LangSmith vs MLflow: Which Platform Should You Use?
- Leanware Editorial Team
- 2 hours ago
- 7 min read
LangSmith and MLflow solve different problems in the AI dev stack. LangSmith helps developers build, debug, and deploy LLM applications. It offers tracing, evaluation, and monitoring tools to understand how prompts and agents behave in production.
It’s framework-agnostic but works seamlessly with LangChain and LangGraph.
MLflow is an open-source platform for managing the ML lifecycle. It handles experiment tracking, model versioning, and reproducible deployments. While built for traditional ML, it’s increasingly used in LLM workflows.
LangSmith focuses on application observability, while MLflow manages model lifecycle and infrastructure.
Let’s look at how they compare and when each makes sense in production.
Why compare LangSmith and MLflow?
AI teams face different infrastructure needs. If you’re building chatbots, agents, or RAG pipelines, you need tools to trace prompts, evaluate responses, and debug model behavior. For traditional ML, experiment tracking, model versioning, and reproducible deployments matter more.
LangSmith and MLflow approach these needs from different angles, each designed for a specific kind of workflow.
What is LangSmith?
LangSmith is a platform for developing, debugging, evaluating, deploying, and monitoring LLM applications. It launched as a commercial product from the LangChain team to address production challenges that the open-source framework doesn’t cover.
The platform is framework-agnostic. You can use it with LangChain, LangGraph, or any other setup, including custom code that calls LLM APIs directly.
LangSmith provides tools for tracing requests, evaluating outputs, testing prompts, and managing deployments in one place. You can prototype locally and then move to production with integrated monitoring and evaluation, combining observability, testing, and deployment in a single workflow.
Core Features and Capabilities
LangSmith's tracing system records every step in your LLM workflow. Each trace shows the full execution graph: which functions ran, their inputs and outputs, latency for each step, and total tokens used. You can drill into any trace to debug failures or understand unexpected behavior.
Evaluation lets you run your application against test datasets, measure performance, and compare runs. It supports automatic evaluation with LLMs as judges and human feedback collection.
Dataset management helps turn production traces into test cases, tracking which pass or fail across versions.
Deployment allows agents to run as scalable servers, handling long-running, stateful workflows with discovery, reuse, configuration, and team sharing.
LangSmith Studio provides a visual interface to design, test, and refine applications before coding, speeding up iteration for complex workflows.
Monitoring dashboards display error rates, latency, token usage, and cost, with alerts for threshold breaches or recurring errors.
Compliance covers HIPAA, SOC 2 Type 2, and GDPR standards for enterprise deployments.
Target use cases and ideal users
LangSmith targets developers building LLM applications: chatbots, agents, RAG pipelines, or any system where you're chaining LLM calls together. Early-stage startups prototyping with LLMs benefit from the quick setup and integrated tooling.
Teams using LangChain or LangGraph get automatic instrumentation without code changes. Teams using other frameworks can manually instrument their code using LangSmith's SDK.
What is MLflow?
MLflow is an open-source platform for managing the entire lifecycle of AI and machine learning models. Built by Databricks, it supports both traditional ML and LLM workflows. You can track experiments, manage models, version prompts, and deploy applications from one place.
For LLM and GenAI developers, MLflow includes tracing, evaluation, and prompt management to debug and monitor model behavior.
For data scientists, it provides experiment tracking, a model registry, and deployment tools that integrate with Docker, Kubernetes, Azure ML, and AWS SageMaker.
It supports major ML frameworks like scikit-learn, PyTorch, and TensorFlow, and runs locally, on-premise, or through managed cloud services such as Databricks, AWS, and Azure ML.
Core features and capabilities
Experiment Tracking: Log parameters, metrics, artifacts, and code to compare and reproduce runs.
Model Registry: Manage model versions across staging, production, and archive states.
Model Packaging: Deploy models across platforms without changing code.
Projects: Package experiments for consistent, reproducible runs.
Tracing & Observability: Debug and monitor LLM and agent workflows with built-in tracing.
LLM Evaluation: Evaluate model outputs and prompts automatically across versions.
Prompt Management: Version, track, and reuse prompts for consistent development.
Broad Integration: Supports frameworks like scikit-learn, PyTorch, TensorFlow, OpenAI, and LangChain.
Target use cases and ideal users
MLflow fits teams running traditional ML workloads like classification, forecasting, and computer vision. Data scientists use it to track experiments, while ML engineers rely on it for versioning and deployments. MLOps teams managing many models use its registry and orchestration tools to keep workflows consistent.
It also supports LLM development through tracing and evaluation features, though that’s still a newer part of the platform.
Feature-by-Feature Comparison
LangSmith and MLflow solve different problems but share some overlap where teams might use both.
1. Experiment tracking and versioning
MLflow tracks traditional ML runs - parameters, metrics, and artifacts—with tools to compare and reproduce results.
LangSmith traces prompt executions and LLM calls, focusing on debugging and evaluation rather than tuning. Use MLflow for model training and LangSmith for testing prompts or agent behavior.
2. Model management and deployment
MLflow manages model versions through staging and production, supporting Docker, Kubernetes, and cloud platforms.
LangSmith deploys LangGraph agents that handle conversational and stateful workflows. Choose MLflow for general ML models and LangSmith for deploying LLM agents.
3. LLM workflow support
LangSmith was built for LLMs, offering native tracing, evaluation, and debugging for chains and agents.
