University of Kwazulu-Natal

Top 5 Llm Observability Tools Deepchecks Com

Bonisiwe Shabane
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top 5 llm observability tools deepchecks com

Large language models (LLMs) such as GPT-4, Google’s PaLM 2, and Meta’s LLaMA have transformed natural language processing. Still, their application poses major challenges, especially concerning hallucinations, which occur when models provide meaningless responses. According to research in IEEE Software, hallucinations might rise to 21% of LLM results, presenting a hazard in applications where high precision is needed. Monitoring and reducing these problems depend on efficient observability tools, guaranteeing dependability and trustworthiness in LLM systems. Research shows that strong observability techniques can lower hallucination rates by approximately 15%, improving the overall performance and safety of AI systems. In this article, we will present five leading LLM observability tools that help achieve these improvements and examine their features, integration capabilities, and use cases.

Developed by WhyLabs, LangKit is an open-source toolkit intended to track LLMs by collecting important signals from both generated responses and input prompts. This tracking of LLM behavior guarantees outputs that are correct, relevant, and safe. Integration with whylogs represents an important feature of LangKit because whylogs is an open-source data logging library designed for machine learning and AI systems. LangKit’s interoperability with whylogs enables integration into existing monitoring and observability systems, allowing users to leverage whylogs’ robust profiling capabilities for text data. This compatibility provides flexibility in how monitoring data is used, enabling the creation of profiles that can be visualized and tracked within the WhyLabs platform or analyzed separately for detailed insights. Install LangKit using the Python Package Index (PyPI) as advised here:

LangKit modules include user-defined functions (UDFs), which are custom functions designed to process and analyze specific types of data, such as text strings. These UDFs are automatically wired into the collection of UDFs on string features supplied by default via whylogs. All we have to do is import the LangKit modules and create a custom schema, as shown here. Most teams discover that their LLM stack is drifting, leaking PII, or burning tokens during a customer‑visible incident, not from unit tests. Working across different tech companies, I have seen this play out when RAG retrieval falls out of sync with embeddings, when a gateway silently retries into a cost spike, or when guardrails add 150... The fastest fixes come from standard telemetry.

The OpenTelemetry GenAI semantic conventions now define spans, metrics, and events for LLM calls, tools, and agents, which means you can trace prompts, token usage, and tool calls instead of guessing what went wrong. My picks below favor that approach. The Observability Tools and Platforms Market is projected to grow to approximately $4.1 billion by 2028, signaling that AI workloads are reshaping monitoring budgets. I analyzed 14 platforms across LLM tracing, evals, and production monitoring, then narrowed to five that consistently delivered on real‑time visibility, OpenTelemetry alignment, and enterprise deployment options. You will learn where each tool fits, how it impacts latency and cost, and which one saves you the most engineering time in 2025. Thanks for reading devops!

Subscribe for free to receive new posts and support my work. LLM systems fail in ways that are subtle, fast, and expensive, and teams often notice only when incidents hit production. New observability platforms designed specifically for GenAI now help teams trace prompts, measure behavior, and detect failures long before customers feel the impact. Below are the top five tools in 2025 that consistently deliver real-time, OTEL-aligned visibility for modern AI stacks. groundcover brings zero‑instrumentation LLM and agent observability built on eBPF with a Bring Your Own Cloud (BYOC) architecture. Per vendor documentation, it traces prompts, completions, costs, and reasoning paths without SDKs, keeping all data in your VPC.

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.

LLM observability refers to gaining total visibility into all layers of an LLM-based software system, including the application, prompt, and answer. Are your AI systems really under control? In 2025, LLM-powered tools like chatbots and copilots are helping industries work smarter and faster. Hallucinations, bias, and hidden costs can cause serious issues, like bad advice or compliance risks. Without proper monitoring, businesses are flying blind. Mistakes can lead to fines, lost trust, and wasted resources.

That’s why observability & monitoring is a must. LLM applications are everywhere now, and they’re fundamentally different from traditional software. They’re non-deterministic. They hallucinate. They can fail in ways that are hard to predict or reproduce (and sometimes hilarious). If you’re building LLM-powered products, you need visibility into what’s actually happening when your application runs.

That’s what LLM observability tools are for. These platforms help you trace requests, evaluate outputs, monitor performance, and debug issues before they impact users. In this guide, you’ll learn how to approach your choice of LLM observability platform, and we’ll compare the top tools available in 2025, including open-source options like Opik and commercial platforms like Datadog and... LLM observability is the practice of monitoring, tracing, and analyzing every aspect of your LLM application, from the prompts you send to the responses your model generates. The core components include: You already know LLMs can fail silently and burn through your budget.

