LangWatch vs. LangSmith vs. Braintrust vs. Langfuse: Choosing the Best LLM Evaluation & Monitoring Tool in 2025

Manouk Draisma

Apr 17, 2025

As GenAI moves into mainstream enterprise and production, evaluation and monitoring tools for Large Language Models (LLMs) are no longer optional — they’re mission-critical.

Whether you’re building agentic systems, RAG pipelines, or domain-specific chat applications, evaluating and monitoring LLM performance is essential to ensure accuracy, cost-efficiency, and trustworthiness. This guide breaks down the best LLM evaluation platforms in 2025 — with practical advice on choosing what fits your team.

Why LLM Evaluation and monitoring matter

LLMs can be unpredictable. Hallucinations, regressions across versions, and inconsistent outputs in production are all common pain points.

Evaluation tools help you:

Run side-by-side tests for prompt or model changes.
Benchmark outputs using automated or human-in-the-loop evaluation.
Trace production issues back to exact inputs, versions, or model changes.
Set up real-time alerts when quality drops.

If you're scaling an AI-native product, this isn't just useful — it's necessary.

What makes a great LLM Evaluation tool?

Before we compare specific vendors, here are the core evaluation and observability capabilities to look for:

Capability	Description
Prompt & Dataset Management	Define and version prompts and test datasets with variable support, UI or code-based editing.
Evaluation Types	Use LLM-as-a-Judge, code-based, or human review methods to score outputs.
Traceability & Logging	Log all executions with metadata (latency, cost, prompt version, etc.).
Multimodal & Tool Use	Support for RAG, function calls, audio, or image inputs.
Deployment Options	Cloud-based, on-premise, hybrid deployment depending on security needs.
Integration & APIs	Compatible SDKs, CI/CD hooks, and tracing for OpenAI, Anthropic, Claude, Azure, etc.
Team Collaboration	UI for both devs and non-devs, roles/permissions, comments, shared views.
Monitoring & Alerts	Alerting when eval scores degrade, auto-flagging, online evaluation pipelines.

Side-by-Side comparison of the Top LLM Monitoring & Evaluation Tools

Feature / Tool	LangWatch	LangSmith	Braintrust	Langfuse
Ideal For	Dev team code-first + cross-functional non-technical users	Dev teams needing code-first workflows	Cross-functional teams, non-technical users	Dev teams needing low-cost logging + hosting
Prompt Management (Code)	Yes	Yes	Yes	Yes
Prompt Management (UI)	Excellent (side-by-side, versioned, interactive)	Yes	Yes	Limited
Dataset Creation	Yes automatically generated datasets from production data (webhooks / filters)	Yes	Yes	Yes
LLM-as-a-Judge	Yes (bring your own or use built-in models)	Yes	Yes	Yes
Build your own Custom Eval metrics	Yes	No	No	No
Evaluation Wizard	Yes	No	No	No
Human in the loop	Yes	Yes	Yes
Domain Expert (non-tech) friendly	Yes	No	Yes	No
User Analytics (topic clustering, usage)	Yes	No	No	NO
Auto-LLM optimisation DSPy based.	Yes	No	No	No
Experimentation	Yes	No	No	No
Multimodal Support	Yes (text, image)	No	No	Limited (Markdown only)
Logging & Tracing	Yes - Full span/trace logging, metadata, replays	Yes	Yes	Yes (in depth)
Online Evaluation	Yes (sampled, triggered, flagged)	Yes	Yes	Yes
On-Premise / Self-Hosting	Yes	No	No	Yes
Security / Compliance	ISO 27001, SSO RBAC, audit logs	Partial	Yes	Partial
Community / Documentation	Private slack 1-1 support / onboarding	Active community	?	GitHub-based, technical, discord
Free Tier	Yes (all functionalities)	Yes (limited)	Yes	Yes (all)
Open Source	Yes	No	No	Yes

When to build a custom evaluation pipeline

You may need a custom solution if:

You're working with complex agent chains or stateful memory.
You require live audio, multimodal inputs, or screenshots of interactions.
You want full control over evaluation logic, visualization, and infrastructure.

LangWatch makes it easy to extend evaluation logic without sacrificing monitoring or visibility—offering a middle ground between buying and building.

