TL;DR
- LLM observability platforms track the performance, cost, and output quality of AI applications, moving beyond traditional APM to analyze prompts, responses, and token usage.
- The market has split between full-stack platforms (Datadog, New Relic), AI-native open-source tools (Langfuse), and AI gateways like Bifrost that provide observability at the infrastructure layer.
- Bifrost acts as a centralized AI gateway, capturing detailed traces, metrics, and logs for all model traffic with built-in OpenTelemetry support, offering a single point of instrumentation.
- OpenTelemetry is emerging as the standard for vendor-neutral instrumentation, allowing teams to pipe LLM trace data into multiple backends without being locked into a single vendor.
- Key evaluation criteria for LLM observability tools include trace depth, OpenTelemetry support, cost tracking accuracy, evaluation features, and deployment model (SaaS vs. self-hosted).
Traditional application performance monitoring (APM) tools are not built for the unique challenges of AI. A request to a large language model can succeed, return in milliseconds, and still produce a factually incorrect, unsafe, or costly response. LLM observability is the practice of capturing the data needed to understand why an AI application behaved a certain way, tracking not just latency and errors, but also the semantic quality of inputs and outputs. This includes tracing prompts, responses, tool calls, token usage, and costs across complex, multi-step agent workflows.
As enterprises deploy more AI agents into production, the need for dedicated observability has become critical. This guide compares the leading LLM observability tools in 2026, from full-stack APM platforms to specialized AI-native solutions. For teams seeking to centralize control and observability at the infrastructure layer, an AI gateway like Bifrost offers a compelling approach. As an open-source AI gateway, it instruments every request flowing to any provider, providing a single, unified source of observability data.
Key Criteria for Evaluating LLM Observability Tools
Before comparing platforms, it's useful to establish a framework. The right tool depends on your existing infrastructure, team structure, and specific AI workloads.
| Capability | Description | Why It Matters |
|---|---|---|
| Trace Depth & Context | The ability to capture the full lifecycle of a request, including prompts, model parameters, retrieved context, tool calls, and the final response. | Shallow traces show what happened; deep traces explain why. Debugging complex agent behavior requires seeing every step in the reasoning chain. |
| OpenTelemetry (OTel) Support | Native support for ingesting and exporting data using OpenTelemetry's standards and semantic conventions for generative AI. | OTel provides vendor-neutral instrumentation. Teams can avoid vendor lock-in and route LLM telemetry to multiple backends (e.g., a central APM and a specialized eval platform) from a single instrumentation point. |
| Cost & Token Tracking | Accurate, real-time tracking of token consumption and associated costs, attributable to specific users, projects, or features. | LLM costs can be unpredictable. Granular cost tracking is essential for managing budgets, optimizing expensive workflows, and preventing unexpected overruns. |
| Evaluation & Quality Monitoring | Features for scoring model outputs against defined criteria (e.g., faithfulness, relevance, safety) and monitoring for quality regressions. | Traditional metrics don't capture semantic failures. Evaluation frameworks are needed to systematically measure and alert on drops in output quality. |
| Deployment Model | Whether the tool is offered as a fully managed SaaS, a self-hostable open-source project, or a hybrid model. | Self-hosting offers maximum data control and residency, often preferred in regulated industries. SaaS offers faster setup and lower operational overhead. |
| Integration with Infrastructure | How well the tool integrates with existing monitoring stacks (APM, logging, SIEM) and the broader AI ecosystem (vector databases, frameworks). | LLM observability data is most powerful when correlated with application and infrastructure health signals. A siloed tool creates blind spots. |
The Top LLM Observability Tools of 2026 at a Glance
This table provides a high-level comparison of the platforms covered in this guide. Bifrost is included as a distinct category—an AI gateway that provides the foundational observability data that feeds other systems.
| Tool | Best For | Primary Category | OpenTelemetry Support |
|---|---|---|---|
| Bifrost | Centralized observability and governance at the gateway layer. | AI Gateway | Native Export |
| Datadog | Enterprises standardized on Datadog for full-stack monitoring. | Full-Stack Observability | Ingest & Export |
| Langfuse | Teams wanting a self-hostable, open-source platform for tracing and evals. | AI-Native Tracing & Evals | Native Ingest & Export |
| New Relic | Organizations extending their existing New Relic APM investment to AI. | Full-Stack Observability | Ingest & Export |
| LiteLLM | Teams needing a flexible proxy to route telemetry to existing backends. | AI Gateway / Proxy | Native Export |
1. Bifrost: Observability at the Gateway
Bifrost is a high-performance, open-source AI gateway that provides observability as a core, built-in function. By routing all LLM traffic through a central point, Bifrost captures comprehensive data for every request and response automatically, without requiring application-level instrumentation for each new model or provider.
