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Confident AI vs Langfuse: Head-to-Head Comparison

Written by humansLast edited on Jan 28, 2026

Choosing the right LLM observability and evaluation platform comes down to what matters most to your team.

Langfuse's main advantage is being open-source and self-hostable—ideal if you need full infrastructure control. Confident AI focuses on depth of features and functionality, offering a more comprehensive evals toolkit out of the box.

In this guide, we'll break down the differences across features, pricing, and use cases to help you decide.

How is Confident AI Different?

1. It's a platform built with an evals-first mindset

Both Confident AI and Langfuse offer evals, but Confident AI treats them as the core focus—not an add-on to standard observability.

50+ industry-standard metrics for AI agents, RAG, and chatbots, powered by DeepEval
Online metrics across all traces, spans, and conversations
Multi-turn simulations for conversational agent testing
Experimentation on any AI app, not just prompts
Regression testing built into test runs to catch breaking changes early
Red teaming for AI security testing

Confident AI covers the AI quality layer of your stack, not just visibility.

2. Native support for multi-turn use cases

Although Langfuse offers "session" tracking for multi-turn use cases, they lack the evals support for it, in both production and development.

In production, Confident AI takes threads and the associated traces into account during evaluation, while development testing on multi-turn use cases also include simulations - which is the most time-consuming part of evaluating chatbots.

Without simulations, can easily spend 2-3 hours on manual prompting before there exist a conversation to evaluate.

3. Serves cross-disciplinary teams, not just developers

While both platforms cater to developers and require technical setup initially, Confident AI is designed with cross-functional collaboration in mind—empowering PMs, QAs, and domain experts to contribute meaningfully.

Product managers drive full iteration cycles using AI connections that call your app via HTTP from anywhere in the platform. Quality teams own regression testing and dataset curation without engineering bottlenecks. Subject matter experts provide annotations on traces and evaluation results directly.

The interface is also designed for clarity and ease of use—see for yourself with our generous free tier.

Features and Functionalities

Confident AI and Langfuse offer a similar suite of features, but Langfuse lacks evaluation depth and is harder for non-technical teams to navigate the platform.

Confident AI

Langfuse

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}

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 Langfuse offer extensive features for LLM observability, while both offering different variations of a free-tier.

A unit in Langfuse includes traces, spans, metric scores, etc.

Confident AI

Langfuse

Free tier _{Based on monthly usage}

Unlimited seats, 10k traces, 1 month data retention

2 seats, 50k units, 30-day data retention

Core Features

Integrations _{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}

Online evals _{Run live evals on incoming traces, spans, and threads/sessions}

Only on traces

Custom span types _{Customize span classification for better analysis on the UI}

PII masking _{Redact custom PII in trace data}

Dashboarding _{View trace-related data in graphs and charts}

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}

LLM Evals

Both Confident AI and Langfuse offer evals, but Confident AI delivers a noticeably stronger experience—in both capability and interface—for technical and non-technical users alike.

Under the hood, Confident AI's metrics are powered by DeepEval, an open-source evaluation framework trusted by leading AI teams at OpenAI, Google, and Microsoft.

Confident AI

Langfuse

Free tier _{Based on monthly usage}

Unlimited offline evals, online evals free for first 14-days

Same as unit limits (50k), but bring your own evaluator

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 Langfuse support human annotations, but take different approaches. Confident AI is more opinionated in its design and is extremely generous to annotation teams.

Confident AI

Langfuse

Free tier _{Based on monthly usage}

Unlimited annotations and annotation queues, forever data retention

Limited to 1 annotation queue

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 traces, spans, and threads}

Custom scoring system _{Allow users to define how annotations are scored}

Yes, either thumbs up/down or 5 star rating system

Yes, either numerical, category-based, or boolean

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}

Prompt Engineering

Both Confident AI and Langfuse offer similar capabilities for prompt versioning and management, with Confident AI offering more customizations in templating, while Langfuse offers composite-prompts.

Confident AI

Langfuse

Free tier _{Based on monthly usage}

1 prompt, unlimited versions

Unlimited prompts and versions

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}

Limited, only {{mustache}} syntax supported

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}

Link prompts to traces _{Find which prompt version was used in production}

Create composite prompts _{Use a prompt in another prompt}

AI Red Teaming

Confident AI offers red teaming for AI applications—Langfuse does not. With red teaming, you can automatically scan for security and safety vulnerabilities in your AI system in under 10 minutes.

Confident AI

Langfuse

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 Langfuse offer generous free tiers, but diverge as your team scales.

Confident AI uses a transparent pricing model based on usage and user seats. Costs are predictable—measured by trace count rather than tokens or storage—so you can forecast spend before you scale.

Langfuse is cheaper at higher volumes, primarily because it doesn't charge per seat. For teams prioritizing budget over feature depth, that matters.

But pricing tells only part of the story. Confident AI's seat-based model reflects what you're getting:

Multi-turn simulations for testing conversational agents, brings hours down to minutes per multi-turn evaluation, easily 30x in time saved
Features for cross-functional teams — non-technical teams can easily test multi-prompt AI systems, instead of making engineers a bottleneck in the AI quality assurance process
Red teaming — secure testing is something everyone eventually needs, don't double-pay for vendors on this
Enterprise support — work sessions to come up with the optimal evals strategy with the authors of DeepEval ensures you get the most ROI out of observability

The trade-off is straightforward: Langfuse costs less. Confident AI does more. Choose based on whether you're optimizing for budget or for AI quality infrastructure that scales with your team.

Security and Compliance

Both Confident AI and Langfuse are enterprise ready, with Confident AI being the less pricey option for many standard security features.

Confident AI

Langfuse

Data residency _{For users that want to be all over the place}

US and EU

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}

Custom RBAC _{For organizations that need fine-grained data access}

Team plan or above

Teams add-on

SSO _{For organizations that want to standardize authentication}

Team plan or above

Teams add-on

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

Open-source

Why Confident AI is the best Langfuse 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.

On paper, the two platforms may look similar. In practice, Confident AI unlocks more ROI by:

Empowering non-technical team members to run an end-to-end, AI app iteration cycle without touching a line of code, instead of single-prompt testing
Including multi-turn simulations that save hours of manual testing for conversational use cases
Offering red teaming out of the box—security testing for AI apps that every production system eventually needs
Delivering more functionality across the board for teams serious about AI quality

Langfuse is a strong choice if open-source flexibility and self-hosting are your priorities. But if you want industry-standard evals baked into your observability stack, don't want to stitch together separate tools for simulations and red teaming, and need a platform accessible to your entire team—Confident AI delivers more value.

Getting started is easy, and the best way to see the difference is to try it yourself for free.

When Langfuse Might Be a Better Fit

Langfuse excels in specific scenarios where Confident AI may not be the optimal fit:

Open-source and self-hosting requirements: If your organization mandates open-source tooling or needs to self-host for compliance, data residency, or cost reasons, Langfuse is purpose-built for this. For teams with the engineering capacity to manage their own infrastructure, this offers full control.
Budget-first, smaller-scale projects: If you're a solo developer or small team building a straightforward LLM application without complex evaluation needs or cross-functional collaboration, Langfuse's lower price point and lighter feature set may be all you need.

The bottom line: Both platforms solve real LLM observability problems. Choose Langfuse if open-source flexibility, self-hosting, or budget are your top priorities. Choose Confident AI if you need evaluation depth, a more comprehensive feature set, or a platform designed for your entire team—not just engineers.

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