Sold by: LangChain
Deployed on AWS
LangSmith provides tools for developing, debugging, and deploying LLM applications. It helps you trace requests, evaluate outputs, test prompts, and manage deployments in one place.
LangSmith is framework agnostic, so you can use it with or without LangChain open-source libraries langchain and langgraph.
Prototype locally, then move to production with integrated monitoring and evaluation to build more reliable AI systems.
LangSmith provides:
- Observability to see exactly how your agent thinks and acts with detailed tracing and aggregate trend metrics.
- Evaluation to test and score agent behavior on production data and offline datasets for continuous improvement.
- Deployment to ship your agent in one click, using scalable infrastructure built for long-running tasks.
4.5
Overview
LangSmith Observability and Evals is a unified observability & evals platform where teams can debug, test, and monitor AI app performance - whether building with LangChain or not.
Find failures fast with agent observability. Quickly debug and understand non-deterministic LLM app behavior with tracing. See what your agent is doing step by step, then fix issues to improve latency and response quality.
Evaluate your agent's performance. Evaluate your app by saving production traces to datasets, then score performance with LLM-as-Judge evaluators. Gather human feedback from subject-matter experts to assess response relevance, correctness, harmfulness, and other criteria.
Experiment with models and prompts in the Playground, and compare outputs across different prompt versions. Any teammate can use the Prompt Canvas UI to directly recommend and improve prompts.
Track business-critical metrics like costs, latency, and response quality with live dashboards, then get alerted when problems arise and drill into root cause.
LangSmith Deployments is a purpose-built infrastructure and management layer for deploying and scaling long-running, stateful agents -- offering:
- 1-click deployment to go live in minutes,
- 30 API endpoints for designing custom user experiences that fit any interaction pattern
- Horizontal scaling to handle bursty, long-running traffic
- A persistence layer to support memory, conversational history, and async collaboration with human-in-the-loop or multi-agent workflows
- Native LangSmith Studio, the agent IDE, for easy debugging, visibility, and iteration
LangSmith Agent Builder: Give every team the ability to build, use, and improve AI agents with the security your org requires.
Highlights
- LangSmith Observability and Evals is a unified observability & evals platform where teams can debug, test, and monitor AI app performance - whether building with LangChain or not. Quickly debug and understand non-deterministic LLM app behavior with tracing. See what your agent is doing step by step, then fix issues to improve latency and response quality.
- LangSmith Deployments is a purpose-built infrastructure and management layer for deploying and scaling long-running, stateful agents offering 1/1-click deployment to go live in minutes, 2/Horizontal scaling to handle bursty, long-running traffic 3/A persistence layer to support memory, conversational history, and async collaboration with human-in-the-loop or multi-agent workflows.
- Please note: there is a $150k annual Platform License plus a minimum $150k annual usage commitment to access this package. To discuss enterprise pricing or to activate your commitment and obtain your license key after signup, please contact us at https://www.langchain.com/contact-sales - alternatively, our self-serve cloud-based products are available at https://www.langchain.com
Details
Sold by
Categories
Delivery method
Supported services
Delivery option
LangSmith Helm Chart
Latest version
Operating system
Linux
Deployed on AWS
New
Introducing multi-product solutions
You can now purchase comprehensive solutions tailored to use cases and industries.
Features and programs
Trust Center
Access real-time vendor security and compliance information through their Trust Center powered by Drata or Vanta. Review certifications and security standards before purchase.
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Cost/unit |
|---|---|
Per Trace | $0.01 |
Per Agent Run | $0.01 |
Metered Usage Amount | $0.01 |
Minimum annual usage commitment, billed in advance | $150,000.00 |
Per Agent Builder Run | $0.10 |
Dimensions summary
You pay for what you use across five independent metering dimensions. Per Trace charges cover observability data captured when your agents run. Per Agent Run and Per Agent Builder Run charge for agent executions on the platform. Metered Usage Amount tracks normalized compute and storage consumption from deployments, engine analysis, and related services. The Minimum annual usage commitment is billed in advance, setting a baseline you draw down against as usage accrues. These dimensions add together based on actual consumption, so your total scales with trace volume, agent activity, and resources used.
Top-of-mind questions for buyers
A trace is a single execution of your application—an agent run, evaluator, or playground session. One trace can include many steps, such as model calls and other tracked events. All those steps roll up into the single trace you are billed for.
It tracks normalized units of work and storage across services. Compute-related work is measured in LangChain Compute Units, and data stored or managed is measured in LangChain Storage Units. Deployments, engine analysis, and sandboxes all consume these units at different rates based on the resources they use.
It depends on your workload. High trace volume from heavy observability pushes Per Trace charges up. Running agents in production drives Per Agent Run and Metered Usage Amount through deployment uptime and engine analysis. All dimensions bill independently and add together on one invoice based on actual consumption.
www.langchain.com
Helpful?
Vendor refund policy
https://www.langchain.com/terms-of-service#:~:text=Customer%20will%20pay%20LangChain%20all ,Fees%20paid%20are%20non%2Drefundable.
Custom pricing options
Request a private offer to receive a custom quote.
