Top 5 AI Gateways with Built-In Observability for AI Traffic

An AI gateway with built-in observability is a unified entry point that routes traffic to multiple LLM providers and captures structured telemetry, per-request logs, and metrics for every call without separate instrumentation code. As production AI applications spread across OpenAI, Anthropic, AWS Bedrock, and self-hosted models, teams lose visibility into which provider served a request, what it cost, and why a call failed. Bifrost, the open-source AI gateway built in Go by Maxim AI, is the best overall choice for enterprise teams that need this visibility at scale, with native OpenTelemetry tracing, Prometheus metrics, and per-request logging in the data path. This post compares the top five AI gateways with observability for LLM traffic and explains what to evaluate when monitoring multi-provider AI in production.

Why Built-In Observability Matters for LLM Traffic

Observability for LLM traffic means capturing the inputs, outputs, token counts, cost, latency, and routing decisions of every model call in a structured, queryable form. When observability lives inside the AI gateway rather than bolted on afterward, every request is traced at the point where routing, caching, and failover decisions are made, so the telemetry reflects what actually happened.

Standalone instrumentation is fragile across many providers. Each LLM API returns different token fields, error shapes, and latency profiles, and wiring a tracer into every SDK call is repetitive and easy to break. The OpenTelemetry GenAI semantic conventions, maintained under the Cloud Native Computing Foundation, define a standard schema for AI telemetry so traces stay consistent across providers. A gateway that emits this schema natively gives platform teams one consistent view of all LLM traffic.

Built-in observability addresses several operational needs at once:

• Cost attribution: Track spend per model, provider, team, and virtual key in real time.
• Latency analysis: Measure time to first token, inter-token latency, and provider response time.
• Failure debugging: Inspect which keys were tried, why a call failed, and which fallback served it.
• Compliance and audit: Retain immutable records of prompts, completions, and access for regulated workloads.

Key Criteria for Evaluating AI Gateways with Observability

Use a consistent framework to evaluate AI gateways with observability for LLM traffic. The criteria below separate gateways that emit production-grade telemetry from those that surface only basic dashboards.

• Native telemetry standards: Does the gateway export OpenTelemetry (OTLP) traces and Prometheus metrics without external plugins?
• Per-request logging: Are full request and response payloads captured asynchronously, with zero added latency?
• Backend compatibility: Can traces and metrics flow into Grafana, Datadog, New Relic, and Honeycomb?
• Routing visibility: Does the telemetry record fallback transitions, retries, and which key served each request?
• Deployment control: Can the gateway and its log store run in a private VPC or on-premises for data residency?
• Performance overhead: How much latency does the observability layer add under sustained load?

For a deeper capability matrix across these dimensions, the LLM Gateway Buyer's Guide maps each criterion to concrete gateway features.

1. Bifrost

Bifrost is the open-source AI gateway built by Maxim AI to route, govern, and observe all LLM traffic through a single OpenAI-compatible API. It unifies access to 1000+ models and treats observability as a first-class part of the data path rather than an add-on. Every request flowing through Bifrost is captured with full metadata, and the gateway emits OpenTelemetry traces and Prometheus metrics natively.

Bifrost includes built-in observability that automatically records inputs, outputs, token counts, cost, latency, and status for every call. The logging plugin runs asynchronously in background goroutines, so capturing this data adds no latency to the request. For distributed tracing, the OpenTelemetry integration sends LLM traces to any OTLP collector using the GenAI semantic conventions, connecting to Grafana Cloud, New Relic, Honeycomb, or self-hosted collectors. Native Prometheus metrics are exposed at a /metrics endpoint and through a Push Gateway for multi-node clusters, covering token usage, cost in USD, upstream latency, cache hits, and per-key health.

The metrics carry rich labels for provider, model, virtual key, team, customer, and routing engine, so platform teams can attribute every dollar and millisecond. Bifrost records the full attempt trail on each request, showing which keys were tried and why a call rotated to a fallback, which makes provider-failure debugging direct. For enterprise stacks, the Datadog connector sends APM traces, LLM Observability spans, and metrics through native Datadog SDKs, and log exports stream large payloads to S3 or GCS while keeping searchable metadata in the database. Bifrost adds only 11 microseconds of overhead per request at 5,000 requests per second in sustained benchmarks, so the observability layer does not become a bottleneck.

Because the Bifrost AI gateway and its log store can run inside a private VPC or on-premises, regulated teams keep prompts and completions within their own boundary. Immutable audit logs support SOC 2, GDPR, HIPAA, and ISO 27001 requirements, and the governance layer ties observability to virtual keys, budgets, and access control. Teams evaluating gateways can compare these capabilities in the LLM gateway buyer's guide.

Best for: Bifrost is built for enterprises running mission-critical AI workloads that require best-in-class performance, scalability, and reliability. It serves as a centralized AI gateway to route, govern, and secure all AI traffic across models and environments with ultra low latency. Bifrost unifies LLM gateway, MCP gateway, and Agents gateway capabilities into a single platform. Designed for regulated industries and strict enterprise requirements, it supports air-gapped deployments, VPC isolation, and on-prem infrastructure. It provides full control over data, access, and execution, along with robust security, policy enforcement, and governance capabilities.

