TL;DR
- LLM observability tracks not just system health (latency, errors) but also AI-specific signals like prompt performance, token costs, output quality, and security risks like prompt injection.
- The best platforms combine AI-native evaluation with existing APM, often using OpenTelemetry as a standard instrumentation layer for portability.
- Bifrost, an open-source AI gateway, offers the best solution by integrating high-performance routing and governance with built-in, real-time observability.
- Datadog and New Relic extend their market-leading APM platforms to cover LLM workflows, ideal for teams already invested in their ecosystems.
- Arize AI provides a powerful, AI-native platform focused on model evaluation and performance monitoring, particularly for tracing and resolving production issues.
Traditional application monitoring is necessary but insufficient for managing large language models in production. An application can appear healthy, with low latency and zero errors, while consistently producing low-quality, incorrect, or harmful outputs. LLM observability closes this gap by providing deep visibility into the entire request lifecycle, from prompt to final response, including model behavior, cost, and quality. Bifrost, an open-source AI gateway, is one of the leading solutions that provides this visibility at the infrastructure layer.
This guide compares the top five LLM observability platforms for enterprise teams, evaluating them on their ability to trace complex workflows, evaluate output quality, control costs, and integrate with existing enterprise systems.
Key Criteria for Evaluating LLM Observability Platforms
When selecting a platform, enterprises should look beyond simple dashboards and consider the depth of visibility and control offered. The key evaluation criteria are:
| Capability | Description | Why It Matters for Enterprises |
|---|---|---|
| End-to-End Tracing | Visibility into every step of a request: RAG retrievals, tool calls, chained model prompts, and data processing. | Quickly diagnosing the root cause of a bad response requires seeing the entire decision path, not just the final model output. |
| Cost & Usage Tracking | Granular monitoring of token consumption, cost-per-request, and usage patterns broken down by model, user, or feature. | AI spend can escalate quickly. Detailed cost attribution is essential for managing budgets and optimizing inefficient workflows. |
| Quality Evaluation | Automated and human-in-the-loop scoring of model outputs for issues like hallucinations, toxicity, bias, and relevance. | "Silent failures," where the model responds confidently but incorrectly, are a major business risk. Continuous evaluation is the primary defense. |
| Security & Compliance | Detection of prompt injection attacks, scanning for sensitive data (PII), and providing auditable logs for compliance. | AI applications are a new attack surface. Observability tools must provide security signals, not just performance metrics. |
| Integration & Extensibility | Support for OpenTelemetry, connections to existing APM/SIEM tools, and SDKs for major AI frameworks (e.g., LangChain). | The platform must fit into the existing enterprise stack without creating data silos or forcing vendor lock-in. |
The Top 5 Platforms Compared
Based on these criteria, here is a comparison of the top LLM observability platforms for enterprise use in 2026.
1. Bifrost
Bifrost is a high-performance, open-source AI gateway that provides observability as a core, integrated feature. By sitting between applications and LLM providers, it captures detailed telemetry for every request without requiring application-level instrumentation. This architecture makes it an ideal control plane for enterprise AI.
The platform’s built-in observability automatically logs complete request and response data, including prompts, model parameters, token usage, latency, and cost. This operates asynchronously, adding no latency to requests. For deeper integration, Bifrost provides native support for both Prometheus metrics and OpenTelemetry (OTLP) tracing, allowing teams to export rich AI telemetry into their existing monitoring systems like Grafana, Datadog, or New Relic.
Best for: Enterprise teams that need a unified platform for AI routing, governance, and observability. Its gateway-based approach provides a centralized control point for managing performance, cost, and security across all AI applications.
Key Features:
- Gateway-Level Visibility: Captures complete telemetry for every AI request automatically, without code changes in the application.
- Open Standards Support: Native exporters for OpenTelemetry and Prometheus ensure seamless integration with existing enterprise monitoring stacks.
- Integrated Governance & Security: Because observability is part of the gateway, it's easy to correlate performance data with security policies. Bifrost’s governance controls and Bifrost Edge extend this visibility and policy enforcement to AI traffic on employee endpoints, addressing shadow AI risks.
- High Performance: Built in Go, Bifrost is designed for high-throughput, low-latency workloads, ensuring the observability layer does not become a performance bottleneck.
2. Datadog
Datadog LLM Observability extends its industry-leading APM platform to provide visibility into AI applications. It is designed for teams already using Datadog for infrastructure and application monitoring, offering a single pane of glass for the entire stack.
The platform provides end-to-end tracing of LLM chains, helping teams identify root causes of errors and unexpected responses like hallucinations. It monitors operational metrics like latency and token usage to optimize performance and cost. A key strength is its out-of-the-box evaluations for quality and safety, including checks for topic relevance and toxicity, along with sensitive data scanning to mitigate privacy risks.
Best for: Organizations already standardized on Datadog for APM and infrastructure monitoring. It provides a familiar interface and correlates LLM performance directly with backend service health.
Key Features:
- Unified Platform: Combines LLM traces with existing application logs, metrics, and infrastructure monitoring in one place.
- Out-of-the-Box Evaluations: Pre-built checks for common issues like hallucinations, toxicity, and relevance.
- Prompt & Response Clustering: Automatically groups similar prompts to help identify usage patterns and performance trends.
- Security Integration: Includes sensitive data scanning to help prevent private information from being processed by models.
3. New Relic
New Relic's AI observability solution also integrates AI monitoring into its established platform, allowing for full-stack visibility. It provides a holistic view of the AI stack, from the AI layer down to applications and infrastructure. New Relic strongly emphasizes open standards, leveraging open-source tools like OpenLIT and OpenLLMetry to capture detailed AI-specific telemetry.
