AI Gateway

Top Enterprise AI Gateways for LLM Observability in 2026

Top Enterprise AI Gateways for LLM Observability in 2026

Running LLM applications in production without observability is operationally reckless. When costs spike, teams struggle to determine whether traffic increased or an agent entered a recursive loop. When quality drops, it is unclear whether prompts regressed, retrieval failed, or a new model version introduced subtle behavior changes. And when compliance questions arise, many teams realize they lack a complete audit trail of what their AI systems actually did.

As enterprise LLM spending surges past $8.4 billion, AI gateways have evolved from simple routing proxies into full observability infrastructure. The modern AI gateway sits between your application and LLM providers, enforcing security policies, managing costs, ensuring reliability, and providing centralized visibility into every model interaction across your organization.

This guide evaluates the top enterprise AI gateways for LLM observability in 2026, based on tracing depth, production monitoring, governance capabilities, and performance under real-world traffic.

What Makes an AI Gateway Enterprise-Ready for Observability

Not every gateway delivers meaningful observability. Basic request logging is table stakes. Enterprise teams need gateways that provide:

  • Distributed tracing: Span-level visibility into every request path across prompts, retrievals, tool calls, and guardrails, with correlation IDs linking user sessions to individual LLM operations
  • Real-time metrics and alerting: Aggregated performance dashboards covering latency distributions, error rates, token usage, cache hit rates, and cost analytics - with alerts that trigger before regressions reach users
  • Governance and audit trails: Complete records of who used which model, with what data, and when - satisfying compliance requirements for regulated industries
  • Cost attribution: Per-team, per-customer, and per-project spend tracking with enforceable budget limits
  • Quality monitoring: The ability to run automated evaluations on gateway traffic, measuring output quality alongside operational metrics

The gateways below are evaluated against these criteria.

1. Bifrost - Best for Production Observability with Enterprise Governance

Bifrost is a high-performance, open-source AI gateway written in Go that provides a unified OpenAI-compatible API across 12+ providers including OpenAI, Anthropic, AWS Bedrock, Google Vertex, Azure, Mistral, Groq, and Ollama. Built as infrastructure from day one, Bifrost treats observability, governance, and reliability as core primitives rather than add-ons.

Observability capabilities:

  • Native Prometheus metrics: Export gateway-level telemetry directly into existing monitoring stacks with structured logging and distributed tracing across every request
  • Real-time cost and usage analytics: Track token consumption, latency distributions, error rates, and cache hit rates per provider, model, team, and customer in a single view
  • Integrated quality monitoring: Run automated quality checks on gateway traffic using custom evaluators, LLM-as-a-judge metrics, and deterministic rules through native integration with Maxim AI's observability suite
  • Production trace debugging: Distributed tracing with custom dashboards enables teams to identify root causes of quality regressions, latency spikes, and cost anomalies without switching between tools

Enterprise governance:

  • Hierarchical budget management: Virtual keys enable team-level, customer-level, and project-level cost controls with hard limits that prevent budget overruns
  • HashiCorp Vault integration: Secure API key management for enterprise security requirements
  • SSO support: Google and GitHub authentication for team access control
  • Comprehensive audit trails: Every request logged with metadata satisfying compliance requirements for regulated industries

Performance:

  • Under sustained traffic at 5,000 requests per second, Bifrost adds roughly 11 microseconds of gateway overhead. Python-based gateways typically add hundreds of microseconds to milliseconds once concurrency climbs. In agent workflows where a single user action triggers multiple LLM calls, that difference compounds fast.

Additional infrastructure:

What sets Bifrost apart from standalone gateways is the closed-loop feedback between gateway operations and application quality. Gateway cost and performance data flows directly into Maxim AI's evaluation and experimentation workflows. Teams can monitor cost trends alongside quality metrics like accuracy, hallucination rate, and task completion - catching regressions before they affect users. Organizations like Clinc, Thoughtful, and Atomicwork rely on Bifrost for production AI infrastructure.

Best for: Engineering teams building production AI applications that need ultra-low latency routing, deep observability, enterprise governance, and native integration with evaluation and quality monitoring workflows.

2. Cloudflare AI Gateway

Cloudflare AI Gateway is a managed service that sits on Cloudflare's global edge network, providing observability and caching for LLM traffic without requiring teams to deploy or maintain any infrastructure.

Key capabilities:

  • Request-level logging: Track every LLM request with metadata including model, tokens, latency, and cost across supported providers
  • Analytics dashboard: Aggregated views of request volume, token usage, cost, and error rates across providers
  • Caching: Reduce costs and latency through response caching at the edge
  • Rate limiting: Control request throughput per user, team, or application
  • Generous free tier: Low barrier to entry for teams evaluating gateway observability

Limitations: Observability depth is limited compared to gateways with distributed tracing and span-level visibility. The platform lacks built-in quality monitoring, evaluation integration, or automated alerting on quality degradation. Governance features are basic relative to enterprise requirements for budget hierarchies, audit trails, and secure key management.