2. Kong AI Gateway

Kong AI Gateway extends the Kong API gateway with AI-specific routing and policy plugins for teams already standardized on Kong for API management. It proxies requests to multiple LLM providers and applies rate limiting, key management, and request transformation through its plugin model.

For observability, Kong AI Gateway tracks token usage, latency, and cost through metrics exporters and audit logs, and it can instrument request flows with OpenTelemetry to trace prompts and responses across infrastructure. Pre-built dashboards are available through Konnect Advanced Analytics, or teams can export metrics to an existing observability stack. The trade-off is that production-grade LLM telemetry often depends on the broader Kong and Konnect ecosystem rather than arriving fully assembled in a single open-source binary.

Best for: Organizations already running Kong for API management that want to add LLM routing and monitoring within the same plugin-based platform.

3. Cloudflare AI Gateway

Cloudflare AI Gateway is a managed service that sits in front of LLM provider APIs at the network edge, requiring no infrastructure to operate. It offers a generous free tier and is well suited to teams that prefer a hosted gateway over a self-managed one.

Cloudflare AI Gateway provides request-level logging that records every LLM request with metadata including model, tokens, latency, and cost across supported providers, plus an analytics dashboard with aggregated views of request volume, token usage, cost, and error rates. Because it is a managed edge service, observability data is stored and surfaced within Cloudflare's platform, which suits teams comfortable with a hosted control plane but offers less control for organizations with strict data-residency or in-VPC requirements.

Best for: Teams that want a zero-maintenance hosted gateway with built-in request logging and analytics, and that do not need to keep telemetry inside their own infrastructure.

4. LiteLLM

LiteLLM is an open-source proxy and SDK that provides a unified interface to 100+ LLM providers. It is widely used by teams that want a lightweight, self-hosted layer to standardize calls across many model APIs.

LiteLLM offers basic built-in logging and supports forwarding telemetry to external observability platforms through callbacks and OpenTelemetry. In practice, teams running LiteLLM in production typically integrate a separate observability platform for dashboards, alerting, and quality monitoring, because the native logging is relatively basic. Teams comparing a unified proxy against a gateway with observability built into the data path can review Bifrost as a drop-in LiteLLM alternative with a full feature comparison.

Best for: Developers who want a lightweight open-source proxy across many providers and are willing to add an external observability stack for production monitoring.

5. AWS Bedrock

AWS Bedrock is a managed service for accessing foundation models from providers such as Anthropic, Meta, Cohere, and Amazon within the AWS ecosystem. For teams fully committed to AWS, it offers a single API to several model families with IAM-based access control.

Observability for Bedrock traffic flows through AWS-native tooling: Amazon CloudWatch captures invocation metrics and logs, and model invocation logging records request and response data to S3 or CloudWatch. This gives strong visibility for Bedrock-hosted models, though the telemetry is scoped to the AWS environment and does not unify traffic to providers outside Bedrock under one gateway view.

Best for: Teams operating entirely within AWS that want managed access to foundation models with observability through CloudWatch and native AWS logging.

How Bifrost Compares on Observability for LLM Traffic

Bifrost differs from the other gateways by treating observability as part of the routing data path rather than a separate dashboard or an external integration teams must assemble. Three properties set it apart for monitoring LLM traffic at scale.

• Native multi-standard telemetry: Bifrost emits OpenTelemetry traces, Prometheus metrics, and a native Datadog feed without bolting on external plugins, so existing backends receive consistent data.
• Routing-aware logging: The per-request log and metric labels record the full attempt trail, fallback index, and the key that served each call, which makes provider-failure analysis direct.
• Deployment control with zero overhead: The gateway and its log store run in-VPC or on-premises, and the asynchronous logging plugin adds no request latency.

Major observability vendors have aligned on the OpenTelemetry GenAI semantic conventions; Datadog began natively supporting them in December 2025, which makes a gateway that already emits this schema straightforward to connect. For teams standardizing telemetry across many providers, the Prometheus project and OTLP collectors give a vendor-neutral path that Bifrost supports by default.

Observability and Governance Are One System

Observability is most useful when it connects to control. In Bifrost, the same virtual keys that enforce budgets and rate limits also label every metric and log entry, so spend tracking and policy enforcement share one data model. A request that exceeds a budget, hits a guardrail, or rotates across keys is visible in the same trace that records its cost and latency.

This matters most for enterprises in regulated industries, where in-VPC deployment keeps telemetry inside the organization's boundary and immutable audit logs satisfy compliance review. The combination of full request tracing, native metrics, and policy-aware labels is why the governance resource page treats observability and governance as parts of one system rather than separate tools.

Choosing an AI Gateway with Built-In Observability

The right AI gateway with built-in observability depends on how many providers a team runs, where telemetry must reside, and how much routing detail the logs need to carry. Managed edge services suit teams that want zero maintenance, AWS-native tooling fits teams committed to one cloud, and lightweight proxies fit teams willing to add a separate monitoring stack. For enterprises running mission-critical, multi-provider AI traffic that demands native OpenTelemetry and Prometheus telemetry, routing-aware logging, and in-VPC deployment, the open-source Bifrost gateway provides the most complete picture of LLM traffic without adding latency.

To see how Bifrost gives your team full observability across all LLM traffic, book a demo with the Bifrost team.