The platform offers a consolidated view of all model responses, helping teams quickly identify outliers and troubleshoot issues like bias, hallucinations, and toxicity. It provides dashboards for tracking key metrics like response time, token usage, and cost, with alerting capabilities to notify teams of anomalies. For teams building agentic workflows, New Relic can monitor the entire Model Context Protocol (MCP) request lifecycle automatically.
Best for: Enterprises that use New Relic for full-stack observability and prefer an approach based on open standards like OpenTelemetry.
Key Features:
- Full-Stack Correlation: Links AI performance metrics directly to application and infrastructure health.
- OpenTelemetry Native: Built around open standards for instrumentation, reducing vendor lock-in.
- Agent & MCP Monitoring: Provides deep visibility into complex, multi-step agentic workflows and tool calls.
- Cost Tracking: Monitors token usage and helps compare different models on cost and performance.
4. Arize AI
Arize AI is an AI-native observability and evaluation platform designed specifically for troubleshooting and monitoring machine learning and LLM applications. Unlike traditional APM vendors, Arize focuses deeply on the evaluation of model quality and the detection of production issues like data drift.
Arize allows teams to monitor, trace, and evaluate their LLM applications from development to production. It excels at analyzing unstructured data like text and embeddings, helping teams understand when and why model performance degrades. The platform offers both a source-available version (Phoenix) for development and a managed commercial platform (Arize AX) for enterprise-scale production workloads.
Best for: AI/ML teams that need a specialized platform for deep model evaluation, drift detection, and production troubleshooting.
Key Features:
- Evaluation-First Approach: A rich set of tools for running online and offline evaluations on model outputs.
- Embedding Analysis: Visualizes and monitors embedding drift to detect subtle changes in data and model behavior over time.
- RAG Troubleshooting: Provides specific tools for diagnosing issues in Retrieval-Augmented Generation pipelines.
- Open Standards: The core platform is built on OpenTelemetry and OpenInference, promoting interoperability.
5. OpenTelemetry (via OpenLLMetry)
While not a standalone platform, OpenLLMetry is a crucial open-source toolkit that extends OpenTelemetry to provide observability for LLM applications. It provides standardized, vendor-neutral instrumentation for capturing AI-specific data like prompt and completion tokens, model parameters, and usage details.
OpenLLMetry works with popular AI frameworks like OpenAI, LangChain, and HuggingFace, and can send its telemetry data to any OpenTelemetry-compatible backend, including Datadog, New Relic, Honeycomb, or self-hosted solutions like Jaeger and Prometheus. This makes it an excellent choice for enterprises that want to build their own observability stack or avoid being locked into a single vendor's ecosystem.
Best for: Teams with strong engineering capabilities who want to build a custom observability solution or ensure their instrumentation is vendor-neutral and portable.
Key Features:
- Vendor-Neutral Standard: Based on the widely adopted OpenTelemetry standard, ensuring portability and preventing lock-in.
- Broad Integrations: Supports a wide range of LLM frameworks, vector databases, and observability backends.
- Automatic Instrumentation: Provides drop-in instrumentation that automatically captures key LLM interactions with minimal code changes.
- Community-Driven: As an open-source project, it benefits from rapid development and a strong community.
Recommendation
For most enterprises, the optimal LLM observability solution is one that combines infrastructure-level control with deep, AI-specific insights.
Bifrost is the top recommendation because it operates at the gateway layer, providing a single, unified control plane for routing, security, governance, and observability. This architecture inherently captures all AI traffic without requiring teams to instrument individual applications, making it easier to enforce standards and gain complete visibility. Its native support for OpenTelemetry and Prometheus ensures that the rich data it collects can be seamlessly integrated into the broader enterprise monitoring ecosystem.
For teams already deeply invested in an existing APM platform, Datadog and New Relic offer compelling solutions that unify LLM monitoring with traditional observability. For teams needing a dedicated, evaluation-centric tool, Arize AI provides best-in-class features for troubleshooting production model quality.
Frequently Asked Questions
What is LLM observability?
LLM observability is the practice of monitoring and analyzing the behavior of large language models in production. It goes beyond traditional metrics like latency and error rates to track AI-specific signals like prompt and response content, token usage, costs, hallucinations, and security vulnerabilities.
How is LLM observability different from traditional monitoring?
Traditional monitoring focuses on whether a system is operational (e.g., uptime, CPU usage, error codes). LLM observability focuses on why a system produces a certain output. It provides the context needed to debug non-deterministic AI behavior, evaluate output quality, and ensure the model is functioning correctly and safely.
How do these platforms help detect hallucinations?
Platforms like Datadog and Arize offer built-in evaluation models that score responses for factuality and groundedness against provided context. Gateway-based systems like Bifrost can integrate with third-party guardrail providers to check for hallucinations before a response is returned to the user, logging the outcome of the check as part of the trace.
Why is OpenTelemetry important for LLM observability?
OpenTelemetry is an open-source, vendor-neutral standard for instrumenting applications to collect telemetry data (traces, metrics, and logs). Using an OpenTelemetry-based tool like OpenLLMetry or a platform with native OTel support like Bifrost prevents vendor lock-in and ensures that your observability data can be sent to any compatible backend system your organization chooses.
Can LLM observability help control costs?
Yes. By tracking token usage for every prompt and completion, these platforms can pinpoint which models, features, or user queries are driving the most cost. This allows teams to optimize inefficient prompts, add caching for common requests, or route queries to cheaper models to manage their spend effectively.
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