Best for: Teams that prioritize zero infrastructure overhead and want lightweight LLM observability on Cloudflare's edge network, particularly for serverless and edge deployments.

3. LiteLLM

LiteLLM is a popular open-source proxy that standardizes calls to 100+ LLM providers behind a unified API. It is widely adopted in the developer community and designed to be fully self-hosted.

Key capabilities:

  • Broad provider support: Standardized access to 100+ providers including niche and open-weight models
  • Request logging: Track prompts, completions, token usage, and latency across providers
  • Spend tracking: Per-key and per-team cost attribution with configurable budget limits
  • Callback integrations: Forward logs to external observability platforms like Langfuse, Datadog, and custom webhooks
  • Self-hosted control: Complete data sovereignty with full infrastructure ownership

Limitations: LiteLLM is Python-based, and performance degrades under high concurrency. At scale, latency overhead can reach hundreds of microseconds to milliseconds. Built-in observability is relatively basic; teams typically need to integrate external platforms for production-grade dashboards, alerting, and quality monitoring. Maintenance burden sits entirely with the team operating it.

Best for: Developers and small teams who want maximum provider flexibility and are comfortable self-hosting and maintaining their own observability infrastructure.

4. Kong AI Gateway

Kong AI Gateway extends Kong's proven API gateway platform to support LLM traffic, applying the same governance model, security posture, and plugin ecosystem to AI workloads.

Key capabilities:

  • Unified API and AI governance: Manage traditional API traffic and LLM traffic under the same governance framework
  • Plugin-based observability: Extend monitoring with Kong's ecosystem of logging, analytics, and tracing plugins
  • Token analytics and cost tracking: Monitor usage across providers with quota management
  • Enterprise security: Authentication, authorization, mTLS, API key rotation, and RBAC through Kong Konnect
  • Request/response transformation: Normalize formats across different LLM providers at the proxy layer

Limitations: Kong AI Gateway is powerful but not lightweight. Setup and customization assume familiarity with Kong's ecosystem. The platform is primarily an API management tool extended to AI rather than an AI-native gateway, which means LLM-specific observability features like semantic caching, quality monitoring, and agent-aware tracing are either limited or require additional plugins. Pricing ties to Kong Konnect or Enterprise plans.

Best for: Enterprises already standardized on Kong that want to layer AI governance and observability on top of their existing API infrastructure without adopting a new tool.

5. Vercel AI Gateway

Vercel AI Gateway provides a managed gateway layer for teams building AI-powered applications on the Vercel platform, with transparent pricing and no markup on token usage.

Key capabilities:

  • Request logging and analytics: Track LLM requests with model, token, and cost metadata
  • Bring Your Own Keys (BYOK): Transparent pricing at provider rates with no per-token surcharge from Vercel
  • Caching: Response caching to reduce duplicate API calls and lower costs
  • Edge deployment: Leverage Vercel's edge network for low-latency routing
  • Framework integration: Native support for Next.js and Vercel's AI SDK

Limitations: Observability is scoped to the Vercel ecosystem. Teams not building on Vercel get limited value. The gateway lacks distributed tracing, quality monitoring, evaluation integration, or enterprise governance features like budget hierarchies and secure key management. Primarily optimized for experimentation and frontend AI features rather than production-scale enterprise workloads.

Best for: Frontend-heavy teams shipping AI features on Vercel who want simple LLM observability and caching without managing separate gateway infrastructure.

How to Choose the Right AI Gateway for Observability

Selecting the right gateway depends on where your primary pain point sits:

  • Production observability with cost and quality monitoring: Bifrost provides the most complete solution, combining ultra-low latency routing, hierarchical budget controls, semantic caching, and native integration with evaluation and quality monitoring workflows
  • Zero infrastructure overhead: Cloudflare AI Gateway offers the lowest friction with a generous free tier and no infrastructure to manage
  • Maximum provider flexibility: LiteLLM supports 100+ providers for teams comfortable with self-hosted maintenance
  • Unified API and AI governance: Kong is the natural extension for enterprises already standardized on Kong infrastructure
  • Frontend AI features: Vercel AI Gateway fits teams embedded in the Vercel ecosystem

For most enterprise teams, the critical differentiator is what happens beyond routing. A gateway that only logs requests gives you a dashboard. A gateway that connects observability to evaluation, quality monitoring, and cost governance gives you operational control over your AI systems.

Conclusion

Enterprise AI gateways in 2026 are no longer optional glue code. They are infrastructure - and infrastructure choices tend to stay with you longer than models. The gateway you choose determines how much visibility, control, and governance you have over every LLM call your organization makes.

For teams that need production-grade observability, enterprise governance, and the performance to handle scale without compromise, Bifrost delivers the most comprehensive solution with 11 microsecond latency overhead and native integration into the full AI quality lifecycle.

Ready to see Bifrost in action? Book a demo to learn how Bifrost can give your team complete observability and control over your LLM infrastructure.

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