Top 10 LLM observability tools: Complete guide for 2025

Large language models are now ubiquitous in production AI applications. If you don't have some AI feature in 2025, are you even a tech company? With AI features hitting production, observability has become critical for building reliable AI products that users can trust. LLM observability goes far beyond basic logging, requiring real-time monitoring of prompts and responses, tracking token usage, measuring latency, attributing costs, and evaluating the effectiveness of individual prompts across your entire AI stack. Without robust observability frameworks, teams face significant risks: AI systems may fail silently, generate harmful outputs, or gradually drift from their intended behavior, degrading quality and eroding trust.

This guide explores the fundamentals of LLM observability, showing what to prioritize when selecting platforms and discovering the leading observability tools in 2025. At Braintrust, we offer the leading LLM observability platform combining integrations with all major LLMs and AI frameworks, paired with intuitive interfaces that let everyone on your team understand how AI features are functioning. While other solutions may log and store events, Braintrust empowers teams to take action on their logs.

Understanding LLM observability in production

LLM observability monitors Large Language Model behavior in live applications through comprehensive tracking, tracing, and analysis capabilities. LLMs now power everything from customer service chatbots to AI agents that generate code and handle complex multi-step tasks. Observability helps teams understand system performance effectively, detect issues before users notice problems, and maintain operational excellence at scale.

Modern LLM observability extends far beyond traditional application monitoring. They track prompts, responses, and token usage. Teams monitor latency and attribute costs accurately. They analyze error patterns and assess quality. Effective platforms capture complete LLM interaction lifecycles, tracking everything from initial user input to final output delivery, making every step in the AI pipeline visible.

LLM observability combines real-time monitoring with historical analysis to give teams a complete picture. Real-time dashboards track current system performance, alert on anomalies, and visualize model behavior as it happens, while historical analysis identifies trends over time, optimizes performance based on patterns, enables compliance reporting, and supports sophisticated pattern recognition. Advanced platforms combine both approaches intelligently, allowing teams to maintain service quality while iterating quickly on improvements.

The process monitors production performance through detailed metrics, analyzes user interactions, and detects drift before it impacts users. Braintrust provides comprehensive ecosystems that integrate seamlessly with popular AI frameworks, helping teams maintain transparency and control at scale.

The mechanics of modern LLM observability

LLM observability uses sophisticated monitoring architectures to capture model behavior across all environments. The process involves three core components: data collection, analysis pipelines, and actionable insights delivery.

Comprehensive data collection and tracing

Observability begins with detailed telemetry capture. This includes prompt content and model responses. Teams track input and output token usage. They measure latency and error rates. Contextual metadata provides additional insights into user sessions, application states, and environmental conditions. Modern observability platforms support tracing across complex workflows, linking operations using intuitive Thread views to enable complete understanding of multi-step AI agent interactions. Tools like Braintrust have made this level of comprehensive tracing accessible to development teams of all sizes.

Intelligent analysis and pattern detection

Advanced frameworks implement analysis tools that process monitoring data to identify patterns, detect anomalies, and find optimization opportunities. Cost analysis tracks spending across users and models, performance monitoring identifies latency bottlenecks, and quality assessment detects hallucinations and bias in model outputs. Platforms such as Braintrust have built-in AI agents that can help you establish baselines and detect deviations from expected behavior, even at enterprise-scale.

Actionable insights and alerting

Production systems generate actionable insights through intuitive dashboards that visualize performance trends and cost analytics in real time. Teams configure alerts for various conditions including cost overruns, latency spikes, and error rate increases that trigger immediate notifications. Advanced platforms like Braintrust use webhooks to integrate with incident management tools such as PagerDuty and Slack, ensuring rapid response when issues arise.

The observability process creates continuous feedback loops that inform model optimization, guide prompt engineering decisions, and enable better infrastructure choices. This approach improves AI application performance while reducing costs and maintaining quality.

Selecting the perfect LLM observability platform

Choosing the right observability platform is crucial because it determines whether AI applications achieve the reliability users expect. As LLMs become mission-critical components of modern software, visibility into their behavior, performance, and health matters more than ever.

Seamless integration and developer productivity

Effective observability requires frictionless integration with your existing stack. Platforms should support popular frameworks like LangChain, LlamaIndex, Vercel AI SDK, and OpenTelemetry without requiring extensive configuration or code changes. Native SDKs reduce configuration overhead significantly. Automatic instrumentation saves implementation time, letting developers focus on building features instead of setting up monitoring infrastructure. Solutions like Braintrust exemplify this approach by offering pre-built integrations that work out of the box.

