLMs produces non-deterministic outputs, it means that a same input can produce different outputs if passed multiple times. This results in an unpredictable behaviour in LLM applications that is hard to reproduce, catch and debug.

• Observability enables complete traceability of LLM operations by capturing inputs, outputs and other intermediate steps, thus making it possible to revisit and analyse those spans that led to the unexpected result.

• Since observability is a continuous monitoring phenomenon, it helps in detecting variation of output over time. Thus improving LLM application with time.

• Observability performs quantifying of the LLM output at scale by incorporating various eval metrics, Thus enabling teams with objective performance tracking over time.

• Along with logging and evaluating, observability is useful in anomaly detection such as latency, token usage or cost. Teams can even setup custom alert when latency, cost or token usage crosses certain threshold or an eval metric fails in production.

Now that we have seen the importance of LLM observability, we will now compare top 5 LLM monitoring tools of 2025.

Tool 1: Future AGI

It is an end-to-end observability and evaluation platform designed to ensure the reliability, performance and accountability of LLM applications at scale. It combines real-time monitoring, evaluations, anomaly detection and tracing into a unified system. By integrating observability with evaluation workflows and prototyping capabilities, Future AGI enables teams to streamline debugging, accelerate iteration cycles, and maintain consistent, production-grade model performance across diverse deployment environments.

The following sections outlines the key components of Future AGI's observability feature. These capabilities are designed to support complete lifecycle of an LLM applications in production environment ranging from real-time monitoring and evaluation to cross-framework tracing and alerting.

Image 1: Future AGI's integration in GenAI Lifecycle; source: https://docs.futureagi.com

1.1 Real-Time Monitoring

• Captures metrics such as latency, cost, token-usage, eval scores at every LLM interaction, thus enabling monitoring of LLM application in production environment.

• Using session management, teams can group and then analyse LLM application that has multi-turn interaction, such as in chatbot.

1.2 Alerts and Anomaly Detection

• Teams can setup custom alerts by defining allowed threshold for latency, cost, token usage or evaluation scores.

Click here to learn how to setup custom alerts

• This alert will trigger when this threshold is breached and the teams will receive an email.

• This feature detects and notifies the stakeholder about the degradation in model performance in production.

1.3 Automated Evaluation

• LLM outputs are automatically evaluated using the existing 50+ built-in eval templates or on custom eval. You can choose from this pre-configured 50+ eval templates and can also set evals for custom metrics.

Click here to learn about all the evals provided by Future AGI

• Prototyping is provided as way to experiment your LLM application to benchmark prompt chains before going into production.

Click here to learn more about prototyping in Future AGI

1.4 Platform Agnostic Open-Source Tracing Support

• traceAI is an open-source python package, maintained by Future AGI, that enables tracing of any application as it is complimentary to OpenTelemetery .

• Although traceAI is natively supported by Future AGI, it can also be used with any SDK and frameworks as long as it has OpenTelemetery compatible backend. Framework such as LangChain, OpenAI, Anthropic, VertexAI, CrewAI, and other such frameworks are supported by traceAI

• In addition to Python, Future AGI also support TypeScript SDK through @traceai NPM package.

Click here to learn more about traceAI Package in Python and TypeScript

• For enterprises who wants granular insights, Future AGI provides fine-grained manual control by creating manual tracing with OpenTelemetery API. Users can create parent/child spans, propagate context in async/threaded workloads, and enrich traces with custom metadata.

1.5 User-Experience

Image 2: Dashboard from Future AGI platform showcasing deployed LLM application

• Provides 10 span kinds (CHAIN, LLM, TOOL, RETRIEVER, EMBEDDING, AGENT, RERANKER, UNKNOWN, GUARDRAIL, EVALUATOR). In the user interface, these span types are clearly differentiated, allowing for precise filtering and detailed trace analysis

• Provides prototyping as a way to experiment and finalise configuration of LLM application before making it live in production, thus giving more confidence in deployment.

Tool 2: LangSmith

Developed by the creator of LangChain, LangSmith is yet another end-to-end observability platform, providing features from prototyping to monitor production LLM application. While its design is optimized for LangChain-native tools and agents, LangSmith also supports broader use cases through flexible instrumentation and telemetry export. The following sections outline LangSmith’s core capabilities, including full-stack observability, and enterprise-ready alerting and notification systems.

