5 Best LLM Observability and Monitoring Tools in 2025

Are your AI systems really under control? In 2025, LLM-powered tools like chatbots and copilots are helping industries work smarter and faster.

But AI models aren’t perfect.

Hallucinations, bias, and hidden costs can cause serious issues, like bad advice or compliance risks.

Without proper monitoring, businesses are flying blind.

Mistakes can lead to fines, lost trust, and wasted resources.

That’s why observability & monitoring is a must.

In this guide, we’ll explain what LLM observability is, why it matters, and the top tools you need in 2025.

TL;DR – The 5 Best LLM Observability & Monitoring Tools in 2025

Here’s a quick overview of the best observability solutions (deep dive later)

1. Best for turning AI data into role-based dashboards and insights: Upsolve
   
   

2. Best for detecting model drift and performance issues: Arize AI

3. Best for tracking experiments and comparing model runs: Weights & Biases (W&B)
   
   

4. Best for explainability, bias detection, and regulatory compliance: Fiddler AI
   
   

5. Best for debugging LLM agents with trace visualization: Langfuse

What is LLM Observability & How Does It Work?

LLM Observability helps you track and analyze how large language models behave in real-world settings.

It ensures your AI is delivering accurate, safe, and reliable outputs,  while also keeping costs and compliance in check.

Think of it like an air traffic control system for your AI.

Just as controllers monitor flights for safety and efficiency, observability tools track every query, response, and system metric.

This helps you spot issues early, fine-tune your models, and build trust with your users.

Core Elements of LLM Observability & Monitoring Tools 

• Latency – Tracks how fast your model responds. Slow responses could frustrate users or signal infrastructure problems.
  
  

• Cost – Monitors how much your AI operations are costing and helps optimize cloud usage.
  
  

• Accuracy – Checks if the AI is providing correct and useful answers.
  
  

• Hallucinations – Detects when the AI makes up false or misleading information.

• Safety & Compliance – Makes sure your outputs meet ethical and regulatory requirements, especially in sensitive industries like finance and healthcare.

Monitoring these metrics helps you catch problems early, adjust settings, and keep your AI working safely and efficiently.

What to Look for in LLM Observability & Monitoring Tools?

Choosing the right LLM Observability Tools can make all the difference.

The best tools should provide:

• Real-time insights to monitor queries and responses instantly, helping you catch issues before they escalate.
  
  

• Cost optimization features that track usage patterns and suggest ways to reduce unnecessary cloud expenses.
  
  

• Accuracy tracking that ensures your AI delivers reliable results, improving user trust and satisfaction.
  
  

• Risk detection capabilities to identify hallucinations and bias early, preventing misinformation and fairness issues.
  
  

• Compliance and safety tools that help align AI systems with regulatory requirements and ethical standards.
  
  

• Scalability that supports the growth of your AI systems without adding unnecessary complexity.

With these key features, you can confidently deploy LLMs that are powerful, efficient, and secure while staying aligned with your goals.

How Did We Evaluate the Best Observability and Monitoring Tools 

You probably already know what observability tools do. So, we focused on what makes them useful for your team.

• First, we looked for tools that make monitoring simple while offering powerful features without being overwhelming.
  
  

• We checked how well they help you track AI performance, spot errors, and optimize costs quickly.
  
  

• Scalability is key, tools need to grow with your business and fit easily into existing workflows.
  
  

• We also made sure they support security and compliance, especially for sensitive industries like healthcare and finance.
  
  

• Finally, we prioritized tools that offer actionable insights, helping you fix issues without digging through data.

Our goal was to find tools that are user-friendly, reliable, and efficient, so you can focus on improving AI rather than troubleshooting it. That’s how we chose the best options for you.

Quick Comparison for Best LLM Observability Tools

Here’s a detailed look at the top LLM observability platforms in 2025:

1. Upsolve – Best Balanced, All-in-One

Upsolve is an end-to-end LLM agent observability platform designed for enterprises that need to scale AI responsibly without compromising on reliability, compliance, or performance.

What are the Key LLM Observability & Monitoring Features?

• Real-time monitoring helps track AI performance instantly for quick issue detection.
  
  

• Compliance-ready dashboards ensure outputs meet industry regulations and audit requirements.
  
  

• Multilingual support allows global teams to monitor AI in multiple languages.
  
  

• Cost optimization tools help reduce cloud expenses while scaling AI operations.

What is the Pricing?

• Growth Plan: From $1,000/month with dashboards, 50 tenants, and core analytics.
  
  

• Professional Plan: From $2,000/month with unlimited dashboards, AI analytics, and support.
  
  

• Enterprise Plan: Custom pricing with full access, compliance, and 24/7 support.

Pros

• Responsive and quick support helps resolve questions without delays.

• Straightforward analytics explain product benefits clearly to teams and customers.

• Fast insights with high-quality visualizations ready to share instantly.

Cons

• Limited theming options, though APIs provide workarounds.
  