This gateway-centric approach treats observability as an infrastructure concern. The gateway is the natural choke-point where every LLM call passes, making it the ideal layer for consistent, comprehensive data capture.
Key Observability Features
- Automatic Request Tracing: Bifrost logs every detail of an LLM interaction, including inputs, outputs, model parameters, provider details, token counts, cost, and latency. This logging operates asynchronously, adding near-zero overhead to request processing.
- Native OpenTelemetry Export: Bifrost can export all trace data via OTLP (OpenTelemetry Protocol), allowing seamless integration with existing observability backends like Datadog, Honeycomb, Grafana, and New Relic. This gives teams the flexibility to use Bifrost's built-in logging while also feeding data into their primary APM.
- Built-in Metrics and Dashboards: The gateway ships with a native Prometheus metrics endpoint and a web UI for real-time monitoring of traffic, costs, and performance patterns. This provides immediate insights without requiring an external setup.
- Unified Data Layer: Because Bifrost unifies access to over 20 providers, it produces a standardized observability format across OpenAI, Anthropic, AWS Bedrock, Google Vertex AI, and others. Teams get a consistent view of their entire AI stack, regardless of the underlying models.
- Enterprise-Grade Auditing & Guardrails: For compliance-sensitive workloads, Bifrost Enterprise adds immutable audit logs and logs the outcomes of guardrail interventions, providing a complete record of policy enforcement.
Best for: Engineering teams that want to centralize AI observability, governance, and routing at the infrastructure layer. It's an ideal fit for organizations using multiple LLM providers who need a single, consistent source of telemetry without instrumenting each application individually.
2. Datadog LLM Observability
For enterprises already invested in the Datadog ecosystem, Datadog LLM Observability is a natural extension. It integrates LLM and agent tracing directly into the platform's broader suite for APM, infrastructure monitoring, and security.
The key value proposition is correlation. Teams can connect an LLM performance issue directly to an underlying infrastructure problem, a code deployment, or a security signal within a single interface.
Key Features
- End-to-End Tracing: Instruments applications to capture the full trace of LLM chains and agent executions.
- Quality & Safety Monitoring: Provides out-of-the-box checks for metrics like toxicity and topic relevancy, and allows for custom evaluations based on user feedback.
- Cost & Usage Tracking: Monitors token usage and estimates costs, helping teams track spending.
- AI Agents Console: Visualizes the structure and decision paths of multi-agent systems.
Best for: Organizations already standardized on Datadog for their primary observability needs. The barrier to entry is low, and the ability to correlate AI behavior with other system telemetry is a significant advantage.
3. Langfuse
Langfuse has emerged as a leading open-source LLM engineering platform, combining observability with prompt management and evaluation tools. Its open-source nature and self-hosting capabilities make it a popular choice for teams that require full data control.
Langfuse is built on OpenTelemetry, reinforcing its commitment to interoperability and avoiding vendor lock-in.
Key Features
- Detailed Tracing: Captures comprehensive traces of LLM calls, including support for complex agent graphs and multi-turn user sessions.
- Integrated Evaluation: Allows teams to measure output quality by running evaluations on production traces, using methods from LLM-as-a-judge to human feedback.
- Prompt Management: Provides version control for prompts, allowing teams to collaborate and link specific prompt versions to production traces.
- Self-Hosting: The entire platform is open-source (MIT licensed) and can be self-hosted, providing maximum data security and control.
Best for: Startups and enterprises looking for a powerful, open-source, and self-hostable platform that tightly integrates tracing with evaluation and prompt management. It's a strong choice for teams building complex agents and wanting deep, collaborative debugging workflows.
4. New Relic AI Monitoring
Similar to Datadog, New Relic has extended its established APM platform to include AI and LLM observability. The primary advantage is providing a unified view for teams already using New Relic for application and infrastructure monitoring.
New Relic's solution captures AI-specific metrics like token usage and response time and displays them alongside standard APM signals.
Key Features
- Full-Stack Visibility: Integrates AI performance data directly into APM, providing a single view of the entire application stack.
