DEV Community: Moussa Coulibaly

7 Best Cloudflare AI Gateway Alternatives for Production AI

Moussa Coulibaly — Thu, 23 Jul 2026 22:09:32 +0000

As AI applications move to production, engineering teams often need more performance, control, and provider flexibility than Cloudflare AI Gateway provides. This guide compares the top 7 alternatives, with Bifrost ranking as the best overall choice for enterprise workloads.

Cloudflare AI Gateway provides a convenient, edge-based proxy for routing and monitoring AI traffic. For developers already using the Cloudflare ecosystem, it offers a fast way to add basic caching, rate limiting, and analytics to LLM calls. However, teams building production-grade, scalable AI systems often encounter its limitations. Key features like automatic provider failover, semantic caching, granular governance, and support for agentic protocols like MCP are areas where specialized gateways offer more robust solutions.

This guide examines seven of the best alternatives to Cloudflare AI Gateway, evaluating each on performance, features, and deployment model to help you choose the right infrastructure for your production AI workloads.

Key Evaluation Criteria for an AI Gateway

Before comparing the tools, it's important to define what separates a basic proxy from a production-ready AI gateway:

Performance: The gateway should introduce minimal latency. High-performance gateways are often built in compiled languages like Go to handle thousands of requests per second with microsecond-level overhead.
Provider Support & Failover: The gateway must support a wide range of LLM providers and offer automatic failover and load balancing to route around outages and maintain application reliability.
Governance & Cost Control: Features like virtual keys, per-user or per-team budgets, and role-based access control (RBAC) are critical for managing costs and access in an enterprise setting.
Advanced Caching: Semantic caching, which serves cached responses for semantically similar queries, offers significantly more cost and latency savings than simple request/response caching.
MCP Gateway Support: For building AI agents that can interact with external tools, native support for the Model Context Protocol (MCP) is essential.
Deployment Flexibility: The ability to deploy the gateway in a VPC, on-premises, or in an air-gapped environment is a requirement for many enterprises with strict data residency or compliance needs.

The Top 7 Cloudflare AI Gateway Alternatives

Based on these criteria, here are the top seven alternatives for teams that have outgrown Cloudflare's offering.

1. Bifrost

Bifrost is a high-performance, open-source AI gateway from Maxim AI, written in Go. It's designed specifically for production AI infrastructure, unifying LLM, MCP, and agent gateway capabilities into a single, scalable platform. For enterprises and teams running mission-critical AI, Bifrost is the most comprehensive alternative.

Best for: Enterprise teams needing a high-performance, self-hostable AI gateway with granular governance and native support for agentic workflows.

Key Features:

Exceptional Performance: Bifrost adds only 11 microseconds of overhead at 5,000 requests per second, making it one of the fastest gateways available. This is a critical advantage for latency-sensitive applications.
Unified API & Provider Failover: It provides a single OpenAI-compatible API for over 20 providers, including OpenAI, Anthropic, AWS Bedrock, and Google Vertex AI. Its automatic failover and load balancing capabilities ensure zero-downtime reliability when a primary provider fails.
Enterprise Governance: Bifrost offers advanced governance features like virtual keys with hierarchical budgets, rate limits, and access controls that can be applied per user, team, or customer.
Semantic Caching: Its semantic caching goes beyond Cloudflare's basic caching to reduce costs and latency on semantically similar queries.
Native MCP Gateway: Bifrost includes a native MCP gateway that enables AI agents to discover and execute external tools, a feature not present in Cloudflare AI Gateway.
Endpoint Governance: Beyond the gateway, Bifrost Edge extends the same governance and security controls to AI traffic on employee machines, tackling the problem of shadow AI from desktop apps and coding agents.

2. LiteLLM

LiteLLM is a popular open-source project that provides a unified, OpenAI-compatible interface to over 100 LLM providers. It's written in Python and is a strong choice for teams who need to self-host and want the broadest possible provider coverage.

Best for: Teams who need to self-host an open-source gateway and prioritize the widest range of model provider integrations.

Key Features:

Extensive Provider Support: LiteLLM's main strength is its vast library of provider integrations.
Self-Hosted Control: As an open-source tool, it can be deployed anywhere, giving teams full control over their data path, which is a key differentiator from Cloudflare's SaaS-only model.
Cost Management: It includes features for tracking costs per API key and enforcing budgets.

