Written by humansConfident AI and LangSmith both offer LLM tracing, online evals, prompt management, and more — but they're built around fundamentally different philosophies. LangSmith is an observability platform with evaluation features added on top, tightly coupled to the LangChain ecosystem. Confident AI is an evaluation-first platform with observability built in, designed for cross-functional teams and framework-agnostic from day one.
In this guide, we'll break down these differences across features, pricing, and use cases so you can decide which fits your team.
How is Confident AI Different?
1. It's a platform built with an evals-first mindset
Although both Confident AI and LangSmith offer evals, Confident AI treats evals as a first-class citizen, making AI quality the primary focus for its LLM observability features rather than treating observability as standard analytics achievable by another tool such as Datadog. This means:
Regression testing is built into test runs to catch breaking changes before users do
Experimentation is possible on any AI app, not just prompts
50+ industry standard metrics for AI agents, RAG, chatbots, powered by DeepEval
Multi-turn simulations included for testing conversational agents
Red teaming for security testing AI apps
Online metrics on all traces, spans, and threads (conversations) logged
In other words, Confident AI covers the AI quality layer of your stack, instead of just AI visibility.
2. All-in-one platform with no vendor lock-in
Confident AI is the ultimate LLM observability platform because it puts all your organization's AI quality needs in one place, while offering seamless integrations with any framework you are using (or might use in the future).
To set the record straight - LangSmith technically integrates with other frameworks like Pydantic AI, but the experience may vary dramatically. If one team builds with LangChain while another uses Pydantic AI for a different use case, they'll get vastly different levels of LLM observability depth and feature support. This creates inconsistent evaluation standards across your organization, making it impossible to establish unified AI quality governance.
Furthermore, several features on LangSmith are only usable when integrated with other tools in their ecosystem such as LangServe.
3. Serves cross-disciplinary teams, not just developers
Although both Confident AI and LangSmith serve developers well and require developers for initial setup, Confident AI was built also for non-technical teams such as PMs, QAs, and subject matter experts (SMEs) in mind.
PMs can run an end-to-end iteration cycle since Confident AI can ping your AI app anywhere through HTTP requests via AI connections, QAs run regression tests and manage datasets for pre-deployment workflows with ease, and SMEs annotate traces and evaluation runs.
UX/UI is also more intuitive, and the best way to verify this is to try it out yourself from our generous free-tier.
Features and Functionalities
Confident AI and LangSmith offer a similar suite of features, but LangSmith lacks evaluation depth and things like ease of use for non-technical teams.
Confident AI
LangSmith
LLM Observability Trace AI agents, track latency and cost, and more
LLM Metrics Metrics for quality assurance, LLM-as-a-judge, and custom metrics
Simulations For multi-turn conversational agents
AI analytics Determine user activity, retention, most active use cases
Limited
Dataset management Supports datasets for both single and multi-turn use cases
Single-turn only
Regression testing Side-by-side performance comparison of LLM outputs
Prompt versioning Manage single-text and message-prompts
Human annotation Annotate monitored data, align annotation with evals, and API support
API support Centralized API to manage evaluations
Red teaming Safety and security testing
LLM Observability
Both Confident AI and LangSmith offer LLM observability. Confident AI has a more generous free-tier and more flexible annotation options.
Confident AI
LangSmith
Free tier Based on monthly usage
Unlimited seats, 10k traces, 1 month data retention
1 seat, 5k traces, 14-day data retention
Core Features
LangChain/Graph Integration One-line code integration
OTEL Instrumentation OTEL integration and context propagation for distributed tracing
Graph Visualization A tree view of AI agent execution for debugging
Metadata logging Log any custom metadata per trace
Trace sampling Sample the proportion of traces logged
Custom span types Customize span classification for better analysis on the UI
Dashboarding View trace-related data in graphs and charts
Fully functional, but graphs are disjointed over the platform
Conversation tracing Group traces in the same session as a thread
User feedback Allow users to leave feedback via APIs or on the platform
Export traces Via API or bulk export
Annotation Annotate traces, spans, and threads
Only on traces
LLM Evals
Both Confident AI and LangSmith offer evals, but Confident AI's evals is noticeably better in both functionality and UX/UI, for both technical and non-technical teams.
