AI Gateway

Top 5 Enterprise AI Gateways in 2026

Top 5 Enterprise AI Gateways in 2026

The enterprise AI market reached $114.87 billion in 2026, with organizations rapidly transitioning from pilot programs to production deployments. According to Deloitte's State of AI in the Enterprise report, the number of companies with 40% or more AI projects in production is set to double in six months, and 74% of companies plan to deploy agentic AI within two years.

This scale introduces serious infrastructure challenges. Engineering teams now juggle multiple LLM providers (OpenAI, Anthropic, Google Gemini, AWS Bedrock, Mistral) each with different API formats, authentication schemes, rate limits, and pricing models. Without a unified control plane, enterprises face vendor lock-in, unpredictable costs, zero failover coverage, and compliance blind spots across their AI stack.

Enterprise AI gateways have emerged as the critical infrastructure layer that sits between applications and model providers. Gartner's Hype Cycle for Generative AI now classifies AI gateways as essential components for scaling AI responsibly, not optional middleware, but foundational infrastructure on par with API management and service meshes.

This analysis evaluates the five leading enterprise AI gateways in 2026 based on performance benchmarks, governance capabilities, deployment flexibility, and production readiness.

1. Bifrost by Maxim AI

Bifrost is an open-source, high-performance AI gateway built in Go by Maxim AI. It is purpose-built for production workloads where latency, reliability, and governance are non-negotiable, and it leads the category on all three.

Performance:

  • 11 microsecond mean latency overhead at 5,000 RPS, 50x faster than Python-based alternatives, ensuring the gateway layer never becomes a production bottleneck
  • 54x faster p99 latency compared to LiteLLM on identical hardware
  • 9.4x higher throughput under sustained load, critical for applications serving real users at scale

Core infrastructure:

  • **Unified OpenAI-compatible interface:** Single API endpoint routing to 12+ providers including OpenAI, Anthropic, AWS Bedrock, Google Vertex, Azure, Cohere, Mistral, Groq, and Ollama
  • **Automatic failover and load balancing:** Seamless provider failover with zero downtime, distributing requests intelligently across multiple API keys and providers
  • **Semantic caching:** Reduces costs and latency through intelligent response caching based on semantic similarity rather than exact match
  • **MCP support:** Model Context Protocol integration for governing AI agent tool access in agentic workflows
  • **Drop-in replacement:** Replace existing OpenAI or Anthropic API calls with a single line of code

Enterprise governance and security:

What sets Bifrost apart is its integration with Maxim AI's evaluation and observability platform. Teams can run automated quality evaluations directly on production logs, set real-time alerts for governance violations, and use AI-powered simulations to test agents across hundreds of scenarios before deployment. This end-to-end approach (from gateway infrastructure to quality management) enables teams to deploy AI agents 5x faster through systematic quality improvement.

Best for: Engineering and product teams building production AI applications that require the highest performance, comprehensive governance, and integrated quality management across the full AI lifecycle.

See More: Bifrost AI Gateway | Bifrost Governance Docs | Agent Observability

2. Cloudflare AI Gateway

Cloudflare AI Gateway leverages Cloudflare's global edge network of 250+ points of presence to proxy AI traffic with built-in caching, rate limiting, and observability. In 2026, Cloudflare introduced Unified Billing, allowing teams to consolidate third-party model charges into a single Cloudflare invoice.

Key capabilities:

  • Global edge caching: Serve identical requests from Cloudflare's cache, reducing latency by up to 90% and cutting redundant API calls
  • Unified billing: Pay for OpenAI, Anthropic, and Google AI Studio usage directly through Cloudflare with a single invoice
  • Zero Data Retention (ZDR): Route traffic through provider endpoints that do not retain prompts or responses for compliance-sensitive workloads
  • Visual routing configuration: Route requests based on user segments, geography, or content analysis through a visual interface without code changes
  • Content moderation: Real-time guardrails for safe AI application deployment

Considerations: Cloudflare's free tier caps AI Gateway logs at 100,000 per month, and scaling requires a Workers Paid plan. The gateway lacks deep governance controls like hierarchical budget management and fine-grained team-level access policies. For teams requiring comprehensive enterprise governance alongside high performance, Bifrost provides a more complete feature set.

Best for: Teams already invested in Cloudflare's ecosystem that want AI traffic managed alongside their existing edge infrastructure with minimal additional setup.

3. Kong AI Gateway

Kong AI Gateway extends Kong's established API management platform with AI-specific plugins for LLM traffic management. For organizations already running Kong for traditional API governance, this provides a unified control plane across both API and AI workloads.