Tradeoffs:

Being Python-based, LiteLLM has higher latency overhead compared to Go-based alternatives like Bifrost.
While it has basic routing and fallback, its enterprise governance and advanced features like semantic caching are less mature than Bifrost's.

3. Kong AI Gateway

Kong AI Gateway extends the widely-used Kong API Gateway with a suite of plugins for managing AI traffic. It's a natural fit for enterprises that have already invested in Kong for their existing API management.

Best for: Organizations already using Kong for API management that want to add AI governance into their existing infrastructure.

Key Features:

Unified API Management: Manages both traditional API traffic and LLM requests from a single control plane.
AI-Specific Plugins: Offers features like prompt engineering, AI observability, and token-based rate limiting.
Data Governance: Provides features like PII sanitization and the ability to enforce allow/deny lists for prompts.

Tradeoffs:

The learning curve and configuration overhead can be high for teams not already familiar with Kong.
While powerful, it is primarily an API management platform with AI features added, rather than an AI-native gateway built from the ground up.

4. OpenRouter

OpenRouter began as an LLM marketplace and has evolved into a popular managed AI gateway that provides access to hundreds of models through a single API key. It focuses on simplifying access and offering competitive, pay-as-you-go pricing.

Best for: Developers and small teams looking for a simple, managed way to access a wide variety of models without managing infrastructure.

Key Features:

Vast Model Selection: Offers one of the largest catalogs of available models through a single integration.
Simplified Access: A single API key provides access to all integrated providers.
Cost-Effective: Pay-per-use pricing with no monthly commitments makes it easy to get started.

Tradeoffs:

OpenRouter is more of a model router than a full-fledged AI gateway. It lacks the deep governance, security, and observability features required for enterprise production use.

5. Databricks Unity AI Gateway

Databricks Unity AI Gateway is a governance solution for enterprise AI that is deeply integrated with the Databricks ecosystem. It extends the governance of Unity Catalog to cover the runtime interactions between models, agents, and tools.

Best for: Enterprises heavily invested in the Databricks platform that want to unify governance for data and AI workloads.

Key Features:

Unified Governance: Provides a single control plane for models, agents, and MCP services within the Databricks environment.
Cost Management: Offers unified visibility and granular cost attribution by user, team, and use case.
Ecosystem Integration: Built to work seamlessly with the rest of the Databricks Lakehouse Platform.

Tradeoffs:

It is tightly coupled to the Databricks ecosystem and is not a standalone gateway for general-purpose use.
Performance is not independently benchmarkable and is tied to the Databricks runtime.

6. Apache APISIX

Apache APISIX is a high-performance, open-source API gateway that has added AI capabilities through plugins. Like Kong, it's a strong option for teams who need a mature API management solution that can also handle LLM traffic.

Best for: Teams needing a flexible, high-performance open-source API gateway that can be extended to manage AI traffic.

Key Features:

High Performance: Known for its speed and scalability in managing traditional API traffic.
Dynamic and Extensible: Supports dynamic plugin loading and plugins written in multiple languages.
AI Proxy Plugin: Offers a plugin for proxying requests to major LLM providers.

Tradeoffs:

APISIX is an API gateway first, not an AI-native gateway. It lacks advanced AI-specific features like semantic caching and deep governance for virtual keys or MCP.

7. Envoy AI Gateway

Envoy AI Gateway is an open-source project that extends the popular Envoy proxy to manage traffic for generative AI services. It is designed to integrate natively with Kubernetes and service mesh environments like Istio.

Best for: Organizations already standardized on Envoy or Istio for their service mesh.

Key Features:

Native Kubernetes Integration: Built on the Kubernetes Gateway API for seamless integration.
Extends Envoy: Leverages the battle-tested performance and reliability of the Envoy proxy.
AI-Aware Routing: Provides LLM-aware routing, token-based rate limiting, and cost tracking.

Tradeoffs:

It's a newer project compared to more established gateways.
It assumes an existing investment and expertise in Envoy and service mesh architecture.

Conclusion: Choosing the Right Gateway

Cloudflare AI Gateway is a solid starting point for developers who need basic observability and caching at the edge. However, the demands of production AI often require more specialized tools.