Final Thoughts

Choosing the right LLM evaluation and monitoring tool depends on your:

Team structure: Developer-first? Cross-functional?
Stage: Early-stage MVP vs. production system with thousands of daily users.
Use case: Is prompt tuning your focus, or real-time monitoring in production?

If you're looking for a developer-friendly, enterprise-ready platform to collaborate with cross-functional teams or less technical founders - with full customized evaluation workflows and automatic prompt optimzers - LangWatch is built for you.

Ready to Try LangWatch?

LangWatch helps GenAI teams evaluate and monitor LLMs across development and production. With built-in tracing, customizable evaluations, and human + LLM scoring, it’s the most flexible tool on the market today.

👉 Start for Free

As GenAI moves into mainstream enterprise and production, evaluation and monitoring tools for Large Language Models (LLMs) are no longer optional — they’re mission-critical.

Why LLM Evaluation and monitoring matter

LLMs can be unpredictable. Hallucinations, regressions across versions, and inconsistent outputs in production are all common pain points.

Evaluation tools help you:

Run side-by-side tests for prompt or model changes.
Benchmark outputs using automated or human-in-the-loop evaluation.
Trace production issues back to exact inputs, versions, or model changes.
Set up real-time alerts when quality drops.

If you're scaling an AI-native product, this isn't just useful — it's necessary.

What makes a great LLM Evaluation tool?

Before we compare specific vendors, here are the core evaluation and observability capabilities to look for:

Capability	Description
Prompt & Dataset Management	Define and version prompts and test datasets with variable support, UI or code-based editing.
Evaluation Types	Use LLM-as-a-Judge, code-based, or human review methods to score outputs.
Traceability & Logging	Log all executions with metadata (latency, cost, prompt version, etc.).
Multimodal & Tool Use	Support for RAG, function calls, audio, or image inputs.
Deployment Options	Cloud-based, on-premise, hybrid deployment depending on security needs.
Integration & APIs	Compatible SDKs, CI/CD hooks, and tracing for OpenAI, Anthropic, Claude, Azure, etc.
Team Collaboration	UI for both devs and non-devs, roles/permissions, comments, shared views.
Monitoring & Alerts	Alerting when eval scores degrade, auto-flagging, online evaluation pipelines.

Side-by-Side comparison of the Top LLM Monitoring & Evaluation Tools

Feature / Tool	LangWatch	LangSmith	Braintrust	Langfuse
Ideal For	Dev team code-first + cross-functional non-technical users	Dev teams needing code-first workflows	Cross-functional teams, non-technical users	Dev teams needing low-cost logging + hosting
Prompt Management (Code)	Yes	Yes	Yes	Yes
Prompt Management (UI)	Excellent (side-by-side, versioned, interactive)	Yes	Yes	Limited
Dataset Creation	Yes automatically generated datasets from production data (webhooks / filters)	Yes	Yes	Yes
LLM-as-a-Judge	Yes (bring your own or use built-in models)	Yes	Yes	Yes
Build your own Custom Eval metrics	Yes	No	No	No
Evaluation Wizard	Yes	No	No	No
Human in the loop	Yes	Yes	Yes
Domain Expert (non-tech) friendly	Yes	No	Yes	No
User Analytics (topic clustering, usage)	Yes	No	No	NO
Auto-LLM optimisation DSPy based.	Yes	No	No	No
Experimentation	Yes	No	No	No
Multimodal Support	Yes (text, image)	No	No	Limited (Markdown only)
Logging & Tracing	Yes - Full span/trace logging, metadata, replays	Yes	Yes	Yes (in depth)
Online Evaluation	Yes (sampled, triggered, flagged)	Yes	Yes	Yes
On-Premise / Self-Hosting	Yes	No	No	Yes
Security / Compliance	ISO 27001, SSO RBAC, audit logs	Partial	Yes	Partial
Community / Documentation	Private slack 1-1 support / onboarding	Active community	?	GitHub-based, technical, discord
Free Tier	Yes (all functionalities)	Yes (limited)	Yes	Yes (all)
Open Source	Yes	No	No	Yes

When to build a custom evaluation pipeline

You may need a custom solution if:

You're working with complex agent chains or stateful memory.
You require live audio, multimodal inputs, or screenshots of interactions.
You want full control over evaluation logic, visualization, and infrastructure.

LangWatch makes it easy to extend evaluation logic without sacrificing monitoring or visibility—offering a middle ground between buying and building.