Confident AI's metrics are powered by DeepEval - this means implementation is open-source and used by some of the world's leading AI companies such as OpenAI, Google, and Microsoft.
Confident AI
LangSmith
Free tier Based on monthly usage
Unlimited offline evals, online evals free for first 14-days
Supports online and offline evals (usage not transparent)
Core Features
Experimentation on multi-prompt AI apps 100% no-code eval workflows on multiple versions of your AI app
Eval alignment Statistics for how well LLM metrics align with human annotation
Eval on AI connections Reach any AI app through HTTP requests for experimentation
Online and offline evals Run metrics on both production and development traces
Multi-turn simulations Simulate user conversations with AI conversational agents
Multi-turn dataset format Scenario-based datasets instead of input-output pairs
Native multi-modal support Support images in datasets and metrics
Not on datasets
Testing reports & regression testing Allow regression testing and stakeholder sharable testing reports
LLM Metrics Supports LLM-as-a-judge metrics for AI agents, RAG, multi-turn, and custom ones.
50+ metrics for all use cases, single and multi-turn, research-backed custom metrics, powered by DeepEval
Offer custom metrics, heavy setup required however. Does not support equation-based scoring.
Non-technical friendly test case format Upload CSVs as datasets that do not assume any technical knowledge
AI app & Prompt Arena Compare different versions of prompts or AI apps side-by-side
Only for single prompts
Human Annotations
Both Confident AI and LangSmith offer human annotations, each with a different focus with Confident AI being more opinionated and supports annotations on more types of data.
Confident AI
LangSmith
Free tier Based on monthly usage
Unlimited annotations and annotation queues, forever data retention
Supports annotations and annotation queues (usage not transparent)
Core Features
Reviewer annotations Annotate on the platform
Annotations via API Allow end users to send annotations
Custom annotation criteria Allow annotations to be of any criteria
Annotation on all data types Annotations on test cases (for development evals), traces, spans, and threads
Only supported for traces
Custom scoring system Allow users to define how annotations are scored
Yes, either thumbs up/down or 5 star rating system
Yes, either continuous (0-1) or category-based
Curate dataset from annotations Use annotations to create new rows in datasets
Only for single-turn
Export annotations Export via CSV or APIs
Annotation queues A focused view on annotating test cases, traces, spans, and threads
Only for traces
Prompt Engineering
Both Confident AI and LangSmith offer similar capabilities for prompt versioning and management, with Confident AI offering more customizations in templating.
Confident AI
LangSmith
Free tier Based on monthly usage
1 prompt, unlimited versions
Supports prompts (usage not transparent)
Core Features
Text and message prompt format Strings and list of messages in OpenAI format
Custom prompt variables Support variables that can be interpolated at runtime
Advance conditional logic Support if-else statements, for-loops
Yes, supported via {% Jinja %} formats
Prompt versioning Manage different versions of the same prompt
Manage prompts in code Use, upload, and edit prompts via APIs
Label/tag prompt versions Identify prompts in human-friendly labels
Run prompts in playground Compare prompts side-by-side
Supports tools, output schemas, and models Version not just prompt content, but also tools, and model parameters such as provider and temperature
Log prompts to spans Find which prompt version was used in production
AI Red Teaming
Confident AI offers red teaming for AI applications whereas LangSmith has no such offering. Red teaming allow you to automatically scan for any security and safety vulnerabilities in your AI in under 10 minutes.
Confident AI
LangSmith
Free tier Based on monthly usage
Red teaming on enterprise-only
Not supported
Core Features
LLM Vulnerabilities Library of prebuilt vulnerabilities such as bias, PII leakage, etc.
Adversarial Attack Simulations Simulate single and multi-turn attacks to expose vulnerabilities
Industry frameworks and guidelines OWASP Top 10, NIST AI, etc.