Image 3: LangSmith's AI Application Stack; source: https://docs.smith.langchain.com

2.1 Trace Python or JS Code

• LangSmith also provide support to both Python and TypeScript code through its built-in @traceable decorator and traceable() utility.

• Even though LangSmith offers native integration with the LangChain ecosystem, it can also be used independently to trace custom logic, REST endpoints, third-party model calls, and utility functions.

Click here to learn more about LangSmith traceable support

2.2 End to End Observability

• LangSmith recently started supporting full OpenTelemetery integration, meaning accepting and exporting OTel-compliant traces via SDK.

• While traceAI is designed from start as a general-purpose, OpenTelemetry-native toolkit, which is ideal for vendor-neutral observability pipelines, LangSmith offers a tightly integrated experience which is optimized for LangChain workflows, agent debugging, and LLM-specific trace enrichment.

2.3 Alerts and Notification

• Within each project, LangSmith offers threshold-based alerts.

• LangSmith offers enterprise-grade alerting system via PagerDuty and flexible webhook. This is ideal if you want integrated incident workflows and real-time response.

Tool 3: Galileo

Galileo began as a debugging tool for NLP models and has since matured into a purpose-built observability platform for production-scale LLM pipelines. It streamlines tracing insights without requiring complex telemetry configurations. The following sections detail its core capabilities, including workflow-based observability, alerting mechanisms, and evaluations for RAG workflows.

Image 4: Galileo's GenAI Studio; source: https://docs.galileo.ai/galileo*

3.1 Workflow-Based Observability

• Galileo provides streamlined observability experience which is easy to adopt for LLM-specific use case. Useful for teams that want structured insights within the Galileo UI without configuring trace propagation, exporters, or any external backends.

• This workflow-centric design is ideal for teams seeking quick visibility into model behavior and performance without the complexity of managing a full telemetry stack, making it especially effective for fast-moving AI teams focused on rapid iteration and deployment.

3.2 Alerts and Notification

• Galileo also offers alerts to teams based on the both system-level metric (latency, cost, token usage, etc) and evaluation metric (correctness, context adherence, etc).

• These alerts are delivered via email or Slack notification. While not as deeply integrated with enterprise incident management tools like PagerDuty, which is provided by LangSmith.

3.3 Streamlined Chunk-Level Eval for RAG Workflow

• Galileo provides easy to monitor RAG workflow by automatically tracking chunk-level metrics such as Context Adherence or Chunk Utilization as soon as teams integrate its SDK. This allows teams to gain such insights without any additional setup.

• This automated evaluation capability allows teams to monitor the relevance and effectiveness of retrieved content in real time, making it easier to identify grounding issues and optimize retrieval strategies across large-scale RAG deployments.

Tool 4: Arize AI

Arize AI is an enterprise-grade, vendor-agnostic observability platform built to support large-scale LLM operations. It offers robust tracing, evaluation, and alerting capabilities designed to meet the scalability and flexibility requirements of modern AI-driven organizations. The following sections highlight its core features, including OpenTelemetry-based tracing, enterprise alerting workflows, and evaluations.

Image 5: Arize AI's GenAI Lifecycle; source: https://docs.arize.com

4.1 OpenTelemetry-Native Tracing

• Arize leverages OpenTelemetery for tracing LLM operations, thus ensures interoperability and portability.

• This makes it perfect for teams looking to integrate with vendor-neutral observability stacks.

4.2 Alerts and Notifications

• Also provides robust mechanism to alert teams about any performance drift or anomalies in metrics such as latency, cost or evaluation scores.

• Integrates with popular industry standard incident management platforms such as Slack notification, PagerDuty, or OpsGenie.

4.3 Evaluation on Traces

• Like most of the LLM observability platforms, Arize can also be used to assess LLM output quality by using metrics such as correctness or context relevance, depending on the use-case.

• These evals helps in detecting low-performing LLM interactions at scale. This enables teams to continuously monitor and debug LLM behavior in production without manual inspection.

• However Arize lacks a dedicated prototyping module to simulate and benchmark full prompt chains before deploying LLM application into production. On the other hand, Future AGI provides Prototyping that allows teams to simulate multi-step workflows, run evaluations, compare configurations and confidently ship the most effective setup into production. This helps reduce failure rates, improves deployment confidence, and ensures that only well-tested prompt chains are shipped.