Final Thoughts

Choosing the right LLM evaluation and monitoring tool depends on your:

Team structure: Developer-first? Cross-functional?
Stage: Early-stage MVP vs. production system with thousands of daily users.
Use case: Is prompt tuning your focus, or real-time monitoring in production?

Ready to Try LangWatch?

👉 Start for Free

As GenAI moves into mainstream enterprise and production, evaluation and monitoring tools for Large Language Models (LLMs) are no longer optional — they’re mission-critical.

Why LLM Evaluation and monitoring matter

LLMs can be unpredictable. Hallucinations, regressions across versions, and inconsistent outputs in production are all common pain points.

Evaluation tools help you:

Run side-by-side tests for prompt or model changes.
Benchmark outputs using automated or human-in-the-loop evaluation.
Trace production issues back to exact inputs, versions, or model changes.
Set up real-time alerts when quality drops.

If you're scaling an AI-native product, this isn't just useful — it's necessary.

What makes a great LLM Evaluation tool?

Before we compare specific vendors, here are the core evaluation and observability capabilities to look for:

Capability	Description
Prompt & Dataset Management	Define and version prompts and test datasets with variable support, UI or code-based editing.
Evaluation Types	Use LLM-as-a-Judge, code-based, or human review methods to score outputs.
Traceability & Logging	Log all executions with metadata (latency, cost, prompt version, etc.).
Multimodal & Tool Use	Support for RAG, function calls, audio, or image inputs.
Deployment Options	Cloud-based, on-premise, hybrid deployment depending on security needs.
Integration & APIs	Compatible SDKs, CI/CD hooks, and tracing for OpenAI, Anthropic, Claude, Azure, etc.
Team Collaboration	UI for both devs and non-devs, roles/permissions, comments, shared views.
Monitoring & Alerts	Alerting when eval scores degrade, auto-flagging, online evaluation pipelines.

Side-by-Side comparison of the Top LLM Monitoring & Evaluation Tools

Feature / Tool	LangWatch	LangSmith	Braintrust	Langfuse
Ideal For	Dev team code-first + cross-functional non-technical users	Dev teams needing code-first workflows	Cross-functional teams, non-technical users	Dev teams needing low-cost logging + hosting
Prompt Management (Code)	Yes	Yes	Yes	Yes
Prompt Management (UI)	Excellent (side-by-side, versioned, interactive)	Yes	Yes	Limited
Dataset Creation	Yes automatically generated datasets from production data (webhooks / filters)	Yes	Yes	Yes
LLM-as-a-Judge	Yes (bring your own or use built-in models)	Yes	Yes	Yes
Build your own Custom Eval metrics	Yes	No	No	No
Evaluation Wizard	Yes	No	No	No
Human in the loop	Yes	Yes	Yes
Domain Expert (non-tech) friendly	Yes	No	Yes	No
User Analytics (topic clustering, usage)	Yes	No	No	NO
Auto-LLM optimisation DSPy based.	Yes	No	No	No
Experimentation	Yes	No	No	No
Multimodal Support	Yes (text, image)	No	No	Limited (Markdown only)
Logging & Tracing	Yes - Full span/trace logging, metadata, replays	Yes	Yes	Yes (in depth)
Online Evaluation	Yes (sampled, triggered, flagged)	Yes	Yes	Yes
On-Premise / Self-Hosting	Yes	No	No	Yes
Security / Compliance	ISO 27001, SSO RBAC, audit logs	Partial	Yes	Partial
Community / Documentation	Private slack 1-1 support / onboarding	Active community	?	GitHub-based, technical, discord
Free Tier	Yes (all functionalities)	Yes (limited)	Yes	Yes (all)
Open Source	Yes	No	No	Yes

When to build a custom evaluation pipeline

You may need a custom solution if:

You're working with complex agent chains or stateful memory.
You require live audio, multimodal inputs, or screenshots of interactions.
You want full control over evaluation logic, visualization, and infrastructure.

LangWatch makes it easy to extend evaluation logic without sacrificing monitoring or visibility—offering a middle ground between buying and building.

Final Thoughts

Choosing the right LLM evaluation and monitoring tool depends on your:

Team structure: Developer-first? Cross-functional?
Stage: Early-stage MVP vs. production system with thousands of daily users.
Use case: Is prompt tuning your focus, or real-time monitoring in production?