  

• Learning curve for customization, but documentation and support are helpful.
  
  

• Initial setup needs coordination, though it’s manageable with proper planning.

Best For:

Organizations need a balanced solution that delivers compliance, monitoring, and cost optimization without overwhelming technical complexity.

2. Arize AI – Best for Model Performance Insights

Arize AI offers a model observability platform that focuses on understanding and improving machine learning performance across production environments.

What are the Key LLM Observability & Monitoring Features?

• Performance tracking helps teams spot accuracy drops and make timely corrections.
  
  

• Bias detection identifies fairness issues to ensure equitable model outcomes.
  
  

• Drift monitoring alerts teams when data changes risk reducing model reliability.
  
  

• Root cause analysis allows users to understand why models fail or underperform.

What is the Pricing?

Arize Phoenix - Free & Open source

Unlimited users, monitoring and troubleshooting machine learning models.

AX Free – $0 /mo

Basic monitoring for developers with 25k spans/month and 7-day data retention.

AX Pro – $50 /mo

For startups and small teams, offering 100k spans/month, 15-day retention, and 3 users.

AX Enterprise – Custom

Full observability with unlimited spans, advanced integrations, compliance, and dedicated support.

Pros:

• Actionable insights into model drift and data issues enhance proactive monitoring.
  
  

• Intuitive interface simplifies tracking and understanding model health.
  
  

• Seamless integration with various ML frameworks streamlines setup.

Cons:

• Limited customization in dashboards may not meet all user needs.
  
  

• Learning curves for advanced features could require additional training.

• Performance issues reported with large-scale deployments.

Best For:

Organizations that prioritize deep model performance insights and data-driven improvements at scale.

3. Weights & Biases (W&B) – Best for Experiment Tracking

Weights & Biases is built for teams that need detailed tracking, comparison, and reproducibility across ML experiments.

What are the Key LLM Observability & Monitoring Features? 

• Experiment logging records hyperparameters and metrics for easy model comparison.
  
  

• Collaboration tools enable real-time sharing across distributed teams.
  
  

• Artifact versioning tracks datasets and models to prevent duplication.
  
  

• Framework integrations connect seamlessly with PyTorch, TensorFlow, and others.

What is the Pricing? 

Cloud-Hosted Pricing

• Free – $0/mo
  
  Core experiment tracking for individuals and small teams.
  
  Versioning, metrics visualization, and community support included.
  
  

• Pro – $60+/mo per user
  
  Collaboration-focused tools for growing teams.
  
  Unlimited projects, notifications, CI/CD integration, and role-based access.
  
  

• Enterprise – Custom
  
  Advanced observability with compliance and security features.
  
  Single-tenant deployment, HIPAA compliance, private networking, and priority support.

Pricing for Privately Hosted:

• Personal – $0/mo
   

  • Core experiment tracking for individuals.
    
    

  • Registry and lineage tracking included.
    
    

  • Run a W&B server locally on any machine with Docker and Python installed.
    
    

  • For personal projects only. Corporate use is not allowed.

• Advanced Enterprise – Custom
   

  • Flexible deployment and privacy controls for large organizations.
    
    

  • HIPAA compliance, private networking, and customer-managed encryption included.
    
    

  • Single Sign On, audit logs, and enterprise support with custom roles.
    
    

  • Run a W&B server locally on your own infrastructure with a free enterprise trial license.

Pros:

• User-friendly interface facilitates easy experiment tracking.
  
  

• Robust visualization tools aid in comparing model runs effectively.
  
  

• Wide integration support enhances compatibility with various ML frameworks.

Cons:

• Performance issues observed, especially with large datasets.

• Documentation gaps may necessitate external resources for troubleshooting.

• Limited offline capabilities could hinder usage in restricted environments.

Best For:

Teams that need structured experiment tracking and reproducibility for research and development workflows.

$42M Backed Arthur Chose Upsolve — Here’s Why

Arthur embeds BI for model & LLM observability and monitoring at Fortune 100 companies | Upsolve AI

4. LangSmith – Best for Debugging and Improving LLM Workflows

LangSmith is tailored for teams that need fine-grained debugging and observability tools to refine AI-driven workflows efficiently.

What are the Key LLM Observability & Monitoring Features? 

• Trace visualizations clarify how requests and responses are connected.
  
  

• Prompt debugging helps identify issues to reduce hallucinations and inconsistencies.
  
  

• Error monitoring spots failed outputs for faster troubleshooting.
  
  

• Usage analytics helps teams optimize resource allocation and cost.

What is the Pricing? 

• Developer – Free
   

  • Perfect for individual developers and hobby projects.
    
    

  • Includes 5,000 base traces per month, access to tracing and monitoring tools, and prompt improvement features.
    
    

• Plus – $39/month per seat
   

  • Best for small teams building reliable AI agents.
    
    

  • Offers up to 10 seats, 10,000 base traces per month, higher rate limits, and email support for troubleshooting.
    
