7 Best LangSmith Alternatives for LLM Devs in 2026

Why look beyond LangSmith

LangSmith, developed by LangChain, provides a platform for debugging, testing, evaluating, and monitoring LLM applications. It integrates with the LangChain framework, offering trace visualization, dataset management, and evaluation capabilities primarily for applications built with LangChain. While effective within its ecosystem, developers may seek alternatives for several reasons. Some teams might operate outside the LangChain framework and require observability tools that offer broader compatibility with different LLM orchestration libraries or custom application stacks. Others may prioritize specific features like advanced MLOps capabilities, deeper integration with traditional ML model monitoring, or more granular control over infrastructure and data residency. Additionally, cost considerations, the need for open-source solutions, or a preference for vendors with established enterprise-grade MLOps offerings can drive the exploration of alternative platforms. The market for LLM development tools is evolving, and specialized solutions often emerge to address niche requirements not fully covered by general-purpose platforms.

Top alternatives ranked

1. 1. Arize AI — Enterprise-grade MLOps for LLM and traditional ML models

   Arize AI is an MLOps observability platform designed for both traditional machine learning models and large language models. It provides capabilities for monitoring model performance, detecting data drift, and debugging predictions in production environments. For LLMs, Arize offers specific features for tracing prompts and responses, evaluating model outputs, and identifying issues like hallucinations or toxicity. Its strength lies in its comprehensive approach to model monitoring, allowing teams to track metrics, analyze model behavior over time, and compare different model versions. Developers can integrate Arize with various LLM providers and orchestration frameworks, making it a flexible option for organizations managing diverse AI portfolios. The platform emphasizes explainability and bias detection, which are critical for responsible AI deployment, and supports enterprise-level deployment with robust security and compliance features.

   • Best for: Enterprises requiring unified observability for both traditional ML and LLM models, comprehensive drift detection, and production debugging.

   See the Arize AI official website.

2. 2. Weights & Biases — Experiment tracking and MLOps for deep learning

   Weights & Biases (W&B) is a development platform for machine learning, widely used for experiment tracking, model versioning, and dataset management. While historically focused on deep learning training and experimentation, W&B has expanded its capabilities to support LLM development through W&B Prompts. This extension allows developers to log, visualize, and evaluate LLM prompts, responses, and chains, offering insights into model behavior and performance. W&B provides tools for comparing different prompts, fine-tuning runs, and tracking metrics relevant to LLMs, such as perplexity or custom evaluation scores. Its strength lies in its comprehensive suite for managing the entire ML lifecycle, from initial experimentation to deployment and monitoring. Teams can leverage W&B for collaborative development, ensuring consistent tracking and reproducibility across projects. The platform is popular among researchers and engineers working on complex deep learning and generative AI tasks.

   • Best for: ML engineers and researchers needing robust experiment tracking, model versioning, and collaborative MLOps for deep learning and LLM development.

   See the Weights & Biases official website.

3. 3. Helicone — Open-source observability for LLM APIs

   Helicone offers an open-source platform for proxying, caching, and observing LLM API calls. It provides developers with visibility into their LLM interactions, enabling them to track usage, monitor performance, and debug issues. Helicone's core features include request logging, response caching to reduce costs and latency, and a dashboard for visualizing API traffic and error rates. Being open-source, it offers flexibility for teams that prefer self-hosting or require customization to fit specific infrastructure requirements. The platform supports various LLM providers, allowing for a centralized observation layer across different models. Helicone aims to provide a lightweight yet powerful solution for understanding and optimizing LLM API usage, making it suitable for developers who need transparent control over their LLM integrations without extensive enterprise MLOps overhead.

   • Best for: Developers seeking open-source, self-hostable LLM observability, API proxying, and caching for cost and performance optimization.

   See the Helicone official website.