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA) .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
Delivery details
LangSmith Helm Chart
Supported services: Learn more
- Amazon EKS
Helm chart
Helm charts are Kubernetes YAML manifests combined into a single package that can be installed on Kubernetes clusters. The containerized application is deployed on a cluster by running a single Helm install command to install the seller-provided Helm chart.
Version release notes
LangSmith 0.13.14 release
Additional details
Usage instructions
See https://docs.smith.langchain.com/self_hosting for full installation and configuration instructions.
Resources
Vendor resources
Support
Vendor support
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
Similar products
LangSmith is an agent engineering platform to build, test, deploy and observe your agents. It helps you trace requests, evaluate outputs, test prompts, and manage deployments in one place. LangSmith is framework agnostic, so you can use it with or without LangChain open-source libraries langchain and langgraph. Prototype locally, then move to production with integrated monitoring and evaluation to build more reliable AI systems. LangSmith provides: - Observability to see exactly how your agent thinks and acts with detailed tracing and aggregate trend metrics. - Evaluation to test and score agent behavior on production data and offline datasets for continuous improvement. - Deployment to ship your agent in one click, using scalable infrastructure built for long-running tasks.
Observability, prompt management and evaluation for LLM applications.
We help customers design and implement Agentic applications on AWS using our modular GenAI Building Block approach. Our experts support rapid PoCs and production deployments, guiding you in selecting and integrating the right LLM for your use case. This ensures flexibility, scalability, and cost-efficiency in your AWS environment.
This product has charges associated with it for hardening, security configuration, and support.
Langflow is an open-source visual framework for building multi-agent and RAG AI applications. This Lynxroute build is security baked in: authentication enabled, Nginx TLS reverse proxy, unique admin credentials at first boot, and CIS Level 1 hardened Ubuntu 24.04 LTS base.
Note: Langflow initialises in ~60 seconds after instance start. Wait 1-2 minutes before opening the Web UI.
MIT license - fully auditable, no vendor lock-in.
Customer reviews
Sanooj M.
Seamless AI Implementation with Lanchain and Strong Support
Reviewed on Aug 13, 2026
Review provided by G2
What do you like best about the product?
Lanchain helped us implement our AI application seamlessly, saving us time and providing strong support through rich modules.
What do you dislike about the product?
Frequent changes in modules, along with a few modules reaching EOL, have required significant changes while maintaining the overall setup.
What problems is the product solving and how is that benefiting you?
We are using LangChain to develop our AI application, which infers code and helps optimize it on the fly.
Albert (Aamir) P.
All-in-One RAG Builder With Robust Ingestion, Splitting, and Embeddings
Reviewed on Aug 13, 2026
Review provided by G2
What do you like best about the product?
It provides all the functions to build RAG, including document ingestion, textsplitter & embeddings.
What do you dislike about the product?
The chunking functions could be sped up when working with large datasets.
What problems is the product solving and how is that benefiting you?
It help us to build chat bots & AI Agents that helps me upskill & for my personal projects
kolawole O.
LangChain Makes Agent Orchestration Easier for My PhD AI System
Reviewed on Aug 13, 2026
Review provided by G2
What do you like best about the product?
I am currently building an agentic AI system for my PhD. I have been using lang chain as part of the process. It makes the agent orchestration maningset other things easier
What do you dislike about the product?
The validity period of their certifications is quite short in my own opinion. It should be longer, also there is a laarge learning curve to get started
What problems is the product solving and how is that benefiting you?
It helps with building agents, deploying, testing and monitoring agents, a skillset I need to build a workflow aware multiagent system
Juan Esteban V.
LangChain Makes Building Practical AI Apps with LLMs Structured and Useful
Reviewed on Aug 13, 2026
Review provided by G2
What do you like best about the product?
I like how it helps me build practical AI applications by connecting language models with tools, data, and external services. During my Master’s in AI at UNIR, it has made experimenting with LLMs feel more structured, focused, and genuinely useful.
What do you dislike about the product?
Sometimes when there are many abstractions docs are a bit hard to follow, but in general I like it a lot, because it gives nice support as well
What problems is the product solving and how is that benefiting you?
LangChain helps reduce the complexity of building AI applications that combine LLMs, tools, and data. For me, as an AI Master’s student at UNIR, it’s been useful for understanding how these pieces fit together, how to connect them effectively, and how to build practical AI projects more easily.
Darshan V.
LangChain Makes Multi-LLM Pipelines Easy with Strong Docs and Solid ROI
Reviewed on Aug 13, 2026
Review provided by G2
What do you like best about the product?
As a framework it doesn't provide any exceptionally why but it gives a good user experience, as the main feature of langchain it helps to integrate multiple large language models together which is also his best feature I mean giving a platform to integrate multiple language model in a pipeline , as a open source model it also provides a good Roi , and because of many docs on the Internet it's also easy to start working with it.
What do you dislike about the product?
The major point of my disliking is that it's abstract layers because that it's hard to understand it also the frequent changes made in the model , because of repeated updates its hard to keep track of the model along with its current working from and because of having a repeated updates it also constantly brakes and to solve that we have to constantly change the code and as it has frequent updates the documents also feels not enough
What problems is the product solving and how is that benefiting you?
The most biggest problem its solves for me is that it helps me giving a perfect pipeline for my agent, with the help of lingchain I chain multiple tasks to my agent pipeline easily.