Top 5 Enterprise LLM Observability Platforms in 2026

TL;DR

• LLM observability provides visibility into the performance, quality, cost, and security of AI applications, which traditional monitoring tools cannot capture.
• Enterprise platforms are evaluated on tracing depth, evaluation capabilities, security and governance features, cost management, and deployment flexibility.
• AI Gateways like Bifrost provide observability as a function of centrally routing all AI traffic, capturing complete data without SDK-level changes.
• APM extensions from vendors like Datadog and New Relic integrate LLM tracing into existing infrastructure monitoring stacks.
• AI-native platforms like Arize AI and Langfuse offer deep, purpose-built tracing and evaluation features, often with open-source options.

LLM observability has become a critical component of the enterprise AI stack. Traditional application performance monitoring (APM) can confirm that an API returned a 200 OK status, but it cannot tell you if the model's response was a hallucination, if it leaked sensitive data, or if a minor prompt change just increased costs by 300%. LLM observability platforms are designed to answer these questions by tracking the inputs, outputs, and internal states of AI applications.

This article compares the top five enterprise LLM observability platforms for 2026, assessing them on the features that matter most for production AI: tracing, evaluation, cost control, security, and integration. The platforms fall into three categories: AI gateways, APM extensions, and AI-native tracing tools.

Key Criteria for Evaluating Enterprise LLM Observability

Before comparing the tools, it's important to establish a framework. For enterprise use, an LLM observability solution must go beyond basic logging and provide robust, scalable, and secure insights.

Criteria	Description	Why It Matters for Enterprises
Full Request & Response Tracing	Captures the entire payload, including prompts, responses, model parameters, tool calls, and retrieval steps.	Essential for debugging, auditing, and understanding model behavior. Incomplete data leaves blind spots.
Evaluation & Quality Scoring	Capabilities to assess outputs against defined metrics (e.g., faithfulness, relevance, toxicity) via automated evaluators or human feedback.	Moves beyond operational metrics (latency, errors) to measure the actual quality and safety of AI responses.
Cost & Token Tracking	Granular visibility into token consumption and cost per request, aggregated by user, project, or model.	Enables cost control, budget enforcement, and identification of expensive outliers before they impact the bottom line.
Security & Governance	Features like PII/secrets redaction, access controls (RBAC), audit logs, and policy enforcement (guardrails).	Critical for compliance, protecting sensitive data, and managing risk in regulated industries.
Integration & Deployment	Support for OpenTelemetry, compatibility with existing stacks (APM, data lakes), and flexible deployment options (cloud, VPC, self-hosted).	Reduces vendor lock-in, integrates with existing workflows, and meets enterprise data residency and security requirements.

The Top 5 Platforms Compared

This list assesses the leading platforms, highlighting their strengths and ideal use cases for enterprise teams.

1. Bifrost: Observability Through a Unified AI Gateway

Bifrost is a high-performance, open-source AI gateway that provides observability as a native function of its architecture. By centralizing all LLM traffic through a single point, Bifrost captures complete, structured data for every request and response automatically, without requiring developers to instrument each application with an SDK.

Bifrost's approach is powerful for enterprises because it guarantees 100% visibility. Since all requests—from any team, application, or model—must pass through the gateway, nothing is missed. This makes it a strong foundation for governance and cost control.

Key Features:

• Built-in, Asynchronous Logging: Bifrost logs the full request context—inputs, outputs, tokens, cost, latency, provider, and model—with negligible impact on performance.
• OpenTelemetry & Prometheus Exports: Natively exports traces and metrics to existing monitoring systems like Datadog, New Relic, Grafana, and Honeycomb, integrating seamlessly into established enterprise stacks.
• Integrated Governance and Security: Because observability is part of the gateway, the same platform enforces governance controls. Virtual keys, budgets, rate limits, and guardrails are applied to the same traffic being observed.
• Enterprise-Grade Guardrails: Connects to a wide range of content safety providers, including AWS Bedrock Guardrails, Azure Content Safety, and Google Model Armor, to enforce security policies on live traffic.
• Endpoint Governance with Bifrost Edge: A key differentiator is Bifrost Edge, which extends the gateway's observability and security policies to AI usage on employee machines, covering desktop apps and browser-based AI to mitigate shadow AI risks.

Best for: Enterprises that need a centralized control plane for all AI traffic, combining comprehensive observability with robust security, governance, and cost management in a single platform. Its high-performance, deployment flexibility (including in-VPC and air-gapped environments) makes it ideal for regulated industries and mission-critical applications.

2. Datadog Agent Observability

Datadog is a dominant player in the traditional APM and infrastructure monitoring space. Its Agent Observability (formerly LLM Observability) product extends its existing platform to cover AI applications. For companies already invested in the Datadog ecosystem, this offers a path to LLM monitoring with minimal vendor sprawl.

Datadog's core strength is its ability to correlate LLM traces with the rest of the application stack. Teams can see how a model's latency impacts overall service performance, linking AI behavior to infrastructure metrics, logs, and user experience data in one place.

Key Features:

• Unified Monitoring: Views LLM performance alongside application and infrastructure metrics.
• Auto-Instrumentation: Provides libraries for popular frameworks to automatically capture trace data.
• Built-in Quality & Safety Checks: Includes out-of-the-box evaluations for metrics like toxicity and topic relevance.
• Cost and Token Tracking: Monitors usage to help manage expenses.