Multi-dimensional monitoring capabilities

Choose platforms offering comprehensive monitoring coverage across all dimensions of LLM behavior. Essential capabilities include real-time latency tracking, token usage analytics for cost visibility, error monitoring, and quality assessment of model outputs. Advanced platforms provide alerting that identifies anomalies and recognizes patterns, surfacing issues before they impact users. Braintrust and similar tools have made multi-dimensional monitoring accessible through unified dashboards that present all critical metrics in one place.

Production-grade reliability and alerting

Enterprise tools must support high-availability monitoring that stays up even when other systems fail. They need configurable alerts for performance issues, production dashboards that update in real time, anomaly detection that catches unusual patterns, and automated CI/CD gates that help ship without regressions. Integration with tools like Slack and PagerDuty helps teams respond quickly when problems arise.

Scalability and performance impact

Observability platforms must handle production workloads without becoming bottlenecks themselves. They shouldn't impact application performance in ways users notice. Consider tools with asynchronous processing capabilities that don't block requests, intelligent sampling that reduces overhead while maintaining visibility, and cloud-native architecture that ensures low latency for users globally. Platforms like Braintrust demonstrate how properly architected observability can maintain minimal performance overhead even at scale.

Enterprise security and governance

Enterprise deployments require stringent security measures that protect sensitive data and meet compliance requirements. Look for SOC2 certification and GDPR compliance, configurable data retention policies that provide control over how long data is stored, support for hybrid deployment when regulations require it, and API-level access controls that protect sensitive prompts and outputs. Leading platforms including Braintrust prioritize security certifications and compliance as foundational requirements.

A comprehensive platform accelerates development velocity while ensuring production transparency and scalability. Prioritize seamless integration and actionable insights when making your selection.

Leading LLM observability platforms for 2025

The LLM observability landscape has matured significantly over the past year, with platforms now offering sophisticated monitoring and optimization capabilities that serve different monitoring needs. These tools enable transparent AI deployment at scale. Here is our list of the industry-leading platforms:

1. Braintrust

Braintrust defines the category for LLM observability, trusted by leading companies globally including Notion, Stripe, Vercel, Airtable, Instacart, and Zapier. Braintrust represents the gold standard for teams building reliable AI applications.

The unified AI development platform

Braintrust integrates evaluation, prompts, and monitoring seamlessly into a single platform, eliminating the development friction that comes from juggling multiple tools. Engineers flow smoothly from analyzing logs to testing improvements to deploying changes, fundamentally changing how teams iterate on AI systems.

Bidirectional sync connects the UI and code, enabling both technical and non-technical team members to collaborate effectively. Product managers contribute through intuitive interfaces while engineers maintain full API control for complex workflows.

Comprehensive framework integration ecosystem

Braintrust supports 13+ major frameworks natively, including OpenTelemetry, Vercel AI SDK, OpenAI Agent SDK, Instructor, LangChain, LangGraph, Google ADK, Mastra, Pydantic AI, Autogen, Crew AI, and Cloudflare. Teams implement observability without rewriting code or wrestling with compatibility issues.

Integration requires minimal configuration effort in most cases. For example, the Vercel AI SDK needs just a single wrapAISDK() call, while OpenTelemetry provides automatic span conversion that captures complete context. Implementation takes hours, not weeks, helping teams achieve comprehensive observability quickly without derailing feature development.

Each integration serves production AI applications with automatic cost tracking, latency monitoring, evaluation metrics, production alerting, and tool tracing that work immediately. Framework-specific optimizations come preconfigured, eliminating guesswork.

Production-first architecture and performance

Brainstore powers Braintrust's technical advantage as a purpose-built database designed specifically for handling AI logs at scale. It delivers 80x faster query performance compared to traditional databases, a difference that becomes critical when analyzing millions of traces to debug production issues or optimize prompts.

Cloud-native architecture includes global CDN distribution that reduces latency regardless of user location. Asynchronous processing maintains application performance even under heavy logging loads. Intelligent filtering optimizes system overhead, ensuring comprehensive observability doesn't impact the user experience.

AI-powered assistance with Loop

Loop is an AI assistant that helps you analyze observability data through natural language. It can generate evaluation datasets, refine scoring systems, and optimize prompts based on your data—all through conversational interactions. Loop analyzes your logs at scale, surfacing insights from millions of traces that would take weeks to identify manually.

