Top 5 Cloudflare AI Gateway Alternatives for Enterprises in 2026

TL;DR

• Cloudflare AI Gateway is a hosted edge proxy, so it cannot be deployed inside a customer VPC or an air-gapped environment, which rules it out for many regulated workloads.
• Bifrost is the strongest enterprise alternative, adding 11 microseconds of overhead per request at 5,000 requests per second while running self-hosted, in-VPC, or on-premises.
• Cloudflare AI Gateway's core features are free, but persistent logs are capped at 10,000,000 per gateway on Workers Paid and guardrails are billed as token-based inference on top of provider costs.
• Enterprise selection turns on four properties: where the gateway runs, whether budgets are enforced per identity, whether audit records are signed and exportable, and whether agentic tool traffic is governed.
• The five alternatives here differ mainly in deployment control and how much governance ships in the open-source core rather than behind a commercial tier.

Enterprises evaluate Cloudflare AI Gateway alternatives when data residency, audit, or deployment requirements collide with a hosted edge service. Cloudflare AI Gateway adds caching, rate limiting, request logging, and analytics to AI traffic with a single configuration change, and it does that well for teams already building on Workers. It cannot be run inside a customer VPC, which is a hard constraint rather than a preference in regulated industries. Bifrost, the open-source AI gateway written in Go by Maxim AI, is the best overall choice for enterprises running mission-critical AI workloads that require best-in-class performance, scalability, and reliability. This comparison covers five alternatives against enterprise deployment, governance, and compliance requirements.

Why Enterprises Look Beyond Cloudflare AI Gateway

Enterprises move off Cloudflare AI Gateway for reasons that have little to do with the product's quality and much to do with where it runs and what it enforces. The service is a hosted proxy on Cloudflare's network, which fixes both the data path and the governance model.

Four constraints drive most evaluations:

• No self-hosted or in-VPC option. Every prompt and response transits Cloudflare infrastructure, which conflicts with data residency rules and with security reviews that require the inference path to stay inside a controlled boundary.
• Enforcement not bound to a provisioned identity. Spend limits can be scoped by model, provider, and custom metadata, and usage insights can attribute traffic to a user string. Enterprise governance needs budgets and model allow lists bound to a provisioned identity or identity-provider group, so access changes when someone joins or leaves.
• Logs rather than audit evidence. Request logs support debugging. Compliance frameworks ask for tamper-evident records with defined retention and export, which is a different artifact.
• No MCP or tool governance. Agentic traffic is the fastest-growing AI category and the least governed, and a model-only proxy does not see which tools an agent can reach.

IBM's 2025 Cost of a Data Breach Report found that 97% of organizations reporting an AI-related breach had no AI access controls in place, which is the gap these constraints leave open. A broader survey of the category is available in the roundup of Cloudflare AI Gateway alternatives for full control of AI traffic.

Enterprise Requirements for an AI Gateway

An enterprise AI gateway is evaluated on where it can be deployed, what it enforces per identity, and what evidence it produces, because those three properties determine whether it clears a security review. Model catalog size rarely decides the outcome.

The identity federation and audit rows are the ones most often discovered late. A gateway that authenticates with static API keys and writes unsigned application logs will pass a proof of concept and fail a customer security questionnaire. Practical implementation guidance is in the guide to access profiles, RBAC, and data access control.

Cloudflare AI Gateway Pricing and Deployment Constraints

Cloudflare AI Gateway's core features are free on all plans, and the costs that matter at enterprise scale appear in logging, guardrails, and export rather than in the gateway itself. Understanding the shape of that bill is part of the comparison.

• Core features are free. Dashboard analytics, caching, and rate limiting are available on all plans with a Cloudflare account.
• Persistent logs are capped. Workers Free retains 100,000 logs across all gateways; Workers Paid retains 10,000,000 logs per gateway, with older entries deleted automatically at the limit.
• Guardrails are billed as inference. Guardrail evaluation is charged as Workers AI token-based inference, so cost scales with prompt and response length on top of provider costs.
• DLP scanning is free. Prompt and response data loss prevention scanning is available on all plans, with predefined profiles on free accounts and the full profile set for Zero Trust subscribers.
• Log export is a paid add-on. Logpush is available on Workers Paid with additional per-million-request charges beyond the included volume.
• Unified billing carries a fee. Purchasing provider credits through the service adds a percentage fee, though provider inference pricing itself is unmarked-up.

The deployment constraint is structural rather than commercial. A hosted edge gateway cannot be placed in a customer VPC, so organizations with data residency obligations, air-gap requirements, or contractual limits on subprocessors have to solve for that separately regardless of price. A self-hosted gateway inverts the cost model: log volume and guardrail evaluation are bounded by the infrastructure the organization already runs, and retention policy is a configuration value rather than a plan tier.