For enterprise teams, the choice frequently comes down to performance, governance, and deployment flexibility. In this context, Bifrost stands out as the strongest alternative. Its combination of microsecond-level performance, comprehensive enterprise governance, native MCP support for agentic applications, and the flexibility of an open-source, self-hostable architecture makes it the most capable platform for scaling AI infrastructure reliably and securely.

Teams evaluating their next AI gateway can request a Bifrost demo or review the open-source repository to learn more.

Sources

Best Enterprise AI Tools by Function (Sales, Support, Eng, Ops)

Moussa Coulibaly — Tue, 14 Jul 2026 15:22:49 +0000

This topic could be written from either a Bifrost (AI gateway/infrastructure) or Maxim AI (evaluation/observability) angle. Which product should be the primary focus?

CASB Alternatives for Governing Generative AI

Moussa Coulibaly — Thu, 09 Jul 2026 10:13:13 +0000

As generative AI proliferates, traditional Cloud Access Security Brokers (CASBs) often fall short in comprehensive governance. This article explores dedicated alternatives and strategies for securing and controlling large language model (LLM) usage across the enterprise, identifying Bifrost as a leading solution for full-stack AI governance.

The rapid adoption of generative AI tools across enterprises has introduced novel security and governance challenges that often outpace the capabilities of existing infrastructure. While Cloud Access Security Brokers (CASBs) have been a cornerstone of cloud security, providing visibility and control over sanctioned SaaS applications and data in the cloud, their architecture and focus frequently struggle to keep pace with the unique demands of large language model (LLM) usage. Bifrost, an open-source AI gateway from Maxim AI, offers a more direct and comprehensive approach to governing generative AI traffic, from the central gateway to the individual endpoint.

The Limitations of Traditional CASBs in Governing Generative AI

Traditional CASBs were primarily designed to address challenges associated with SaaS application usage and data residency. They excel at monitoring and controlling access to known cloud services, enforcing data loss prevention (DLP) policies, and identifying shadow IT where unsanctioned cloud apps are in use. However, generative AI introduces several complexities that can bypass or overwhelm these established controls:

Protocol and API Diversity: While many LLM interactions occur over standard HTTP/S APIs, the nature of the data (prompts and completions) and the rapid evolution of models and providers present a moving target. CASBs may struggle to deeply inspect and apply granular policies to these dynamic LLM conversations.
Endpoint Proliferation (Shadow AI): Generative AI tools are increasingly deployed as desktop applications, browser extensions, and coding agents directly on employee machines. This "shadow AI" bypasses network perimeters and traditional CASB visibility, allowing sensitive data to flow directly from the endpoint to external LLM providers without organizational oversight.
Focus on Known Services: CASBs typically operate with a predefined catalog of cloud applications. The landscape of LLM providers and specialized AI tools is vast and constantly expanding, making it difficult for CASBs to maintain comprehensive coverage and policy enforcement for every emerging AI service.
Contextual Understanding of Prompts: Applying effective governance to generative AI requires understanding the intent and content of prompts and responses, not just blocking known file types. Traditional DLP capabilities in CASBs, while useful for structured data, may not be nuanced enough to detect IP leakage or sensitive information in natural language interactions without extensive customization.

These limitations highlight a significant gap in an organization's security posture, leaving sensitive data vulnerable and compliance at risk.

The Urgent Need for Dedicated Generative AI Governance

The uncontrolled proliferation of generative AI tools creates a new vector for critical enterprise risks, demanding a dedicated governance strategy.

Data Leakage and IP Exposure: Employees feeding proprietary code, customer data, or internal strategies into public LLMs can lead to unintended data exfiltration and intellectual property loss.
Compliance Violations: Industries subject to regulations like GDPR, HIPAA, SOC 2, or financial compliance can face severe penalties if sensitive customer or employee data is processed or stored by unapproved AI services without an audit trail.
Unapproved Model Usage: Without governance, employees might use models that are unvetted for accuracy, bias, or data privacy, leading to unreliable outputs or the inadvertent spread of misinformation.
Cost Sprawl: Ungoverned LLM usage can lead to unexpected and uncontrolled API costs, especially for high-volume or complex queries.
Lack of Auditability: Most traditional security tools lack the granular logging and monitoring necessary to create an immutable audit trail of AI interactions, which is essential for incident response and compliance.

