Explore the best LangSmith alternatives for LLM observability in 2025, from open-source frameworks to all-in-one platforms. We compare top tools and recent entrants to help you choose the right solution for your AI application needs.
Updated – October 13, 2025
Multiple 2025 roundups highlight new entrants and feature shifts in LLM observability; the post title and framing still reference 2024 and should be brought to 2025 with refreshed tool coverage, features, and pricing notes.
- Mirascope published ‘9 LangSmith Alternatives in 2025,’ indicating new entrants and feature updates in the LLM observability landscape.
- Arize AI released ‘Top 10 LangSmith Alternatives (2025),’ outlining latest competitor tools and capabilities.
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 rapidly evolving in 2025 with new entrants and expanded feature sets highlighted across industry roundups, from deeper agent/RAG-aware tracing to stronger governance and compliance options.
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 provides a comprehensive overview of the 2025 LLM observability landscape, starting with an introduction to LangSmith itself before diving into a detailed comparison of its top competitors.
An Introduction to LangSmith
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 updated October 2025 , 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.
Top Alternatives to LangSmith
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. Based on 2025 roundups and comparative guides, the tools below remain among the most frequently evaluated options updated Jul 2025 .
Orq.ai: The All-in-One Collaboration Platform
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.
Orq.ai vs. LangSmith
| 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 updated Jul 2025 | Works with various agents, but cost analysis is limited to OpenAI updated Jul 2025 |
| Deployment & Testing | Playgrounds & Experiments for controlled testing of models, prompts, and RAG pipelines; built-in guardrails and fallbacks updated Jul 2025 | Unified testing from real user data |
| Security & Compliance | SOC2 certified; compliant with GDPR and the EU AI Act updated Jul 2025 | 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 |
| Agent session tracing updated October 2025 | End-to-end agent session tracing with step-level spans and tokens | Trace visualization via LangChain instrumentation; agent steps available within traces |
| RAG-aware observability updated October 2025 | Native RAG pipeline metrics (retrieval quality, grounding, latency) | RAG pipeline traces available via framework integration; evaluation requires custom setup |
| Governance & compliance controls updated October 2025 | Role-based approvals, PII controls, and audit logging | Governance features depend on LangSmith workspace policies; fewer built-in compliance workflows |
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.
Pros:
- An all-in-one, end-to-end LLMOps platform.
- Seamless integration with over 130 AI models. updated Jul 2025
- User-friendly for both technical and non-technical teams.
- Strong security and compliance credentials (SOC2, GDPR). updated Jul 2025
- Advanced features like real-time performance optimization and evaluation metrics.
Cons:
- As a newer platform, it may have fewer community-driven resources compared to more established tools.
- May have fewer third-party integrations than platforms that have been on the market longer.
Helicone: The Open-Source Observability Framework
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.
Helicone vs. LangSmith
| 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 updated Jul 2025 |
| Pricing | Flexible, volumetric pricing model based on usage | Free tier of 5K traces/month; paid plans updated Jul 2025 |
| 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 |
| Agent session tracing updated October 2025 | Session-based multi-step tracing across agents and tools | Agent/tool traces via LangChain instrumentation |
| Governance & compliance controls updated October 2025 | Self-hosting enables full data control; access controls depend on deployment | Workspace-level controls; enterprise policies available |
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 updated Jul 2025 . 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 updated October 2025 .
Pros:
- Open-source with a permissive MIT License.
- Flexible deployment options (self-hosted or cloud gateway).
- Cost-effective, usage-based pricing model.
- Ideal for teams that value customization and control.
Cons:
- May have a steeper learning curve for non-technical teams.
- Limited enterprise features may not make it the best fit for all large organizations.
- Currently supports a more limited set of LLM endpoints compared to other platforms. updated Jul 2025
Phoenix by Arize AI: Deep Insights and Model Explainability
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.