By processing your entire dataset and responding to natural language queries, Loop helps you identify patterns, optimize costs, and find performance bottlenecks faster than manual analysis. Teams accelerate their iteration cycles by asking Loop to analyze data, generate reports, and suggest improvements based on comprehensive analysis of production logs.

Enterprise security and compliance leadership

Security forms the foundation of Braintrust's architecture, not an afterthought. SOC 2 Type II certification ensures robust controls, GDPR compliance protects data privacy across jurisdictions, and API-level controls provide comprehensive protection for sensitive prompts and model outputs.

Deployment options include hybrid architectures that balance convenience with security. Data plane isolation enhances security by keeping sensitive data on your infrastructure. Self-hosting via Terraform offers complete flexibility, while Docker support simplifies deployment across different environments. Teams meet specific compliance requirements easily without compromising on features.

Revolutionary observability approach for AI systems

Traditional monitoring fails with AI applications because it assumes predictable, deterministic software behavior. Braintrust addresses the probabilistic nature of AI systems and handles the unique monitoring challenges they present.

Multi-dimensional monitoring covers all aspects of LLM behavior. It tracks inputs, behavior, and outputs across complete request lifecycles. Request-level tracing captures complete context including preprocessing steps, model configuration, raw responses, post-processing transformations, and final outputs delivered to users, making the entire AI pipeline transparent.

Semantic monitoring evaluates output meaningfulness rather than just technical metrics. Automated scoring provides scalable evaluation that checks factual accuracy, relevance to user intent, safety validation, and format compliance at a scale that manual review can't match. User feedback provides ground truth for continuous improvement.

Cost visibility and tracking

Braintrust automatically tracks and displays estimated LLM costs alongside token usage metrics for every request. The platform captures prompt tokens, completion tokens, and estimated costs, making it easy to understand the financial impact of your AI applications. Teams can filter and analyze costs using metadata fields, helping identify which parts of your application consume the most resources.

Flexible alerting system

Braintrust provides a webhook-based alerting system that triggers notifications when conditions you define are met on new logs. Using BTQL filters, teams can configure alerts for any metric or pattern in their data—from score thresholds to error conditions to custom business logic. Alerts integrate with Slack, PagerDuty, and 7,000+ apps via webhooks and Zapier, ensuring teams get notified through their preferred channels when issues arise.

Proven track record and customer success

Teams report 30% accuracy improvements within weeks of implementation. Development velocity increases up to 10x, delivering features faster and building competitive advantage. These aren't marketing claims but real results from production deployments.

Customer testimonials consistently highlight how Braintrust transforms experimental prototypes into production-ready applications that serve millions of interactions daily. Leading technology companies demonstrate the platform's effectiveness at scale.

TypeScript-first design and modern development

Braintrust provides native JavaScript support that reflects the reality of production AI systems today. TypeScript frameworks receive first-class treatment because customer-facing AI applications increasingly use modern web technologies.

TypeScript design ensures type safety throughout your observability code. Developers get intelligent autocomplete that accelerates development. Compile-time checking prevents common errors before they reach production. SDK design follows modern best practices that feel natural to JavaScript developers.

Investment and industry validation

Andreessen Horowitz and Greylock Partners back Braintrust financially, providing the resources needed for continued innovation. OpenAI's Greg Brockman validates the technical approach, lending credibility from one of AI's most respected voices. This strong backing ensures platform longevity and continued development.

Customer success proves technical viability at scale. Enterprise security meets the requirements of regulated industries. Strong investment backing combined with real-world results position Braintrust as the leader in the observability category.

2. Arize

Arize delivers advanced AI observability with comprehensive support for LlamaIndex, LangChain, DSPy, and multiple model providers. OpenTelemetry instrumentation provides flexibility for custom integrations. Phoenix evolved from ML monitoring origins, bringing mature practices to LLM observability.

Advanced drift detection: Phoenix monitors embedding drift effectively, tracking changes in vector representations that indicate semantic shifts in model behavior. Traditional metrics miss these subtle but important issues that can degrade quality over time.

RAG-specific observability: The platform monitors RAG pipelines with special attention to retrieval quality. It analyzes retriever performance thoroughly, making knowledge gaps apparent quickly. Content relevance tracking improves the quality of retrieved documents.

Enterprise monitoring infrastructure: Phoenix supports production-grade deployment reliably with real-time dashboards that provide immediate visibility, automated alerting that catches issues early, and cost attribution that enables effective budget management.