Organizations require visibility into what AI tools are being used, who is using them, what data is being shared, and how those interactions align with internal policies and external regulations.

Emerging Alternatives and Strategies for AI Governance

Addressing the gaps left by traditional CASBs for generative AI requires specialized approaches. Several categories of solutions are emerging to tackle this problem:

Specialized AI Gateways: These act as an intelligent proxy layer for all LLM API traffic, centralizing routing, authentication, load balancing, cost management, and governance for prompts and responses.
Endpoint AI Governance Agents: These are software agents deployed directly onto user devices to enforce policies locally, particularly for desktop AI applications, browser-based AI, and coding assistants that bypass network controls.
Enhanced Data Loss Prevention (DLP) for LLMs: Some DLP solutions are evolving to better understand natural language, but still often focus on content scanning rather than full lifecycle governance of AI interactions.
Dedicated AI Security Platforms: Broader platforms that combine elements of gateway functionality, endpoint control, and specific AI security features like prompt injection detection or model output validation.

For enterprises aiming for comprehensive control and visibility, a combination of specialized AI gateways and endpoint governance agents often provides the most robust solution.

Bifrost: A Comprehensive AI Gateway and Endpoint Governance Solution

Bifrost addresses the unique challenges of generative AI governance by acting as both a centralized AI gateway and an endpoint enforcement mechanism. It unifies access to over 1000 models through a single OpenAI-compatible API, while also extending crucial governance and security controls to every machine in an organization.

As an AI gateway, Bifrost functions as the central control plane, where administrators configure virtual keys, budgets, rate limits, routing rules, and audit logging. This centralized approach enables consistent policy enforcement across all AI applications configured to route through it. Organizations can implement automatic fallbacks and load balancing to ensure reliability and cost optimization across multiple LLM providers. Bifrost also functions as an MCP gateway, allowing for the secure and governed execution of external tools by AI agents.

Bifrost extends this powerful governance to the endpoint through Bifrost Edge. Bifrost Edge is an agent deployed on employee macOS, Windows, and Linux machines that routes all AI traffic from desktop applications, browser AI, and coding agents through the central Bifrost gateway. This critical component eliminates "shadow AI" by bringing otherwise ungoverned endpoint usage under the same policies configured in the Bifrost gateway.

Key capabilities of Bifrost Edge include:

App Governance: Administrators can allow or deny specific AI applications (e.g., Claude Desktop, ChatGPT web, Cursor) across the fleet, with policies enforced directly on the device.
MCP Governance: Edge provides unprecedented visibility into which Model Context Protocol (MCP) servers users have configured within their AI tools, enabling admins to approve or deny these external tool connections fleet-wide.
Unified Guardrails: The same guardrails configured in Bifrost (e.g., secrets detection, custom regex for PII, AWS Bedrock Guardrails, Azure Content Safety) are automatically applied to endpoint AI traffic, protecting sensitive data before it leaves the machine.
MDM Deployment: Designed for enterprise rollout, Bifrost Edge supports fleet-wide deployment via MDM platforms like Jamf, Microsoft Intune, and Kandji, simplifying adoption.

Bifrost, with Edge, provides full-stack AI governance that spans both server-side and client-side AI interactions, ensuring that an organization's security, compliance, and cost control policies apply consistently everywhere.

Other Approaches to Generative AI Governance

While Bifrost offers a unified gateway-plus-endpoint solution, other individual tools and strategies also contribute to the broader AI governance landscape.

Network-Level Proxies and Firewalls: These can block access to known unsanctioned AI domains or apply basic content filtering. However, they lack the deep LLM context required for nuanced policy enforcement and cannot distinguish between approved and unapproved uses of the same AI service, nor can they govern desktop applications that bypass network proxies.
Cloudflare AI Gateway provides caching, rate limiting, and observability for AI inferences, acting as an intelligent edge for LLM requests. It is a hosted solution, primarily focusing on network-level optimization and security for API calls.
Kong AI Gateway offers an API management solution tailored for AI traffic, including features like prompt engineering, caching, and policy enforcement within the Kong ecosystem. Its strength lies in integrating AI governance into existing API gateway deployments.
Data Loss Prevention (DLP) Software: Modern DLP solutions are evolving to identify sensitive data in prompts and responses, but they are typically reactive (blocking after detection) and may struggle with the sheer volume and variability of LLM interactions. They do not inherently provide the routing, load balancing, or endpoint app governance that dedicated AI gateways or agents offer.
Specialized AI Security Platforms: Some platforms offer AI-specific threat detection and vulnerability scanning. While valuable for identifying risks within AI models and applications, they often do not provide the foundational infrastructure for traffic routing, policy enforcement across multiple providers, or endpoint control.