MLflow added LLM support recently through integrations with OpenAI, LangChain, and others, but it remains secondary to its ML focus.
4. Integration
LangSmith integrates tightly with LangChain and LangGraph and supports others via SDKs.
MLflow works across ML and LLM frameworks without bias, making it better suited for mixed environments.
Deployment and licensing
MLflow is open-source and self-hostable under Apache 2.0, with managed options from major clouds. LangSmith is SaaS-based, with open SDKs but a closed backend. Choose based on your preference for control or convenience.
Pricing
MLflow is open-source and free to use, with costs limited to hosting and infrastructure.
LangSmith follows a usage-based pricing model. The Developer plan is free for one seat with up to 5k traces per month.
The Plus plan costs $39 per seat/month, including higher trace limits and one development deployment.
The Enterprise plan offers custom pricing with options for hybrid or self-hosted setups, SSO, and dedicated support.
How They Fit Into LLM & MLOps Workflows
LangSmith is used during development to trace prompts, debug responses, and monitor agent behavior. It helps you understand why a model produced a certain output and test new prompt versions before deployment.
In production, you can track latency, token usage, and errors to identify performance issues.
MLflow is used in training and deployment pipelines. It tracks experiments, manages model versions, and handles promotion from staging to production. It keeps everything reproducible and organized as you retrain models or move them between environments.
Pros and Cons of LangSmith and MLflow
LangSmith:
MLflow:
Choosing Between LangSmith and MLflow
Let’s look at how each platform handles ML and LLM workflows:
LLM applications: LangSmith provides tracing, evaluation, and agent deployment without extra infrastructure.
Traditional ML workflows: MLflow tracks experiments, manages model versions, and supports reproducible deployments.
LLM-specific tasks: Chatbots, agents, and RAG pipelines are supported by LangSmith’s built-in observability.
Classical ML tasks: Supervised learning, forecasting, and recommendation systems are handled by MLflow.
Self-hosting: MLflow can be run locally or on your own servers; LangSmith requires enterprise agreements for on-prem deployment.
Managed services: LangSmith offers a hosted option for teams without infrastructure; MLflow relies on cloud providers for managed hosting.
Integration: LangSmith works closely with LangChain and LangGraph; MLflow is framework-agnostic.
How to Migrate or Start with One of Them
If you are building an LLM application with LangChain or similar frameworks, start with LangSmith for seamless integration and tooling designed for LLM workflows. For traditional ML models or diverse ML workloads, MLflow offers a broader scope and handles most use cases directly.
If your future direction is uncertain, MLflow’s open-source nature reduces risk, and you can add LangSmith later if LLM workflows become a priority.
When migrating from MLflow to LangSmith, you will need to reinstrument code, as tracking APIs differ. Using both platforms together is possible, with MLflow managing traditional models and LangSmith handling LLM workflows. Exporting data between them requires custom scripts, since neither platform provides built-in migration tools.
Your Next Step
Choose the platform based on your primary workload. For LLM-focused projects, use LangSmith. For traditional ML or mixed workloads, use MLflow.
Identify your main workflows and team experience
Run a small project on each platform to see how they perform
You can also connect with our experts for a consultation on setting up workflows, integrating LangSmith and MLflow, or assessing which platform fits your project and team requirements.
Frequently Asked Questions
What is the main difference between LangSmith and MLflow?
LangSmith is built specifically for LLM applications with tracing, evaluation, and deployment focused on prompt chains and agents. MLflow is a general MLOps platform for traditional machine learning with experiment tracking, model registry, and deployment for any ML framework.
LangSmith optimizes for debugging non-deterministic LLM behavior while MLflow optimizes for managing statistical model training and deployment. MLflow added LLM features through autologging and evaluation tools, but LangSmith remains more specialized for LLM workflows.
Can you use LangSmith and MLflow together?
Yes. Many teams use MLflow for traditional ML models and LangSmith for LLM workflows. For example, you might use MLflow to manage a reranking model while using LangSmith to trace the full RAG pipeline that includes LLM calls. The platforms serve different purposes and don't conflict. However, most teams choose one as their primary platform based on their dominant workload type.
Is LangSmith open-source?
Partially. LangSmith's client SDKs for Python and JavaScript are open-source, but the platform itself is a commercial hosted service. You can use LangSmith Cloud (SaaS) or deploy it in your own environment through enterprise agreements. This differs from MLflow, which is fully open-source under Apache 2.0 license and can be self-hosted freely.
Which is better for LLM applications?
LangSmith is purpose-built for LLM applications with native tracing of prompt chains, evaluation tools designed for LLM outputs, prompt management with versioning, and deployment infrastructure for stateful agents. MLflow now supports LLM workflows through tracing, evaluation, and prompt management with integrations for OpenAI, LangChain, and LlamaIndex.
However, LangSmith provides deeper specialization and more comprehensive LLM-native features. If LLM applications are your primary focus, LangSmith offers better specialized tooling.
What are the alternatives to MLflow or LangSmith?
For general MLOps, alternatives include Weights & Biases, ClearML, Neptune.ai, and Kubeflow. For LLM-specific tooling, alternatives include Weights & Biases with LLM tracing, Phoenix by Arize, and Helicone. BentoML focuses on model serving and deployment. Each platform has different strengths in experiment tracking, deployment, or observability, so evaluate based on your specific requirements.
