Marketing
July 13, 2025
This article provides a comprehensive comparison of leading LLM observability platforms to help you navigate the landscape beyond LangSmith. Talk to our experts to determine the best observability tool for your AI-powered mobile app.
Chris Fitkin
Founding Partner
The rise of Large Language Models (LLMs) has revolutionized application development, but with great power comes great complexity. Ensuring your LLM applications are reliable, performant, and cost-effective requires a specialized set of tools for monitoring and analysis—a practice known as LLM observability. LangSmith, from the creators of the popular LangChain framework, has quickly become a go-to solution in this space. However, the market is burgeoning with a diverse array of competitors and alternatives, each tailored to specific needs, from deep model explainability to open-source flexibility.
Choosing the right observability platform is not just a technical decision; it’s a strategic one that impacts your development lifecycle, operational efficiency, and ability to scale. Whether you prioritize full data control with a self-hosted solution, an all-in-one platform that bridges technical and non-technical teams, or a budget-friendly tool focused on user engagement, there is a LangSmith alternative designed for you. This guide will provide a comprehensive overview of the LLM observability landscape, starting with an introduction to LangSmith itself before diving into a detailed comparison of its top competitors.
LangSmith, released in July 2023, is the commercial observability offering from LangChain, the widely adopted framework for building LLM applications. With a rapidly growing community of over 100,000 members, LangSmith has established itself as a formidable player. Its core value proposition lies in its tight integration with the LangChain ecosystem. For developers already using LangChain, adopting LangSmith is seamless; no significant code adjustments are required to begin uploading traces from LLM calls to its cloud platform.
The platform is designed around the concept of unified testing and observability. This powerful paradigm allows development teams to capture real user interaction data and transform it into evaluation datasets. By doing so, they can uncover issues that traditional monitoring and testing tools might miss, leading to more robust and reliable applications. LangSmith allows users to rate LLM replies either manually or by using another LLM, providing a flexible feedback loop for continuous improvement.
From a technical standpoint, LangSmith is API-first and OpenTelemetry (OTEL) compliant, which means it can complement existing DevOps investments rather than requiring a complete overhaul. It provides a cloud-based SaaS service with a free tier that includes 5,000 traces per month, making it accessible for smaller projects and individual developers. However, its focus is primarily on its cloud offering. A self-hosting option is available, but it is reserved as an add-on for Enterprise plans, which can be a significant consideration for organizations with strict data residency or security requirements. Furthermore, while LangSmith offers some cost analysis and analytics, these features are currently limited to OpenAI usage, which may not suffice for teams leveraging a variety of model providers.
While LangSmith is an excellent choice for many, especially those embedded in the LangChain ecosystem, it is essential to understand the alternatives. The LLM observability space offers a rich tapestry of solutions, from comprehensive platforms to specialized open-source frameworks, each with unique strengths.
The landscape of LangSmith alternatives is diverse, offering solutions that range from open-source frameworks providing maximum control to comprehensive observability platforms designed for enterprise scale. Each tool is engineered with a specific philosophy, catering to different needs such as model explainability, user engagement analytics, or cost-effective scalability. Let’s explore the leading contenders.
Launched in February 2024, Orq.ai has quickly positioned itself as a strong contender among LangSmith alternatives by offering a comprehensive, end-to-end solution for managing the entire AI application lifecycle. It’s not just an observability tool but an advanced Generative AI Collaboration Platform designed to help teams develop, deploy, and optimize LLM applications at scale.
One of Orq.ai’s most significant differentiators is its focus on collaboration, aiming to bridge the gap between technical and non-technical team members. This makes it easier for everyone, from engineers to product managers, to collaborate on AI projects and deploy them effectively.
| Feature | Orq.ai | LangSmith |
|---|---|---|
| Primary Focus | All-in-one platform for development, deployment, and optimization | Unified testing and observability |
| Model Integration | Seamless integration with 130+ AI models from leading providers | Works with various agents, but cost analysis is limited to OpenAI |
| Deployment & Testing | Playgrounds & Experiments for controlled testing of models, prompts, and RAG pipelines | Unified testing from real user data |
| Security & Compliance | SOC2 certified; compliant with GDPR and the EU AI Act | Enterprise plans available, specific certifications not listed |
| Collaboration | Designed to bridge gap between technical and non-technical teams | More developer-focused, especially for LangChain users |
| Evaluation | Integrates programmatic, human, and custom evaluations | Allows manual or LLM-based rating of replies |
Orq.ai provides a powerful suite of tools that streamline development from the ground up. Its Playgrounds & Experiments feature allows teams to run controlled sessions to test and compare different AI models, prompt configurations, and even Retrieval-Augmented Generation (RAG)-as-a-Service pipelines. This flexibility is enhanced by seamless integration with over 130 AI models, empowering teams to experiment and select the best-fit model for any use case.