AI Gateway Comparison for Enterprise Deployments

The five alternatives split cleanly by deployment model and by how much governance ships in the open-source core. The table below maps each against the enterprise requirements above.

Two of these five govern only within a specific platform boundary, which is a good fit if the organization has standardized there and a poor one otherwise. The remaining three are general-purpose and differ mainly in how much of the enterprise control set requires a commercial contract. Bifrost keeps identity-bound virtual keys, budgets, rate limits, routing, and MCP tool filtering in the open-source core, with clustering, RBAC, guardrails, and signed audit logs in the enterprise tier. That line is worth checking for every option on this list.

Mapping these requirements onto a recognized control framework makes the evaluation defensible. The NIST AI Risk Management Framework is the most commonly referenced starting point, and it expects an inventory of AI systems plus evidence that controls operate, both of which are gateway outputs rather than documents.

1. Bifrost

Bifrost is a high-performance AI gateway that unifies access to 20+ providers through a single OpenAI-compatible API, with governance, guardrails, clustering, and observability in one deployment. It starts with zero configuration and adds 11 microseconds of overhead per request at 5,000 requests per second on a t3.xlarge instance, at a 100% success rate under sustained load, as published in the Bifrost benchmarks.

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.

Deployment control. In-VPC deployment is supported across AWS, GCP, and Azure with network isolation, data sovereignty, and no external network dependency, and on-premises and air-gapped deployment is documented separately. Clustering provides high availability with automatic service discovery and zero-downtime deployments.

Governance. Virtual keys carry model and provider filtering plus hierarchical budgets at the key, team, and customer levels, with token and request rate limits applied at the virtual key and provider-config levels. Access profiles make those settings reusable so key allocation scales with headcount rather than with tickets.

Compliance. Audit logs are HMAC-signed, filterable, retained for a configurable period, exportable as JSON, JSON Lines, or Syslog, and archivable to S3 or GCS for long-term retention.

Role-based access control and OIDC user provisioning map roles from Okta, Microsoft Entra, Keycloak, or Google Workspace, and data access control governs who can view payloads in logs.

Security. Guardrails run on prompts and responses using reusable profiles, with Gitleaks-backed secrets detection, a custom regex engine including a PII template, and integrations with AWS Bedrock Guardrails, Azure Content Safety, Google Model Armor, CrowdStrike AIDR, Gray Swan Cygnal, Lakera Guard, and Patronus AI. Native regex and secrets-detection guardrails run in-process rather than as a separately metered inference call.

Agentic traffic. Bifrost operates as both an MCP client and an MCP server, so tool access is governed alongside model access through per-key tool filtering and MCP tool groups. Further detail is in the MCP gateway resource page.

Endpoint coverage. Bifrost Edge, currently in alpha, extends the same gateway policies to desktop apps, browser AI, coding agents, and their MCP servers on employee machines, so governance reaches the AI that was never configured to route through the gateway.

2. LiteLLM

LiteLLM is an open-source Python proxy and SDK offering a unified interface across a large provider catalog, with virtual key management, per-key budgets, and self-hosted deployment. It is a common first step for teams that need multi-provider access without a commercial contract.

Best for: Python-centric engineering teams that want an open-source, self-hosted proxy with key-level budgets and are prepared to operate it themselves.

Strengths. Wide provider coverage, active community development, familiar operational model for Python infrastructure, and budget controls that go beyond reporting.

Trade-offs. The Python runtime constrains throughput and tail latency under sustained load compared with a compiled gateway, and signed, audit-grade logging is thinner than in a purpose-built enterprise gateway. A direct feature-by-feature view is available in the Bifrost LiteLLM alternatives comparison and in the analysis of LiteLLM alternatives for production AI workloads.

3. Kong AI Gateway

Kong AI Gateway layers AI-specific plugins onto the Kong API gateway, covering multi-provider routing, prompt templating, semantic caching, and traffic control. It is a natural choice where Kong already fronts the organization's conventional API traffic.

Best for: Platform teams with an existing Kong deployment that want AI traffic managed through the same proxy tier, plugin model, and operational runbooks as their other services.

Strengths. Mature proxy engineering, a well-understood plugin architecture, strong conventional gateway features, and consistency with existing tooling and on-call practice.

Trade-offs. AI-specific governance depth is thinner than in a purpose-built AI gateway, and much of the enterprise control set sits behind a commercial tier. Organizations without an existing Kong footprint adopt significant operational surface area to reach AI-specific outcomes. See the Kong AI Gateway alternatives roundup for a fuller treatment.