Customizations Custom vulnerabilities, frameworks, and attacks
Red team any AI app Reach AI apps through the internet to red team
Purpose-specific red teaming Get use case tailored attacks based on AI purpose
Risk assessments Generate risk assessments that contains things like CVSS scores
Pricing
Both Confident AI and LangSmith offer generous free-tiers, and operate on a philosophy of try-first-before-deciding. This means free for most features, but limited to an individual seat for experimental purposes.
However these are the main differences:
Confident AI
Confident AI charges based on a combination of usage and user seats. Pricing is transparent with usage cost you can calculate in advance. Usage is measured in GB-months at $1 per unit, A GB-month represents either one GB of data ingested or one GB of data retained for one month — and teams can allocate their usage flexibly between the two, making costs straightforward to forecast.
Confident AI:
Is 50% cheaper than LangSmith for individual users ($19.99 per seat)
Has more generous data retention policies (3 months vs 14 days on LangSmith)
Place no limits on the number of seats on cheaper plans (LangSmith confines you to 10 max seats)
For startups: Offers an affordable option for small teams needing enterprise features, and a sweet YC deal
For growth-stage companies: Offers custom pricing with flexible seats and unlimited projects
For enterprise: Offers custom pricing for those with enterprise standards and requirements
LangSmith
LangSmith charge based on a combination of usage and user seats. Pricing is transparent but less flexible, with stricter limits for mid-market companies. Growth-stage companies that don't need enterprise but want more than 10 seats might find pricing restrictive.
LangSmith:
Requires you to go into an annual commitment for 10 seats and above
No middle-tier for grow-stage companies
Has more restrictive data retention limits
Security and Compliance
Both Confident AI and LangSmith are enterprise ready, with Confident AI being the less pricey option for many standard security features.
Confident AI
LangSmith
SOC II For organizations requiring audit-ready compliance
HIPAA For customers in the healthcare domain
GDPR For customers with a focus in EU
2FA For users that want extra security
Social Auth (e.g. Google) Simplified authentication via identity providers
Only for paid plans
Custom RBAC For organizations that need fine-grained data access
Team plan or above
Enterprise only
SSO For organizations that want to standardize authentication
Team plan or above
Enterprise only
InfoSec Review For customers with a security questionnaire
Team plan or above
Enterprise only
On-Prem Deployment For customers with strict data requirements
Enterprise only
Enterprise only
Why Confident AI is the best LangSmith Alternative
Although both are feature-rich LLM observability platforms, Confident AI stands out because it centralizes everything related to AI quality — observability, evaluations, simulations, and red teaming — while offering a UI intuitive enough for non-technical teams to use independently.
The impact is measurable. Humach, an enterprise voice AI company serving clients like McDonald's, Visa, and Amazon, shipped voice AI deployments 200% faster after adopting Confident AI. Their team of 20+ non-technical annotators replaced fragmented spreadsheets and CSV-based testing with a single collaborative workspace for multi-turn evaluation, bias testing, and governance — eliminating what they estimate would have been hundreds of thousands of dollars in custom tooling. As their Chief AI Officer put it: "Confident AI increased our speed to market by 200%. For us, compliance and trust aren't optional — they're required."
This means that although both look similar on paper, Confident AI unlocks more ROI by:
Enabling product managers, QA teams, and domain experts to run complete evaluation cycles without engineering support — saving teams 20+ engineering hours per week
Compressing multi-turn conversation testing from hours of manual prompting into minutes through automated simulations
Including red teaming out of the box — security testing every production AI system eventually needs, without licensing a separate vendor
Offering more functionality at a lower price point, with no vendor lock-in to any single framework
LangSmith requires engineering involvement at every evaluation step, limits annotations to traces only, and ties its deepest feature support to the LangChain ecosystem. For teams that need their entire organization contributing to AI quality — not just engineers — Confident AI delivers more value.
When LangSmith Might Be a Better Fit
LangSmith excels in specific scenarios where Confident AI may not be the optimal choice:
Deep LangChain Ecosystem Integration: If your entire AI stack is built exclusively on LangChain (using LangGraph, LangServe, LangChain agents), LangSmith offers tighter integration with framework-specific features. For teams who plan to stay 100% within the LangChain ecosystem, this creates a more seamless developer experience.