For deployment, Orq.ai ensures dependability with built-in guardrails, fallback models, and regression testing. Post-deployment, teams can monitor AI models in real time using detailed monitoring and intuitive dashboards. Its model drift detection tools are crucial for identifying and correcting subtle changes in model behavior over time. On the security front, Orq.ai meets stringent data security and privacy requirements, boasting SOC2 certification and compliance with both GDPR and the EU AI Act.
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For teams that prioritize open-source solutions and customization, Helicone presents a compelling alternative. An alumnus of the YCombinator W23 batch, Helicone is an open-source framework designed specifically for developers who need to efficiently track, debug, and optimize their LLMs. Its architecture is built for flexibility, offering both self-hosted and gateway deployment options.
This flexibility allows teams to scale their observability efforts without sacrificing control over their data or performance. Helicone is particularly well-suited for developers who want to get their hands dirty and tailor the platform to their specific needs.
| Feature | Helicone | LangSmith |
|---|---|---|
| Model | Open-source (MIT License) | Commercial offering |
| Deployment | Self-hosted (on-premise) and cloud gateway options | Primarily cloud SaaS; self-hosting is an Enterprise add-on |
| Pricing | Flexible, volumetric pricing model based on usage | Free tier of 5K traces/month; paid plans |
| Core Function | Logs requests and answers; tracks multi-step workflows with Sessions | Unified testing and observability; turns user data into test sets |
| Features | Prompt versioning, user segmentation, text and image I/O support | OTEL-compliant, built-in tracing for LangChain |
| Target Audience | Developers and teams preferring open-source and customization | LangChain users and teams looking for an integrated platform |
Setting up Helicone is straightforward, requiring only a couple of code changes to configure it as a proxy. It currently supports OpenAI, Anthropic, Anyscale, and a few other OpenAI-compatible endpoints. While its core function is to log requests and answers, it provides powerful features for deeper analysis. With Sessions, developers can track and visualize multi-step workflows across different agents. It also supports prompt versioning, allowing teams to test and compare various prompt configurations systematically.
One of Helicone’s most attractive aspects is its pricing. The flexible, volumetric model makes it a budget-friendly and cost-effective option for both small teams and larger enterprises looking to scale efficiently. Its free tier is also generous, allowing for up to 50,000 monthly logs.
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Phoenix, a product of the established ML observability platform Arize AI, carves out its niche by focusing on the deep, granular details of model performance. It is a specialized, open-source platform designed to help teams monitor, evaluate, and optimize their AI models at scale, with a strong emphasis on model explainability and drift detection. For organizations working with high-stakes AI systems where trust and transparency are paramount, Phoenix offers a robust solution.
Phoenix provides advanced tools for tracking and improving the performance of large-scale AI systems. It is particularly valuable for teams that need to dive deeper than standard observability metrics and understand the why behind their model’s behavior.
| Feature | Phoenix by Arize AI | LangSmith |
|---|---|---|
| Primary Focus | Model explainability, drift detection, and deep performance insights | Unified testing and observability across the application lifecycle |
| Scope | Narrower, specialized focus on evaluation and diagnostics | Broader, more of an all-in-one observability platform |
| Key Features | Excels at model drift detection, built-in hallucination detection, explainability tools | Turns user data into test sets, integrated tracing with LangChain |
| Deployment | Accessible as open source (ELv2 License) | Primarily a commercial cloud SaaS product |
| Missing Features | Does not offer prompt templating or full deployment capabilities | Includes prompt management and is part of a broader development framework |
| Compatibility | Robust tracking tool compatible with LangChain, LlamaIndex, OpenAI agents | Native integration with LangChain |
Phoenix truly excels at identifying when a model begins to deviate from its expected behavior due to changes in data patterns—a phenomenon known as model drift. Its insights, coupled with powerful explainability features, help teams maintain trust and transparency in their AI systems. The platform includes a built-in hallucination-detecting tool and an OpenTelemetry-compatible tracing agent, making it a robust tracking tool.