3. Comet Opik

Comet's Opik provides comprehensive LLM observability with native support for OpenAI, LangChain, LlamaIndex, DSPy, and agent frameworks. The platform integrates smoothly with existing ML workflows for teams already using Comet.

Agent-focused monitoring: Opik monitors AI agents with special capabilities for tracking multi-step reasoning processes, analyzing tool usage patterns, and understanding collaborative workflows. Agent behavior becomes visible in ways traditional monitoring can't capture.

Open-source transparency: The open-source foundation ensures transparency and allows teams to self-host completely when requirements demand it. Managed services reduce operational overhead for teams that prefer cloud hosting. Enterprise features add value without sacrificing openness.

Development workflow integration: The platform integrates with CI/CD pipelines naturally. Automated monitoring catches regressions early in development. Pull request validation prevents quality issues from reaching production.

4. Helicone

Helicone provides proxy-based observability supporting OpenAI, Anthropic, and Google Gemini with a focus on simplicity. The platform makes monitoring accessible to teams without deep observability expertise.

Instant monitoring architecture: The proxy approach enables immediate observability by simply changing API endpoints without touching application code. Existing applications gain complete visibility instantly, making Helicone good for teams that need monitoring now.

Multi-provider visibility: Unified monitoring spans multiple model providers, letting you compare performance and costs easily across OpenAI, Anthropic, and others. Quality metrics appear in unified dashboards, providing a single view that simplifies management across your entire AI stack.

Cost intelligence: Helicone is strong at cost tracking with detailed usage analytics, budget alerts that prevent overruns, and virtual API keys that enable accurate cost attribution across teams, projects, or customers.

5. Langfuse

Langfuse offers open-source integrations with support for OpenAI, LangChain, and LlamaIndex, providing essential observability features while maintaining complete transparency through its open-source model.

Transparency: Open-source code provides transparency that proprietary tools can't match. Teams customize monitoring logic freely to fit their specific needs. Functionality extensions are possible when standard features aren't enough. Complete control remains with your team.

Prompt performance tracking: Prompt versioning tracks changes over time. Prompt testing capabilities improve optimization by comparing prompt variations scientifically. Performance tracking spans all prompt variations to identify what works best.

Flexible deployment options: Various deployment models provide flexibility for different requirements. Self-hosting maintains complete control over data and infrastructure. Cloud deployment reduces operational overhead for smaller teams. Hybrid architectures meet complex requirements.

6. MLflow

MLflow provides enhanced LLM support that extends beyond traditional ML workflows, bringing mature experiment tracking to modern AI systems. Auto-tracing works with major frameworks while multi-provider monitoring adds value for teams using multiple models.

Unified ML/AI observability: MLflow monitors both classical ML models and modern LLMs from a single platform, simplifying toolchain management for teams working across both paradigms. Classical models and LLMs coexist in the same workflow.

Comprehensive lifecycle management: End-to-end versioning tracks everything from experiments to production deployments. Experiment tracking captures all changes and their results. Deployment monitoring ensures reliability once models ship to users.

Enterprise infrastructure integration: MLflow integrates seamlessly with existing enterprise infrastructure including data warehouses and business intelligence tools, connecting AI observability to broader analytics workflows naturally.

7. Galileo AI

Galileo provides enterprise-focused AI observability with support for major LLM providers and comprehensive agent framework integration, serving organizations with strict security and compliance requirements.

Agent monitoring: Sophisticated capabilities monitor AI agents through multi-step reasoning assessment, tool usage validation, and collaborative workflow visibility. Complex agent behaviors become transparent and debuggable.

Enterprise security focus: Advanced access controls protect sensitive data throughout the platform. Comprehensive audit logging tracks all activity for compliance purposes. Industry compliance certifications meet regulatory requirements for enterprises in regulated industries.

Custom monitoring models: Proprietary models like Luna-2 enhance assessment accuracy for specific monitoring tasks. Purpose-built models improve consistency compared to general-purpose LLMs used for evaluation.

8. DeepEval

DeepEval focuses on testing framework integration with pytest-like functionality designed specifically for LLM applications. Basic LlamaIndex support works adequately, with development monitoring receiving priority over production observability.

Developer-first monitoring: Pytest-style testing feels immediately natural to Python developers. Existing workflows integrate easily without learning entirely new paradigms. The learning curve stays minimal, accelerating adoption.

Synthetic data monitoring: Synthetic test datasets enable comprehensive coverage even when real-world data is limited or unavailable. Testing becomes possible early in development before production data exists.

CI/CD pipeline integration: Development pipelines integrate seamlessly with automated monitoring that prevents regressions. Quality gates protect production by catching issues before deployment.