Simpler Needs, Smaller Scale: If you're a solo developer or 2-person team building a straightforward RAG application without multi-turn conversations, safety requirements, or cross-functional collaboration, LangSmith's narrower feature set may feel less overwhelming.
The bottom line: Both platforms solve real LLM observability problems. Choose LangSmith if you're building simple, LangChain-exclusive applications with a small technical team. Choose Confident AI if you need evaluation depth, cross-functional collaboration, or plan to scale beyond 10 engineers.
Frequently Asked Questions
Is Confident AI better than LangSmith?
Confident AI is better than LangSmith for teams that need evaluation depth, cross-functional collaboration, and framework flexibility. It offers 50+ research-backed metrics through DeepEval, multi-turn conversation simulation, built-in red teaming, and no-code evaluation workflows that non-technical team members can use independently. LangSmith is better for small, engineering-only teams that are fully committed to the LangChain ecosystem and primarily need observability with basic evaluation scoring.
Is Confident AI cheaper than LangSmith?
Yes. Confident AI is approximately 50% cheaper per seat than LangSmith, with more generous free-tier limits — unlimited seats and 10k traces with 1-month data retention, compared to LangSmith's 1 seat, 5k traces, and 14-day retention. Confident AI also places no limits on seats for cheaper plans, while LangSmith caps at 10 seats before requiring an annual enterprise commitment. Usage beyond the free tier is priced at $1 per GB-month, which teams can flexibly allocate toward either ingestion or retention.
Can non-technical teams use LangSmith?
LangSmith is primarily designed for engineering teams. Non-technical users cannot independently trigger evaluations against production AI applications, and annotations are limited to traces only — not spans, threads, or test cases. There is no way to call your AI app directly for experimentation the way you would in Postman. Confident AI enables product managers, QA teams, and domain experts to run complete evaluation cycles, manage datasets, and annotate across all data types through a no-code interface.
Does Confident AI work with LangChain?
Yes. Confident AI integrates with LangChain alongside OpenTelemetry, OpenAI, Pydantic AI, and 10+ other frameworks. Unlike LangSmith, which provides its deepest feature support exclusively for LangChain and LangGraph, Confident AI delivers a consistent observability and evaluation experience regardless of which framework your team uses — eliminating the risk of inconsistent AI quality standards across different parts of your organization.
Which is better for evaluating RAG applications — Confident AI or LangSmith?
Confident AI is stronger for RAG evaluation. It offers dedicated retrieval and generation metrics through DeepEval including answer faithfulness, hallucination detection, contextual relevancy, and retrieval precision — all research-backed and open-source. Evaluations can target individual retrieval or generation spans within traces, so teams can isolate whether issues stem from retrieval quality or generation logic. LangSmith offers basic evaluation scoring but lacks this depth of RAG-specific metrics and component-level granularity.
Which is better for evaluating AI agents — Confident AI or LangSmith?
Confident AI is better for evaluating AI agents. It supports evaluation at both the overall agent level and individual span level — meaning teams can test tool selection, reasoning steps, and final outputs independently within a single agent trace. Multi-turn simulation automates end-to-end agent conversation testing that would otherwise require hours of manual prompting. LangSmith's agent evaluation is tightly coupled to LangGraph and lacks comparable multi-turn evaluation depth and simulation capabilities.
Which is better for enterprise — Confident AI or LangSmith?
Confident AI offers stronger enterprise flexibility. It includes fine-grained RBAC on its team plan (LangSmith restricts this to enterprise only), regional deployments across the US, EU, and Australia, publicly available on-premises deployment guides, and white-glove evaluation support directly from the DeepEval team. LangSmith gates features like SSO, RBAC, and security reviews behind its enterprise tier, and requires annual commitments for teams exceeding 10 seats. Confident AI's enterprise customers include Panasonic, Amazon, and Humach.