    

• Enterprise – Custom Pricing
   

  • Tailored for large organizations with advanced deployment needs.
    
    

  • Provides flexible cloud or self-hosted options, SSO, role-based access control, dedicated engineering support, and SLAs.

Pros:

• Comprehensive debugging tools assist in identifying and resolving issues.
  
  

• Detailed trace views provide clarity on model interactions.
  
  

• Resource-aware analytics aid in optimizing performance and costs.

Cons:

• Technical setup may require onboarding and training.
  
  

• Limited offline functionality for teams in constrained environments.
  
  

• Scaling to enterprise levels may need additional infrastructure planning.
  
  

Best For: 

Organizations focused on debugging and refining AI workflows with performance and cost insights.

Fiddler AI

Fiddler AI helps teams build trust in AI by making models explainable, transparent, and aligned with regulations.

What are the Key LLM Observability & Monitoring Features?

• Explainability Workflows: Walks users through interpreting model predictions with practical, step-by-step guidance.
  
  

• Bias Attribution Engine: Identifies data and feature sources of bias, helping teams address fairness issues.
  
  

• Adaptive Drift Alerts: Learns from patterns to highlight real, actionable deviations in model performance.
  
  

• Governance Scorecards: Offers live dashboards that track compliance, fairness, and overall model health.
  
  

• Scenario Simulation Studio: Lets teams test input changes and see their effects before deploying models.
  
  

• Federated Learning Support: Facilitates privacy-preserving, distributed model training across locations.

What is the Pricing? 

Growth – $1,000+/mo

Basic observability with embedded dashboards, support for 50 tenants, CSV/PDF exports, and app embedding.

Professional – $2,000+/mo

Enhanced insights with unlimited dashboards, scheduled reports, AI-driven analytics, and usage tracking.

Enterprise – Custom

Full observability for complex workflows, unlimited tenants, advanced integrations, SSO, and compliance tools.

Best For:

Enterprises in compliance-heavy sectors aiming for transparent, fair, and explainable AI-driven decision-making.

Pros

• Explainability Support: Users appreciate the detailed workflows that help interpret AI model predictions.
  
  

• Compliance Tools: Strong regulatory tracking and bias checks assist teams in meeting industry requirements.
  
  

• Performance Insights: Adaptive alerts and scenario testing help maintain model accuracy and robustness.

Cons

• Usability Challenges: Beginners struggle with navigation due to limited tutorials and onboarding guidance.

• No Free Trial: Lack of a limited-feature version makes it harder for new users to explore before committing.

Best For: Enterprises and regulated industries needing AI explainability, compliance, and real-time model monitoring.

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Who Should and Shouldn’t Use LLM Observability & Monitoring Tools

Who Should Use Them

• Enterprises scaling AI to monitor performance, control costs, and ensure compliance.
  
  

• Data science teams need deeper insights and faster debugging.
  
  

• Regulated industries like healthcare and finance require strict compliance.
  
  

• Startups experimenting with AI that want to catch issues early.

Who Shouldn’t Use Them

• Businesses with simple AI needs that don’t require advanced monitoring.
  
  

• Small teams on tight budgets where enterprise tools are overkill.
  
  

• Projects without real-time or compliance demands that don’t need complex setups.

Conclusion

LLMs are driving more and more critical processes, but without proper observability, teams struggle to understand their behavior, fix problems early, or ensure models stay reliable at scale.

Upsolve helps by offering a complete observability solution that integrates seamlessly into AI workflows:

• All-in-one dashboards simplify monitoring across tools and teams.
  
  

• Transparent decision tracking shows how models reach conclusions.
  
  

• Actionable insights guide teams to improve performance and avoid costly errors.

While many tools focus only on one aspect like error detection or data drift, Upsolve provides full-spectrum observability across models, workflows, and datasets, making it perfect for enterprises that rely on LLMs and need trust, efficiency, and control.

Talk to us and explore how Upsolve can transform your AI operations!

FAQs

Q1. What’s the difference between ML observability and LLM observability?

ML observability tracks traditional models like fraud detection or recommendation systems, while LLM observability focuses on generative AI’s unique risks, hallucinations, bias, cost inefficiencies, and compliance challenges.

Q2. Do small teams need LLM observability tools?

Yes, even small teams benefit to control costs, catch errors early, and ensure safe outputs. Platforms like Upsolve scale well for teams of all sizes.

Q3. How fast can companies see results?

With proper integration, results are often visible within weeks, especially through real-time dashboards and actionable alerts.

Q4. Are these tools compliant with regulations?

Leading LLM observability tools provide built-in compliance features including GDPR, HIPAA, SOC2, helping organizations meet regulatory requirements effortlessly.

Q5. Can observability tools also improve prompts and models?

Yes, feedback loops from observability platforms help refine prompts, retrain models, and continuously optimize performance.

Q6. Are LLM observability tools really used in startups and companies?

Yes. Startups and enterprises use LLM observability tools to ensure AI stays reliable, efficient, and compliant.