Without observability, you’re debugging in the dark. With it, you can trace failures to root causes, detect prompt drift, optimize prompts based on real performance, and maintain the audit trails required for compliance. The right observability solution will help you catch issues before users do, understand what’s driving costs, and iterate quickly based on production data. When evaluating observability tools, ask yourself these questions to find the right fit for your needs. Large language models are now ubiquitous in production AI applications. If you don't have some AI feature in 2025, are you even a tech company?

With AI features hitting production, observability has become critical for building reliable AI products that users can trust. LLM observability goes far beyond basic logging, requiring real-time monitoring of prompts and responses, tracking token usage, measuring latency, attributing costs, and evaluating the effectiveness of individual prompts across your entire AI stack. Without robust observability frameworks, teams face significant risks: AI systems may fail silently, generate harmful outputs, or gradually drift from their intended behavior, degrading quality and eroding trust. This guide explores the fundamentals of LLM observability, showing what to prioritize when selecting platforms and discovering the leading observability tools in 2025. At Braintrust, we offer the leading LLM observability platform combining integrations with all major LLMs and AI frameworks, paired with intuitive interfaces that let everyone on your team understand how AI features are functioning. While other solutions may log and store events, Braintrust empowers teams to take action on their logs.

LLM observability monitors Large Language Model behavior in live applications through comprehensive tracking, tracing, and analysis capabilities. LLMs now power everything from customer service chatbots to AI agents that generate code and handle complex multi-step tasks. Observability helps teams understand system performance effectively, detect issues before users notice problems, and maintain operational excellence at scale. Modern LLM observability extends far beyond traditional application monitoring. They track prompts, responses, and token usage. Teams monitor latency and attribute costs accurately.

They analyze error patterns and assess quality. Effective platforms capture complete LLM interaction lifecycles, tracking everything from initial user input to final output delivery, making every step in the AI pipeline visible. LLM observability combines real-time monitoring with historical analysis to give teams a complete picture. Real-time dashboards track current system performance, alert on anomalies, and visualize model behavior as it happens, while historical analysis identifies trends over time, optimizes performance based on patterns, enables compliance reporting, and supports sophisticated... Advanced platforms combine both approaches intelligently, allowing teams to maintain service quality while iterating quickly on improvements. Objective overview with each tool listed.

Observability for large language models enables you to: An OpenTelemetry-compliant SDK for tracing and metrics in LLM applications. A modular observability and logging framework tailored to LLM chains. A proxy-based solution that captures model calls without SDK changes. Large Language Models (LLMs) are quickly becoming a core piece of almost all software applications, from code generation, to customer support automation and agentic tasks. But with outputs that can be unpredictable, how do you prevent your LLM from making costly mistakes?

Looking ahead to 2025, as enterprises deploy LLMs to high-stakes workflows and applications, robust evaluation and testing of models is crucial. This guide covers how to evaluate LLMs effectively, spotlighting leading LLM evaluation software and comparing each LLM evaluation platform based on features and enterprise readiness. Humanloop is an LLM evaluations platform for enterprises. Humanloop’s end-to-end approach ensures teams can perform rigorous LLM testing without compromising on security or compliance. Humanloop enables teams to run LLM Evaluations in their user-interface or in code, by leveraging pre-set or fully customizable evaluators, which can be AI, code or human based. For example, enterprises like Gusto and Filevine use Humanloop to evaluate the accuracy of their agents or to assess AI apps for objective metrics like cost and latency as well as more subjective metrics...

Humanloop is designed to be collaborative, flexible and scalable — making it a leading choice for enterprises who aim to foster and bring technical and non-technical teams together to build AI products and agents... Additionally, Humanloop offers best-in-class Prompt Management features—essential for iterating on prompts outside of the codebase—and robust LLM Observability to continuously track user interactions, model behavior and system health. For enterprises, Humanloop also offers enterprise-grade security, including role-based access controls (RBAC), SOC 2 Type II compliance, and self-hosting deployment options. To build LLM-powered apps, developers need to know how users are using their app. LLM observability tools help them do this by capturing LLM provider requests and generations, then visualizing and aggregating them. This helps developers monitor, debug, and improve their apps.

To help you pick the best of these tools, we put together this list. All of the following products: PostHog is an open source all-in-one platform that combines LLM observability with several other developer-focused tools, such as product and web analytics, session replay, feature flags, experiments, error tracking, and surveys. Its LLM observability product (known as LLM analytics) integrates with popular LLM providers, captures details of generations, provides an aggregated metrics dashboard, and more.

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Subscribe For Free To Receive New Posts And Support My

Subscribe for free to receive new posts and support my work. LLM systems fail in ways that are subtle, fast, and expensive, and teams often notice only when incidents hit production. New observability platforms designed specifically for GenAI now help teams trace prompts, measure behavior, and detect failures long before customers feel the impact. Below are the top five tools in 2025 that consiste...