Key capabilities:

  • Plugin-based architecture: Extend AI governance with custom plugins for rate limiting, PII detection, prompt validation, and content moderation
  • MCP gateway support: Manage and govern Model Context Protocol servers for agentic AI workflows through Kong's control plane
  • Enterprise RBAC and audit logging: Leverage Kong's mature role-based access control and compliance tooling proven at scale
  • Multi-cloud and hybrid deployment: Enforce governance policies consistently across on-premises, cloud, and hybrid environments
  • AI request transformation: Normalize and validate requests across different provider API formats

Considerations: Kong AI Gateway is best suited for enterprises with existing Kong infrastructure. For greenfield AI deployments, the overhead of deploying and managing the full Kong platform may not be justified compared to purpose-built AI gateways like Bifrost that offer zero-configuration startup and AI-native governance features out of the box.

Best for: Large enterprises with existing Kong API management deployments that want to extend their governance framework to AI traffic without introducing a separate tool.

4. LiteLLM

LiteLLM is a Python-based open-source AI gateway that provides a unified OpenAI-format interface across 100+ LLM providers. It remains the most widely adopted open-source gateway for teams that prioritize broad provider coverage and familiar Python tooling.

Key capabilities:

  • 100+ provider support: The broadest provider compatibility in the category, covering OpenAI, Anthropic, AWS Bedrock, Google Vertex, Azure, Cohere, NVIDIA, HuggingFace, and dozens more
  • Virtual key management: Create and manage API keys per team or project with individual spend limits
  • Python SDK and proxy server: Dual-mode usage as both an importable Python library and a standalone proxy server
  • MCP tool integration: Load MCP tools in OpenAI format and use them with any LiteLLM-supported model
  • Traffic mirroring: Replicate production traffic to a secondary model for evaluation without affecting live responses

Considerations: LiteLLM has significant limitations for enterprise production use. There is no formal commercial backing, no enterprise SLAs, and no dedicated support escalation path. Users report frequent regressions between versions, edge-case instability, and performance degradation under sustained load. The Python runtime adds measurable latency overhead that becomes a bottleneck for real-time applications, benchmarks show 50x higher overhead compared to Bifrost's Go-based architecture.

Best for: Engineering teams with strong internal DevOps capabilities that need broad provider coverage for prototyping and early-stage development, and can manage the operational complexity of an unsupported open-source platform.

5. Azure API Management AI Gateway

Azure API Management has expanded with a Unified AI Gateway pattern that provides centralized governance for organizations heavily invested in the Microsoft ecosystem. This pattern, developed in production by Uniper, optimizes AI governance and operational efficiency through Azure's native policy engine.

Key capabilities:

  • Unified authentication enforcement: Consistent API key and JWT validation for every request, with managed identity for backend authentication to Azure-hosted AI services
  • Model-aware dynamic routing: Automatically route requests based on model capacity, cost, performance, and operational factors without code changes
  • Centralized audit and traceability: All AI requests and responses are logged centrally for unified auditing and compliance reporting
  • Policy-driven configuration: YAML-based policy definitions for rate limiting, content filtering, and access control that apply across all AI endpoints
  • Wildcard API definitions: A single API definition handles all AI providers, eliminating the need for configuration changes when adding new models or providers

Considerations: Azure API Management's AI capabilities are tightly coupled to the Azure ecosystem. Organizations using multi-cloud or non-Microsoft infrastructure will find the integration overhead significant. The platform also lacks AI-native features like semantic caching and purpose-built budget management that Bifrost delivers natively.

Best for: Enterprises already running on Azure that need to govern AI model access alongside their existing API management infrastructure within Microsoft's ecosystem.

How to Evaluate an Enterprise AI Gateway

Selecting the right AI gateway depends on your organization's specific requirements. Here are the critical dimensions to assess:

  • Latency overhead: For real-time AI applications, every millisecond matters. Bifrost's 11µs overhead is orders of magnitude lower than Python-based alternatives, ensuring governance controls do not degrade user experience.
  • Cost governance depth: Hierarchical budget management at team, project, and customer levels is essential for multi-team organizations. Without it, a single runaway workflow can consume an entire quarter's AI budget overnight.
  • Compliance and audit readiness: The EU AI Act's high-risk system rules take full effect in August 2026, requiring comprehensive logging, traceability, and policy enforcement at the infrastructure layer.
  • Agentic AI support: With the Cloud Security Alliance projecting 40% of enterprise applications will embed autonomous AI agents by end of 2026, gateways must support MCP governance, multi-step workflow controls, and agent-level observability.
  • Integration with quality management: Governance does not end at access control. The ability to run automated quality evaluations on production data and continuously measure AI reliability is critical for sustained governance.

For organizations building production AI applications that require both infrastructure-level governance and continuous quality monitoring, Bifrost paired with Maxim AI's end-to-end evaluation and observability platform provides the most comprehensive stack available in 2026.

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