Best for: Organizations already standardized on Datadog for their primary monitoring needs. It provides a single pane of glass for teams who want to add LLM visibility to their existing APM workflows without onboarding a new vendor.

3. New Relic AI Observability

Similar to Datadog, New Relic has extended its established APM platform to include AI observability. It leverages the OpenTelemetry standard, which gives teams more flexibility and reduces vendor lock-in compared to proprietary instrumentation.

New Relic focuses on providing a holistic view of the AI stack, from the application layer down to the infrastructure. It offers pre-built dashboards and integrations for popular AI frameworks and providers, allowing teams to get started quickly.

Key Features:

• OpenTelemetry Native: Built on the open standard for telemetry, ensuring data portability.
• Full-Stack Visibility: Connects AI layer performance with application and infrastructure health.
• Real-time Insights: Offers dashboards for tracking performance, errors, and cost issues.
• Model Context Protocol (MCP) Monitoring: Provides visibility into the entire MCP request lifecycle for agentic applications.

Best for: Companies that use New Relic as their APM and want to add LLM monitoring within that ecosystem, especially those who prioritize open standards like OpenTelemetry.

4. Arize AI

Arize AI is an AI-native observability platform that offers deep capabilities for both LLM evaluation and production monitoring. It is split into two main products: Phoenix, a popular open-source library for tracing and evaluation during development, and AX, the enterprise-scale monitoring platform.

Arize's strength lies in its comprehensive, model-centric approach. It's designed from the ground up to handle the nuances of AI systems, with strong features for monitoring embedding drift, evaluating RAG pipeline performance, and debugging complex agent workflows.

Key Features:

• Open-Source Foundation: Phoenix allows teams to start with a powerful, self-hostable tool for tracing and evaluation.
• Enterprise-Scale Monitoring: Arize AX is built for high-volume production environments, processing trillions of events per month for its customers.
• Deep Evaluation Tooling: Offers advanced evaluation capabilities, including support for LLM-as-a-judge and integration with various evaluation frameworks.
• OpenTelemetry-Based: Uses the OpenInference standard, which is built on OpenTelemetry, for vendor-agnostic instrumentation.

Best for: ML-focused teams and enterprises that require deep, AI-native tracing and evaluation capabilities across the entire model lifecycle. The open-source entry point makes it accessible, while the enterprise platform provides the scale and features needed for large-scale production deployments.

5. Langfuse

Langfuse is an open-source LLM engineering platform that combines observability, prompt management, and evaluation in one integrated system. It has gained significant traction for its developer-friendly workflow and comprehensive feature set.

Langfuse provides detailed, hierarchical traces that capture every step of an LLM application's execution, from model calls to tool use and retrieval steps. Its open-source nature allows for self-hosting, giving enterprises full control over their data—a critical requirement for privacy and compliance.

Key Features:

• Open Source and Self-Hostable: Gives teams complete data sovereignty and control.
• Integrated Platform: Combines tracing, prompt management, and evaluation in a single workflow.
• Detailed Tracing: Offers hierarchical views of complex agent interactions.
• Framework Agnostic: Integrates with various LLM frameworks and models.

Best for: Teams looking for a powerful, open-source platform that covers the entire LLM development lifecycle. Its self-hosting capability makes it a strong choice for organizations with strict data residency requirements or those who prefer to build on an open-source stack.

Frequently Asked Questions

What is the difference between LLM observability and traditional APM?

Traditional Application Performance Monitoring (APM) tracks operational metrics like latency, error rates, and resource usage. LLM observability goes further by analyzing the quality and content of AI responses, tracking things like hallucinations, relevance, token costs, and potential data leakage—issues that traditional APM cannot detect.

Why is OpenTelemetry important for LLM observability?

OpenTelemetry is an open standard for instrumenting, generating, and collecting telemetry data (traces, metrics, logs). Using an OpenTelemetry-native platform prevents vendor lock-in, as the instrumentation in your code is not tied to a specific vendor's SDK. This allows enterprises to switch observability backends without re-instrumenting their applications.

How does an AI gateway provide observability?

An AI gateway acts as a central proxy for all LLM API requests. Because every request and response flows through it, the gateway can log the complete data payload for every transaction automatically. This provides 100% visibility without requiring developers to add observability code to each individual application.

Can these platforms detect security issues like prompt injection?

Yes, advanced LLM observability platforms can help detect security threats. By monitoring prompts for anomalous patterns and applying guardrails, they can flag potential prompt injection attacks or attempts to leak sensitive data. Platforms with integrated guardrail systems provide an active defense layer, not just passive monitoring.

How do I choose the right platform for my organization?

The right choice depends on your existing infrastructure and primary needs.

• If you need a unified control plane for governance, security, and observability, an AI gateway like Bifrost is the most comprehensive solution.
• If you are already heavily invested in an APM platform, using the LLM module from Datadog or New Relic is the path of least resistance.
• If your primary need is deep, AI-native evaluation and tracing, a purpose-built platform like Arize AI or Langfuse is likely the best fit.

Next Steps

Choosing an enterprise LLM observability platform is a strategic decision that impacts reliability, security, and cost. Gateways provide a holistic control plane, APM extensions offer integration with existing stacks, and AI-native tools deliver specialized depth. Teams evaluating these options can request a Bifrost demo to see how gateway-based observability works or explore the open-source repository to get started.