Confident AI vs LangSmith: Head-to-Head Comparison

Choosing the right LLM observability (and evals) platform often comes down to trade-offs.

On paper, Confident AI and LangSmith both cover the essentials – LLM tracing, online evals, prompt experimentation, and more – but their strengths show up in different places.

In this buyer's guide, we'll cover these differences in more detail, comparing features and functionalities, pricing, ROIs, and the best fit for different AI use cases.

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 does 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 offers generous free-tiers, and operates 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 charge based on a combination of usage and user seats. Pricing is transparent with usage cost you can calculate in advance. Usage cost is not measured by tokens or disk storage, but rather things like trace count that you can easily anticipate in advance.

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 too 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 customers with a security guy}

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) _{For users that don't want to remember their passwords}

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

Why Confident AI is the best LangSmith Alternative

Although both are a feature-rich LLM observability platform, Confident AI is the best LangSmith because it centralizes all features related to AI quality (observability, evaluations, simulations, and red teaming) while offering UX/UI intuitive enough for non-technical teams to use.

This means that although both looks similar on paper, the reality is Confident AI unlocks more ROI by:

Allowing non-technical members to contribute to LLM observability and evals
Enabling simulations to save up to 3 hours of testing time for multi-turn use cases
Offering red teaming which is security testing for AI apps, something every organization ultimately needs in production
Being cheaper, while offering more

This means that if you want industry-standard evals in your observability stack, don't want to double-pay for simulation tools and red teaming tools, and want non-technical users to be able to also use Confident AI, you will find more value in Confident AI.

Migrating from LangSmith to Confident AI is extremely easy (integration guide here), and the best way to test out the difference is to try it yourself for free.

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. The best way to decide? Try both on your actual use case.

Do you want to brainstorm how to evaluate your LLM (application)? Ask us anything in our discord. I might give you an "aha!" moment, who knows?