4. Envoy AI Gateway

Envoy AI Gateway is an open-source project built on the CNCF Envoy Gateway, adding LLM-aware routing, token-based rate limiting, and provider credential handling to the Envoy data plane. It reached a 1.0 release and targets Kubernetes-native enterprise traffic engineering.

Best for: Kubernetes platform teams already standardized on Envoy that want AI routing governed by the same data plane and Gateway API resources as the rest of their service mesh.

Strengths. Proven Envoy data plane, Kubernetes-native configuration through Gateway API, token-aware rate limiting rather than request counting, and a genuinely open governance model under CNCF.

Trade-offs. Configuration is Kubernetes-first, so it is a heavier lift for teams not already running a mesh. Application-layer governance primitives such as per-consumer budgets, signed audit logs, and MCP tool policy are thinner than in a purpose-built AI gateway. A comparison focused on routing is available in the roundup of Envoy AI Gateway alternatives for LLM routing.

5. Databricks Mosaic AI Gateway

Databricks Mosaic AI Gateway is the governance layer for model serving inside the Databricks platform, providing rate limits, payload logging, usage tracking, and built-in safety filters for endpoints running there. It is tightly integrated with Unity Catalog and Databricks identity.

Best for: Organizations that have standardized on Databricks and want AI governance to inherit the same workspace identity, lineage, and catalog controls as their data estate.

Strengths. Strong integration with Databricks governance, lineage tracking that extends to model usage, managed operations, and consistency with existing data platform controls.

Trade-offs. Governance is scoped to the Databricks boundary, so AI traffic originating outside the platform, including coding agents, desktop AI, and applications running elsewhere, is not covered. Organizations running AI across multiple clouds and surfaces need a control plane that is not tied to one data platform, a point developed in AWS Bedrock gateway alternatives.

Frequently Asked Questions

What is the best Cloudflare AI Gateway alternative for enterprises?

Bifrost is the strongest enterprise fit, because it can be self-hosted, deployed in-VPC, or run air-gapped while enforcing identity-bound virtual keys, hierarchical budgets, guardrails on prompts and responses, RBAC with OIDC provisioning, and HMAC-signed audit logs. It adds 11 microseconds of overhead per request at 5,000 requests per second.

Can Cloudflare AI Gateway be self-hosted?

No. Cloudflare AI Gateway runs as a hosted service on Cloudflare's network, so AI traffic transits Cloudflare infrastructure by design. Organizations that require the inference path to stay inside their own VPC, region, or air-gapped environment need a self-hostable gateway such as Bifrost, LiteLLM, Kong AI Gateway, or Envoy AI Gateway.

How much does Cloudflare AI Gateway cost?

Core features including dashboard analytics, caching, and rate limiting are free on all plans. Persistent logs are capped at 100,000 entries on Workers Free and 10,000,000 per gateway on Workers Paid, guardrail evaluation is billed as Workers AI token-based inference, and Logpush export carries per-million-request charges beyond its included volume.

What should an enterprise AI gateway include for compliance?

At minimum: identity-bound access control, enforced budgets and rate limits, guardrails on both prompt and response, tamper-evident audit logs with defined retention and export, SSO or OIDC role mapping, and a deployment option inside the organization's own infrastructure. Those six cover the substance of most SOC 2, HIPAA, GDPR, and ISO 27001 reviews.

Does an AI gateway need to govern MCP servers?

Yes, when agents are in use. An MCP server following the Model Context Protocol can read files, call internal APIs, and take actions autonomously, so tool access carries more risk than model access. Governing models and tools from a single MCP gateway keeps both under the same virtual keys, budgets, and audit records rather than in separate systems.

How hard is it to migrate from a hosted AI gateway?

It is usually a configuration change rather than a rewrite. Bifrost is a drop-in replacement for the OpenAI, Anthropic, AWS Bedrock, Google GenAI, LangChain, and PydanticAI SDKs, so applications point at a new base URL and keep existing prompts, streaming behavior, and tool-calling structures unchanged.

Choosing an Enterprise AI Gateway in 2026

The right Cloudflare AI Gateway alternative follows from where the organization's constraints sit. Teams that only need caching and analytics on Workers traffic are well served by the hosted option. Enterprises accountable for data residency, per-team cost control, prompt-level data protection, and audit evidence need a gateway they can place inside their own infrastructure and govern by identity. Bifrost provides that control plane in an open-source core, with Bifrost Enterprise adding clustering, advanced governance, and in-VPC deployment, and Bifrost Edge extending the same policies to every employee machine.

To see how Bifrost would run inside your environment, book a demo with the Bifrost team, or start with the governance resource hub and the LLM gateway buyer's guide.