However, its specialization is also its main limitation. Phoenix’s narrower focus may not cater to teams looking for an all-in-one solution. It does not offer a broader set of observability features like prompt templating or full deployment capabilities found in platforms like LangSmith or Orq.ai. It is the perfect tool for deep-dive analysis but may need to be paired with other tools for complete lifecycle management.
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Langfuse has earned its reputation as the most used open-source LLM observability tool, offering a powerful and transparent platform for teams seeking an alternative to commercial offerings like LangSmith. It provides comprehensive tracing, evaluations, prompt management, and metrics to help developers debug and improve their LLM applications.
Its core philosophy is built on being model and framework agnostic, combined with a commitment to open-source principles and self-hosting. This makes Langfuse a highly attractive option for organizations that prioritize customization, data security, and full control over their deployment environments.
| Feature | Langfuse | LangSmith |
|---|---|---|
| Model | Open-source (Apache 2.0 License), community-driven | Commercial, product-driven |
| Deployment | Strong self-hosting architecture and a managed cloud service | Primarily cloud SaaS; self-hosting is an enterprise feature |
| Agnosticism | Model and framework agnostic | Deeply integrated with LangChain, which is its primary strength |
| Features | Detailed tracing, prompt templating, human/AI evaluation, metrics dashboards | Unified testing and observability, turns user data into evaluations |
| Community | Backed by a vibrant open-source community that drives rapid evolution | Large user base, but development is commercially directed |
| Data Control | Self-hosting ensures full control over data and environments | Data is managed within the LangSmith cloud platform |
Langfuse ensures end-to-end visibility with features like detailed tracing of LLM calls, robust evaluation capabilities (supporting both human and AI-based feedback), a centralized prompt management system, and performance metrics dashboards. Its prompt templating tools streamline the process of creating, testing, and optimizing prompts, a crucial part of the LLM development workflow.
Being open source under the Apache 2.0 license and backed by a vibrant community means the platform evolves rapidly based on user feedback. It also integrates well with a variety of tools, including OpenTelemetry, LangChain, and the OpenAI SDK. For teams that want to tailor their observability platform to their exact needs and maintain control over their infrastructure, Langfuse is an ideal solution. However, this control comes with responsibility.
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The LLM observability market is rich with options, and several other tools offer unique value propositions worth considering.
Navigating the crowded landscape of LLM observability tools can be a daunting task. The decision between LangSmith and its many competitors depends on a complex interplay of factors: your team’s technical expertise, your application’s specific needs, your budget, your long-term scalability goals, and your data security requirements. This is where we, as experienced technology partners, can provide immense value.
With over 20 years of experience in app development and more than 120 successful projects launched, we at MetaCTO have the deep technical expertise required to guide you through these critical decisions. Our work in AI development and AI-enabled mobile app development has given us firsthand experience with the challenges of building, deploying, and maintaining robust LLM applications. We understand that the right observability tool is the bedrock of a successful AI product.
We can help you evaluate each platform against your unique criteria.
As fractional CTOs, we provide the strategic technical leadership to make these choices with confidence. Once a decision is made, our development team can seamlessly integrate the chosen service—whether it’s LangSmith, Orq.ai, or any other competitor—into your application, ensuring you have the visibility you need to succeed from day one.
The journey to building a successful LLM application does not end at deployment. Continuous monitoring, evaluation, and optimization are critical for long-term success, and choosing the right observability platform is a cornerstone of this process. LangSmith offers a powerful, well-integrated solution, especially for teams already utilizing the LangChain framework. Its ability to unify testing and observability provides a streamlined workflow for debugging and improving applications.
However, the ecosystem of LangSmith alternatives offers a rich spectrum of capabilities tailored to diverse needs. Orq.ai provides an all-in-one, collaborative platform perfect for teams seeking end-to-end lifecycle management. Open-source powerhouses like Langfuse and Helicone grant unparalleled control and customization for technically adept teams with specific data security needs. For those requiring deep diagnostic insights, Phoenix by Arize AI delivers best-in-class model explainability and drift detection. Finally, tools like HoneyHive cater to user-centric startups by focusing on engagement analytics and cost-effectiveness.
The best choice is not universal; it is deeply personal to your project’s goals, your team’s structure, and your operational constraints. Navigating this landscape requires careful consideration and expert guidance.
Ready to implement the perfect LLM observability solution for your app and gain the insights you need to scale with confidence? Talk to an expert at MetaCTO today, and let’s build the future of your AI application together.
Last updated: 13 July 2025
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