These alternatives address specific aspects of AI governance, but often require integration of multiple disparate tools to achieve comprehensive coverage, potentially leading to complexity and gaps.

Selecting the Right AI Governance Strategy

For enterprises, selecting the right generative AI governance strategy hinges on achieving comprehensive visibility, consistent enforcement, and scalability, while meeting compliance needs.

Full Visibility: The solution must be able to see all AI traffic, regardless of whether it originates from a server-side application, a coding assistant, or a browser tab.
Granular Control: The ability to set and enforce policies on who can use which models, what data can be shared, and at what cost is paramount.
Compliance and Auditability: Robust audit logs and the ability to integrate with enterprise identity providers are non-negotiable for regulated industries.
Ease of Deployment and Management: A solution that can be rolled out efficiently across a large fleet via existing MDM infrastructure and managed centrally reduces operational overhead.

A combined AI gateway and endpoint governance approach, like Bifrost and Bifrost Edge, provides a single pane of glass for configuring and enforcing policies, closing the shadow AI gap, and ensuring that all generative AI usage aligns with organizational requirements. This integrated strategy offers the control and visibility that traditional CASBs cannot natively deliver for the dynamic world of LLM interactions.

Teams evaluating AI gateways and endpoint governance can request a Bifrost demo or review the open-source repository for more information.

Sources

The Register. (2023, November 13). CASBs struggle to grasp generative AI risk – report. https://www.theregister.com/2023/11/13/casbs_generative_ai_risk_report/
Gartner. (2024, February 19). Security Threats of Generative AI. CASB for Generative AI Security. https://www.gartner.com/en/articles/security-threats-of-generative-ai
Maxim AI. (n.d.). End Shadow AI with Bifrost Edge. https://www.getmaxim.ai/bifrost/edge
IBM. (2023, August 3). The rise of shadow AI: how to manage it and reduce risks. https://www.ibm.com/blogs/research/2023/08/03/shadow-ai/
Snyk. (2023, October 11). GenAI security and compliance: Mitigating new risks. https://snyk.io/blog/genai-security-compliance-mitigating-risks/

Building Dashboards for LLM Usage and Performance

Moussa Coulibaly — Thu, 02 Jul 2026 17:28:46 +0000

An analysis of key metrics and tools for creating effective LLM usage and performance dashboards. For teams needing enterprise-grade observability, tools like Bifrost provide built-in metrics and integrations to simplify the process.

Tracking the behavior of large language models in production is essential for maintaining application reliability, managing costs, and ensuring a high-quality user experience. As AI applications scale, manually monitoring API calls becomes impractical. Engineering teams require dedicated LLM usage and performance dashboards to visualize key metrics, identify trends, and troubleshoot issues. An open-source AI gateway like Bifrost can serve as a central point for collecting the necessary data for these dashboards.

Why Dashboards are Critical for LLM Operations

Dashboards provide a consolidated, real-time view of an AI application's health. Without them, teams operate with significant blind spots, reacting to problems only after they impact users. A well-designed dashboard helps teams proactively manage several key areas:

Cost Management: Visualize token consumption and cost per request, per user, or per model to prevent budget overruns.
Performance Monitoring: Track metrics like latency (time to first token and total response time) and throughput to ensure the application meets performance SLOs.
Error Detection: Quickly identify and diagnose spikes in API errors, provider outages, or model-specific failures.
Usage Analysis: Understand which models are being used most frequently, who the top users are, and how request patterns change over time.

Key Metrics to Track in an LLM Dashboard

An effective LLM dashboard goes beyond simple request counts. It should provide a granular view into the operational metrics that directly affect cost, performance, and reliability. Teams should focus on visualizing the following categories.

Cost and Usage Metrics

Token Counts: Track prompt tokens, completion tokens, and total tokens per request. Aggregate this data by model, user, and time period.
Request Volume: Monitor the total number of requests, broken down by model and API key.
Estimated Cost: If cost data is available, visualize the cumulative cost over time to align with budget forecasts.