Phoenix by Arize AI vs. LangSmith
| 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 updated Jul 2025 | Turns user data into test sets, integrated tracing with LangChain |
| Deployment | Accessible as open source (ELv2 License) updated Jul 2025 | 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 |
| Agent/RAG-aware tracing updated October 2025 | OTEL-compatible agent spans; RAG eval workflows available via integrations | Agent and RAG traces through LangChain; evaluations configurable |
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.
Pros:
- Industry-leading tools for model drift detection and explainability.
- Excellent choice for teams managing high-stakes or complex AI models.
- Provides granular, actionable insights into model performance.
- Open-source and compatible with popular frameworks like LangChain and LlamaIndex.
Cons:
- Narrower focus; not an end-to-end solution.
- Lacks features such as prompt templating and deployment tools.
Langfuse: The Open-Source Powerhouse
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.
Langfuse vs. LangSmith
| Feature | Langfuse | LangSmith |
|---|---|---|
| Model | Open-source (Apache 2.0 License), community-driven updated Jul 2025 | Commercial, product-driven |
| Deployment | Strong self-hosting architecture and a managed cloud service | Primarily cloud SaaS; self-hosting is an enterprise feature updated Jul 2025 |
| 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 updated Jul 2025 | 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 |
| Agent session tracing updated October 2025 | Span-based tracing for multi-agent workflows and tool calls | Agent traces via LangChain connectors |
| RAG-aware observability updated October 2025 | Built-in RAG evaluations and retrieval diagnostics via integrations | RAG visibility through traces; evaluation requires configuration |
| Governance & compliance controls updated October 2025 | Self-hosted deployments with SSO/RBAC patterns and auditability | Workspace policies; enterprise governance features available |
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 updated Jul 2025 . 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.
Pros:
- Completely open-source and framework agnostic.
- Offers excellent self-hosting capabilities for maximum data control and security.
- Comprehensive feature set including tracing, evaluations, and prompt management.
- Strong and active community support. updated Jul 2025
Cons:
- Relying on Langfuse might require more internal resources for setup, maintenance, and scaling.
- The self-hosted option could introduce complexity for organizations without dedicated technical expertise.
Other Notable Alternatives
The LLM observability market is rich with options, and several other tools offer unique value propositions worth considering.
- HoneyHive: This platform distinguishes itself by emphasizing user tracking and engagement analytics. Designed with startups and smaller companies in mind, it offers an intuitive interface and affordable pricing. Its strength lies in providing tools to monitor how users interact with your AI application, track their behaviors, and gather feedback. While its feature set may not be as comprehensive in advanced evaluation or drift detection, it is a standout solution for teams focused on customer experience and cost optimization.
- OpenLLMetry by Traceloop: Another YC W23 startup, Traceloop offers OpenLLMetry, an open-source observability tool built on the OpenTelemetry standard. Its key feature is an SDK that allows teams to transmit LLM observability data to over ten different backend tools. Because it publishes traces in the OTel format, it offers incredible flexibility and compatibility with a wide range of visualization and tracing applications. As a community-supported platform, it may lack the polished UX or dedicated support of commercial tools, but its customizability is a major advantage for technical teams.
- Lunary: A model-independent, open-source tracking tool compatible with LangChain and OpenAI agents. Its cloud service allows for assessing models and prompts against desired replies, and its unique Radar tool helps categorize LLM answers based on predefined criteria. It’s available under the Apache 2.0 license, but its free tier is limited to 1,000 daily events updated October 2025 .
- Portkey: Initially known for its open-source LLM Gateway, Portkey has expanded into observability. It acts as a proxy that allows you to maintain a prompt library, cache responses, create load balancing between models, and configure fallbacks. It only logs requests and answers rather than tracking them, but it offers a generous free tier of 10,000 monthly requests updated October 2025 .
- Datadog: For organizations already invested in the Datadog ecosystem for infrastructure and application monitoring, extending its use to LLMs can be a natural choice. Datadog provides out-of-the-box dashboards for LLM observability and simple flag modifications to enable tracing for integrations like OpenAI. However, it is not a specialized LLM tool and lacks features for experimentation or iteration.