9. RAGAS

RAGAS provides specialized RAG observability with deep LlamaIndex support and basic LangChain compatibility, focusing exclusively on RAG monitoring rather than trying to cover all LLM use cases.

RAG-specific metrics: Research-backed metrics serve RAG systems with measurements like faithfulness scoring that ensures accuracy and answer relevancy scoring that improves quality. Specialized metrics outperform generic evaluation approaches.

Research-backed methodology: Academic research validates monitoring approaches through peer-reviewed studies. Best practices guide implementation, giving teams confidence in evaluation methods.

Specialized domain focus: RAG applications receive unmatched depth of monitoring. Document-heavy systems benefit greatly from specialized approaches. Focus on one domain enables excellence that generalist tools can't match.

10. OpenAI Evals

OpenAI Evals offers basic monitoring capabilities exclusively for OpenAI models through a CLI interface that provides simplicity. The template system enables standard benchmarks but lacks comprehensive production features.

Benchmark-focused monitoring: Standard academic benchmarks become easily accessible for research applications. Template-based evaluation enables benchmark comparisons across model versions.

Community registry: An open-source registry shares evaluation templates across the community. Community contributions expand available benchmarks, creating collective benefit from shared evaluation approaches.

Limited production use: Specific use cases work well within the tool's scope, but comprehensive production features are lacking. Multi-provider support doesn't exist, limiting usefulness for teams using multiple models.

The future of AI transparency: Why pick Braintrust?

LLMs now form the foundation of modern AI applications, making comprehensive observability mission-critical for any team shipping AI features to production. The right observability platform provides the essential visibility that lets teams deploy AI systems confidently, knowing they can understand and control behavior even as complexity scales.

Braintrust's comprehensive integration ecosystem leads the industry in breadth and depth. Technical excellence ensures production reliability under demanding workloads. Specialized tools like RAGAS and DeepEval excel within their narrow focus areas, serving specific use cases well.

Choose platforms with comprehensive framework integration that works with your existing stack. Production monitoring capabilities matter significantly since development-only tools create blind spots. Operational workflow alignment saves time by reducing context switching. Robust observability infrastructure pays substantial dividends through dramatically decreased debugging time and measurably improved operational efficiency.

The observability landscape continues evolving rapidly as LLMs become more complex and widely deployed. Broad framework support remains essential as new frameworks emerge. Production-grade reliability ensures success when stakes are high. Developer-friendly integration accelerates adoption across teams. Choose tools that scale appropriately with your ambitions, then build transparent, effective AI deployments that users trust.

Frequently asked questions

What are the best LLM observability tool alternatives to Arize Phoenix?

Braintrust surpasses Arize Phoenix with native support for 13+ frameworks and setup that takes hours. The platform uniquely combines evaluation with monitoring in a unified workflow. Phoenix users frequently switch to Braintrust for better performance and cross-functional collaboration. Braintrust's purpose-built infrastructure delivers 80x faster query performance at scale.

Which LLM observability platforms detect quality drift most effectively?

Braintrust provides comprehensive monitoring tools that help teams identify drift and quality issues. Loop, the AI assistant, can analyze large volumes of logs to surface patterns and anomalies when you ask it to investigate. Teams can set up custom alerts using BTQL filters to monitor for quality degradation based on score thresholds, error patterns, or any metric they define. The platform combines automated scoring, user feedback, and detailed trace analysis to give teams visibility into quality changes over time.

What's the most comprehensive LLM evaluation and observability platform?

Braintrust provides the only platform that combines evaluation, monitoring, and optimization in a single system. Teams eliminate multiple disconnected tools that create friction and data silos. Workflow efficiency improves dramatically when evaluation results flow directly into production monitoring. No other platform matches this integration depth for end-to-end AI development.

Which LLM observability platforms support full on-premise deployment?

Braintrust offers a unique hybrid architecture that separates the data plane (where your sensitive data lives) from the control plane (the UI and management layer). The data plane can be deployed in your own AWS, Azure, or infrastructure using Terraform or Docker, ensuring your logs, traces, and prompts never leave your environment. Meanwhile, you access the hosted Braintrust UI which queries your data plane directly from your browser. This architecture gives you complete data isolation while maintaining the convenience of a managed UI. Teams maintain full control over data retention, access policies, and infrastructure choices.

How do LLM observability tools secure sensitive prompt data?