- Response Tracing: Offers a detailed view of the entire AI request journey, from user input to final response, to simplify debugging.
- Agent & Tool Monitoring: Automatically detects and instruments popular agentic frameworks, visualizing how agents, tools, and services interact.
- Model Comparison: Allows teams to compare the cost and performance of different models across different application environments.
Best for: Engineering teams heavily invested in the New Relic platform who want to add LLM observability without introducing a new vendor.
5. LiteLLM
LiteLLM is an open-source library that provides a unified interface to call over 100 LLM providers. While its primary function is abstraction, it also serves as a lightweight gateway with powerful observability features.
Instead of being a bundled observability product, LiteLLM acts as a flexible telemetry router. It uses a callback system to send detailed data about each LLM call to a wide range of external backends, including Langfuse, Datadog, Grafana, and any OpenTelemetry-compatible system.
Key Features
- Callback-Based Telemetry: Provides hooks that run on request success or failure, sending rich data to specified destinations.
- Broad Integration Support: Offers native integrations with dozens of observability and MLOps platforms.
- OpenTelemetry Export: Includes a first-class OpenTelemetry callback, making it easy to standardize data export.
Best for: Teams that need a simple, open-source proxy to standardize model access and want the flexibility to route observability data to one or more backends they already use.
Recommendation: Choosing Your Approach
The right LLM observability tool depends on where you want to instrument your AI stack.
- For centralized control, an AI gateway like Bifrost provides the most efficient solution. It captures complete, standardized data for all traffic at the infrastructure layer, which can then be fed into any backend. This approach is powerful because it combines observability with essential governance features like routing, fallbacks, and access control. Beyond the gateway, Bifrost's governance and security can be extended to the endpoint with Bifrost Edge, which routes AI traffic from desktop apps and coding agents through the same governed infrastructure.
- For existing platform users, the LLM modules from Datadog or New Relic offer the lowest friction path, embedding AI monitoring within a familiar full-stack context.
- For an open-source, AI-native workflow, Langfuse provides a deeply integrated suite for tracing, evaluation, and prompt engineering, with the added benefit of self-hosting for data-sensitive applications.
Ultimately, LLM observability is not just about logging data; it's about gaining actionable insights to improve the cost, performance, and quality of production AI systems. The best approach is one that integrates seamlessly into your existing engineering workflows and provides the context needed to move from detecting a problem to resolving it. Teams evaluating these tools can request a demo of Bifrost or explore the open-source repository to see how gateway-level observability works.
Frequently Asked Questions
What is the difference between LLM observability and traditional APM?
Traditional APM tracks infrastructure and application metrics like latency, error rates, and resource utilization. LLM observability goes deeper, analyzing the content and structure of AI requests, including prompt details, response quality, token usage, and multi-step agent behavior to understand semantic, not just operational, failures.
Why is OpenTelemetry important for LLM observability?
OpenTelemetry provides a vendor-neutral standard for instrumenting, generating, and exporting telemetry data. For LLM applications, it means you can instrument your code once and send detailed traces to any compatible backend, avoiding vendor lock-in and allowing you to use multiple specialized tools (e.g., an APM for infrastructure and an eval platform for quality).
How do I measure the "quality" of an LLM response?
Quality is measured by running evaluations that score an LLM's output against a set of criteria. These can include LLM-as-a-judge (using another powerful model to score the response), programmatic checks (e.g., code evaluators), and human feedback. Common criteria include factual accuracy, relevance, tone, safety, and lack of hallucinations.
Can an AI gateway replace a dedicated observability tool?
An AI gateway like Bifrost can serve as the primary source of observability data, providing comprehensive logs, traces, and metrics for all AI traffic. It offers built-in dashboards for real-time monitoring. For deep, specialized analysis or correlation with other enterprise systems, it's designed to export its data via OpenTelemetry into dedicated platforms like Datadog, Grafana, or New Relic.
What are the main cost drivers in LLM applications?
The primary cost driver is token usage—both input tokens (prompts) and output tokens (responses). Costs can escalate quickly due to inefficient prompts, verbose models, unnecessary tool calls in agent workflows, or a lack of caching for repeated queries. Effective observability tools must provide granular, real-time cost tracking to manage this.
How does LLM observability help with security?
Observability tools can help detect security threats like prompt injection, data leakage, and misuse by monitoring inputs and outputs for malicious patterns or sensitive data. By logging every request, they also provide a crucial audit trail for security investigations and compliance.
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