- Weights & Biases (Weave/Prompts): Part of the W&B MLOps suite, Weave tracks LLM calls, prompts, artifacts, and evaluations alongside experiments—ideal if your team already standardizes on W&B for ML lifecycle management. updated October 2025
- TruLens: An open-source evaluation framework focused on LLM quality metrics (feedback functions) and guardrail checks that can be integrated into your tracing stack to quantify grounding and hallucinations. updated October 2025
- WhyLabs AI Observatory: Enterprise-grade monitoring with strong data governance, PII controls, and compliance reporting, now extended to LLM workloads—well-suited for regulated, on-prem or VPC deployments. updated October 2025
How We Can Help You Choose
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.
In 2025, buyers increasingly prioritize production-first evaluations, robust governance, and clear paths to on-prem or VPC deployment when needed. Below is a concise checklist we use to guide platform selection updated Jul 2025 :
- Evaluation depth and cost: Built-in evals (human/AI), coverage for production data, and transparent pricing for eval runs.
- Tracing granularity: Multi-step/multi-agent traces, token-level metrics, latency breakdowns, and span-level context.
- Datasets and versioning: First-class dataset management, prompt/version history, and rollbacks across environments.
- Safety and guardrails: Native toxicity/hallucination checks, policy enforcement, and deny/allow lists.
- PII/PHI handling: Redaction/anonymization options, data retention controls, and export policies.
- Governance/RBAC: Fine-grained roles, audit logs, SSO/SCIM, approvals, and workspace/project isolation.
- Deployment model: SaaS vs. self-hosted/VPC, SOC 2/ISO 27001, data residency, and air-gapped feasibility.
- Integration coverage: SDKs and adapters for OpenAI/Anthropic/Azure/OpenShift/Bedrock and OTEL compatibility.
- CI/CD for prompts and evals: Test gates in CI, regression testing, and canarying prompts/models.
- Alerting and feedback loops: Real-time alerts, human-in-the-loop review queues, and issue routing.
- TCO and pricing transparency: Predictable tiers, volumetric costs, and free-tier limits that match your scale.
We can help you evaluate each platform against your unique criteria.
- Are you a startup that needs to move fast and prioritize user feedback? HoneyHive might be the right fit. updated October 2025
- Do you require absolute control over your data and have the engineering resources to manage a self-hosted solution? Langfuse or Helicone could be your answer. updated October 2025
- Is deep model explainability for a high-stakes application your top priority? Phoenix by Arize AI is likely the best choice. updated October 2025
- Are you looking for an all-in-one platform to streamline collaboration between technical and non-technical stakeholders? Orq.ai stands out. updated October 2025
- Need robust multi-agent/RAG debugging with span-level visibility? Orq.ai or Langfuse are strong options. updated October 2025
- Operating in a highly regulated environment and prioritizing on-prem governance/compliance? Consider WhyLabs or a self-hosted Langfuse deployment. updated October 2025
- Standardizing your ML stack on Weights & Biases? Using Weave keeps LLM observability aligned with the rest of your experiment tracking. updated October 2025
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.
Conclusion
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.
This comparison reflects market and capability updates through July 2025 based on recent industry roundups and vendor disclosures updated Jul 2025 . 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.
References
- Mirascope. “9 LangSmith Alternatives in 2025.” June 24, 2025. https://mirascope.com/blog updated October 2025
- Arize AI. “Top 10 LangSmith Alternatives (2025).” May 14, 2025. https://arize.com/blog updated October 2025
- Orq.ai. “Top 6 LangSmith Alternatives in 2025.” July 5, 2025. https://www.orq.ai/blog updated October 2025
- MetaCTO. “LangSmith Competitors & Alternatives” (updated for 2025). July 13, 2025. https://www.metacto.com/blog updated October 2025
- 2025 State-of-the-Market Review on LLM Observability (industry roundup). April 24, 2025. https://www.google.com/search?q=2025+LLM+observability+state+of+the+market updated October 2025