4. 4. DeepSeek AI — LLM provider with strong code capabilities

   DeepSeek AI is a research company developing large language models, including models optimized for code generation and understanding. While primarily an LLM provider, their focus on high-performance models for specific tasks, particularly in coding, positions them as an alternative for developers who might use LangSmith to evaluate the performance of models from various providers. Developers could use DeepSeek's models directly and implement custom evaluation frameworks or integrate with other observability tools to monitor their performance. DeepSeek's models are known for their efficiency and strong performance in programming-related benchmarks, making them attractive for applications requiring accurate code generation, debugging, or explanation. For teams building code-centric LLM applications, leveraging models from DeepSeek AI might necessitate a different approach to observability and evaluation than a general-purpose tool like LangSmith, often involving custom metrics and testing specific to code quality.

   • Best for: Developers prioritizing high-performance LLMs for code generation and understanding, often combined with custom evaluation pipelines.

   See the DeepSeek AI official website.

5. 5. Qwen-LM (Alibaba Cloud) — General-purpose open-source LLMs

   Qwen-LM, developed by Alibaba Cloud, is a family of open-source large language models designed for a wide range of tasks, including text generation, comprehension, and multi-modal capabilities. Similar to DeepSeek AI, Qwen-LM is an LLM provider rather than an observability platform. However, for developers who choose to build applications using Qwen models, the need for evaluation and monitoring remains critical. Teams might opt for Qwen-LM due to its open-source nature, performance characteristics, or specific language support. In such cases, developers would integrate Qwen models into their applications and then use separate observability tools or build custom evaluation scripts to assess model performance, trace interactions, and manage datasets. The open-source availability of Qwen models allows for greater customization and deployment flexibility, which can be a key driver for developers looking for alternatives to proprietary LLM ecosystems or those seeking to run models on their own infrastructure.

   • Best for: Developers seeking high-performing, open-source LLMs for diverse applications, integrating with external or custom observability solutions.

   See the Qwen-LM official website.

Side-by-side

How to pick

Selecting the right LLM observability and evaluation platform depends on your specific development workflow, existing infrastructure, and team requirements. Consider the following factors:

• LLM Orchestration Framework: If your application is heavily reliant on LangChain, LangSmith offers native and deep integration. If you use other frameworks like LlamaIndex, Haystack, or custom Python code, alternatives like Arize AI or Weights & Biases might provide broader compatibility through their SDKs. Helicone can proxy any LLM API call, offering provider-agnostic observability.
• Scope of Monitoring: For teams managing a mix of traditional machine learning and LLM models in production, Arize AI provides a unified MLOps observability platform. If your focus is primarily on LLM-specific issues like prompt engineering, response quality, and hallucination detection, LangSmith or Weights & Biases (with W&B Prompts) offer specialized tools.
• Experimentation vs. Production: Weights & Biases excels in experiment tracking and managing the ML lifecycle from research to deployment, making it suitable for iterative development and fine-tuning. For production monitoring, debugging, and continuous evaluation of deployed LLM applications, LangSmith and Arize AI offer more dedicated features.
• Open-source Preference: If your team prefers open-source solutions for greater control, customization, or self-hosting capabilities, Helicone is a strong candidate for LLM API observability. DeepSeek AI and Qwen-LM provide open-source models, but you'd need to pair them with separate observability tools.
• Cost and Scalability: Evaluate the pricing models of each alternative in relation to your expected usage. LangSmith offers a free tier, with paid plans scaled by traces. Helicone has a self-hostable option which can be cost-effective for high-volume usage if you manage the infrastructure. Enterprise solutions like Arize AI typically involve custom pricing based on scale and features.
• Integration with Existing Tools: Assess how well each platform integrates with your current CI/CD pipelines, data storage, and other developer tools. A seamless integration minimizes friction and accelerates adoption.
• Specific LLM Provider Needs: If you are primarily working with a specific LLM provider and need deep insights into their API usage or model performance (e.g., OpenAI, Anthropic, Google), look for alternatives that offer robust, tailored integrations or a provider-agnostic proxy like Helicone.
• Team Collaboration: Features like shared dashboards, experiment logging, and annotation capabilities are crucial for collaborative development. Platforms like LangSmith and Weights & Biases offer strong collaborative features for teams working on LLM projects.