Tool 5: Weave (from W&B)

Weights & Biases (W&B), a widely adopted platform in the MLOps ecosystem, has extended its capabilities to support LLM observability through its new offering, Weave, which supports LLM observability, making it an LLMOps tool. The sections below explore its core strengths, including an intuitive UI, streamlined tracing, and current limitations in OpenTelemetry compatibility.

Image 6: Weave AI; source: https://weave-docs.wandb.ai/*

5.1 Intuitive UI for Traces, Runs and Experiments

• Provides developer-friendly user interface to visualise each execution in forms of runs, which can be organised as a project and then can be used to compare them.

• Teams already familiar with W&B for ML and AI model tracking can adopt quickly. This reduces friction and better onboarding.

5.2 Streamlined Real-Time Tracing

• W&B’s Weave makes it easier for developers to instrument their code with minimal efforts. This is done by applying the @weave.op decorator to functions it will automatically capture input, output and metadata, constructing a hierarchical trace of function executions.

• Since Weave does not generate these spans using the OpenTelemetry API, this may limit flexibility for those teams aiming to export traces to other OTEL-compatible backends like Jaeger or Prometheus. This can pose challenges for organizations who are seeking a unified observability strategy across different systems.

Side-by-Side Comparison

Table 1: In-depth comparison of Future AGI, LangSmith, Galileo, Arize AI, W&B Weave

Key Takeaways

• If you are someone who is looking for full compatibility with the OpenTelemetery ecosystem, which includes supports for standard exporters such as OTLP, Jaeger, and Prometheus, and want a vendor-neutral, cloud-agnostic approach to tracing across LLM and non-LLM components alike, then traceAI from Future AGI is a better choice.

• For teams already deeply embedded in LangChain ecosystem, LangSmith can be a better choice. However this close coupling of LangChain and LangSmith can become an issue if the team ever seeks smoother interoperability experience. LangChain has drawn criticism for frequent breaking changes, dependency bloat, and evolving APIs, which can hinder long-term maintainability. [9]

• Teams looking for LLM observability platform with minimal setup then Galileo comes as an alternative. But keep in mind that lack of support for OpenTelemetery may cause an issue for teams building vendor-neutral, cloud-agnostic observability pipelines.

• Arize comes as a strong choice for teams looking for scalability, flexibility and vendor-agnostic observability due to its support for OpenTelemetery support. However Arize currently lacks a prototyping environment to simulate, benchmark, and iterate on LLM application logic before deployment. This can be limiting the experimentation workflows and forces teams to evaluate changes post-deployment.

• If your team had already used W&B for ML experimentation and is now expanding for LLM observability, then Weave can be a smoother onboarding experience. However its lack of support for OpenTelemetry limit flexibility if you are aiming to ever export traces to other OTEL-compatible backends and be platform-agnostic and future-proof.

Conclusion

The demand for a observability platform continues to grow as the LLM application shift from research prototypes into production systems. It is not sufficient enough to just trace function calls and log responses. Teams need a comprehensive end-to-end insight into the behaviour of LLM model, its cost pattern, performance and evaluation scores at scale.

Each tool compared in this blog brings distinct strengths but what distinguishes Future AGI is its comprehensive OpenTelemetry-native observability stack combined with native evaluation and prototyping support. By combining tracing, evaluation, alerting, and pre-deployment experimentation into a single low-code platform, it empowers teams to ship more reliable and better-optimized LLM applications confidently at scale.

Click here to learn how FutureAGI can help your organization build high-performing, trustworthy AI systems at scale. Get in touch with us to explore the possibilities.

Reference

[1] https://futureagi.com/blogs/llm-observability-monitoring-2025

[2] https://futureagi.com/customers/benchmarking-llms-for-customer-support-a-3-day-experiment

[3] https://futureagi.com/blogs/top-5-llm-evaluation-tools-2025

[4] https://docs.futureagi.com

[5] https://docs.smith.langchain.com

[6] https://docs.galileo.ai

[7] https://docs.arize.com

[8] https://weave-docs.wandb.ai

[9] https://news.ycombinator.com/item?id=40739982