Enterprise platforms like Braintrust implement encryption for data at rest and TLS for data in transit. Role-based access controls every operation at a granular level. SOC 2 Type II certification ensures security controls are independently validated and audited. Comprehensive audit logs track every access and modification for compliance reporting.

What hybrid deployment options exist for LLM observability platforms?

Braintrust pioneered hybrid architectures where data plane isolation keeps sensitive information on your infrastructure. The control plane provides convenient management through hosted services. This architecture satisfies security teams who need data residency while maintaining operational simplicity. Performance remains optimal because data doesn't traverse unnecessary network hops.

Which LLM observability tools integrate with OpenTelemetry?

Braintrust provides automatic span conversion that works without manual configuration. Intelligent attribute mapping extracts relevant LLM-specific information from OpenTelemetry traces automatically. The integration requires minimal setup since Braintrust understands OpenTelemetry's semantic conventions natively. Existing observability stacks connect seamlessly while gaining LLM-specific insights.

What LLM observability platforms work best with LangChain?

Braintrust provides zero-configuration LangChain integration that works immediately after wrapping your chain. The platform captures complete workflow context including all intermediate steps. LangGraph support adds specialized capabilities for agent workflows with loops and conditional execution. Framework-specific optimizations understand LangChain's architecture deeply for accurate cost tracking.

How do LLM observability platforms integrate with existing monitoring tools?

Braintrust integrates with existing monitoring tools through webhooks and Zapier. Teams can send alerts to Slack, PagerDuty, and 7,000+ other apps when specific conditions are met in their logs. Webhook configuration takes just minutes through the UI. Teams maintain existing workflows without disruption while gaining new LLM-specific visibility.

Which LLM observability platforms offer the best free tiers?

Braintrust provides the most generous free tier with enough capacity to evaluate thoroughly. No credit card is required initially, removing friction from getting started. Production features work immediately rather than being artificially limited. Migration from competitors stays risk-free since teams can run both systems in parallel.

What's the best LLM observability tool for mid-size engineering teams?

Braintrust scales perfectly for teams of ten to fifty engineers without artificial constraints. Usage-based pricing prevents overpaying during experimentation while scaling naturally as applications grow. No artificial seat limits exist that force difficult decisions about access. Performance stays consistent as request volume increases from thousands to millions per day.

How do LLM observability platforms handle latency spikes?

Braintrust captures latency metrics for every request, making it easy to spot performance issues. Teams can filter and analyze traces to identify which prompts, models, or inputs cause slowdowns. Using BTQL filters, teams can configure alerts that trigger webhooks to Slack or PagerDuty when latency exceeds defined thresholds. The detailed trace view shows exactly what happened during slow requests, helping teams diagnose root causes.

Which LLM observability platforms best support team collaboration?

Braintrust excels through bidirectional sync that keeps UI and code perfectly aligned. Non-technical team members contribute easily through intuitive visual interfaces. Engineers maintain complete programmatic control through comprehensive APIs. Comments and annotations enable discussions directly on prompts, traces, and evaluation results.

What's the best LLM observability tool for healthcare applications?

Healthcare requires strict compliance features that meet regulatory requirements for patient data. Braintrust offers self-hosted deployment options with complete data isolation and SOC 2 Type II certification. Comprehensive audit logs track every access and modification for regulatory review. Track medical accuracy rigorously since errors can have severe consequences.

How do LLM observability platforms support prompt experimentation?

Braintrust uniquely combines experimentation with production monitoring in a single workflow. Comprehensive version control tracks all changes with complete history. Teams can compare different prompt versions across experiments to see which performs best. The playground allows rapid iteration and testing before deploying changes to production.

Which LLM observability tools act as AI proxies?

Braintrust's AI proxy sits between your application and model providers (OpenAI, Anthropic, Google, AWS, Mistral, etc.), offering a unified API interface. It supports auto-caching (especially when temperature=0 or a seed is provided), unified parameter translation, logging, and API key management across providers.

What's the easiest LLM observability platform to implement?

Braintrust provides quick integration for most modern frameworks with pre-built integrations. Most frameworks need only a single wrapper function or decorator. Zero-configuration approaches eliminate the need for extensive instrumentation code and allow teams to get setup within hours.

What are implementation best practices for LLM observability tools?

Start with basic monitoring first rather than trying to implement every feature immediately. Add advanced features like custom scoring and complex alerting gradually as you understand system behavior. Set up alerts conservatively to avoid alert fatigue from too many false positives. Review metrics regularly in team meetings to build shared understanding. Establish baseline metrics early before problems occur so you know what normal looks like.