Performance and Latency Metrics

End-to-End Latency: The total time from when a request is sent to when the final token is received.
Time to First Token (TTFT): Measures how quickly the model begins generating a response. This is a critical metric for user-perceived performance in streaming applications.
Tokens per Second (Throughput): Indicates the generation speed of the model once it starts responding.

Reliability and Error Metrics

Error Rate: The percentage of requests that fail, categorized by HTTP status code (e.g., 4xx, 5xx) and provider-specific error types.
Provider Health: Monitor the uptime and response times of each connected LLM provider to detect outages or degradation.
Fallback and Retry Rates: If using a gateway with automatic fallbacks, track how often requests are rerouted due to primary provider failures.

Approaches to Building LLM Dashboards

Teams have several options for building and deploying dashboards, ranging from using managed services to building custom solutions on open-source tooling.

1. Using an AI Gateway with Built-in Observability

The most direct approach is to use an AI gateway that provides observability features out of the box. A gateway like Bifrost is positioned to capture detailed metadata about every request and expose it in standard formats.

Native Prometheus Metrics: Bifrost exposes a /metrics endpoint compatible with Prometheus, a leading open-source monitoring system. This allows teams to scrape detailed metrics on requests, latency, token counts, and errors directly from the gateway. These metrics can then be visualized in Grafana, a popular open-source dashboarding tool.
OpenTelemetry Integration: For more complex environments, Bifrost supports the OpenTelemetry (OTLP) standard. This enables the export of distributed traces and metrics to compatible backends like Honeycomb, New Relic, or Jaeger, providing deeper insights into the entire request lifecycle.
Dedicated Connectors: For enterprises standardized on specific platforms, Bifrost offers a Datadog connector that sends traces, metrics, and logs directly to Datadog for unified observability.

This approach centralizes data collection at the infrastructure layer, requiring no changes to the application code itself.

2. Instrumenting Application Code

Alternatively, teams can add monitoring libraries directly to their application's source code. SDKs for platforms like OpenAI and Anthropic can be wrapped with custom code to log metrics to a time-series database or observability platform.

While this method offers high flexibility, it also has drawbacks:

Increased Complexity: Each application and service must be individually instrumented and maintained.
Inconsistent Data: It can be difficult to ensure that all teams are collecting the same set of metrics in a consistent format.
Lack of Central Control: Governance and routing logic are distributed across applications rather than managed from a central point.

3. Leveraging Managed LLM Observability Platforms

Several third-party platforms specialize in LLM observability. These services typically provide an SDK that teams integrate into their applications. The SDK sends data to the vendor's platform, which offers pre-built dashboards and analytics tools. This can accelerate deployment, but it also introduces a dependency on an external service and may not provide the same level of control as a self-hosted gateway.

How Bifrost Simplifies Dashboard Creation

Using an AI gateway like Bifrost as the data source for dashboards provides a powerful and scalable solution. Because all LLM traffic routes through the gateway, it becomes the single source of truth for all operational metrics.

The gateway's native observability features mean that engineering teams can connect their existing monitoring tools like Grafana or Datadog and start building dashboards immediately. For example, a team could create a Grafana dashboard with panels for:

Requests per Minute: A time-series graph showing total throughput.
P95 Latency by Model: A chart tracking the 95th percentile latency for each model.
Token Usage by Virtual Key: A table showing which projects or users are consuming the most tokens, using Bifrost's virtual keys for attribution.
Error Rate by Provider: A pie chart breaking down errors by the upstream LLM provider.

This setup not only provides deep visibility but also reinforces security and governance. Bifrost applies central governance policies, and with Bifrost Edge, that same visibility and control can be extended to AI usage on employee endpoints, ensuring that even traffic from desktop tools is captured in the central dashboards.

Getting Started with LLM Dashboards

Effective dashboards are a cornerstone of reliable AI operations. They transform raw operational data into actionable insights, enabling teams to optimize performance, control costs, and quickly resolve production issues. While multiple approaches exist, centralizing metric collection at the gateway layer offers a clean, scalable, and non-intrusive solution.

Teams evaluating AI gateways for this purpose can request a Bifrost demo or review the open-source repository to explore its observability capabilities.