An AI demo is relatively easy to put together: connect an API key, send a prompt and display the response. Production is different. Providers impose rate limits, a model that performs well on one task may struggle on another, and teams need to know which application is generating unexpected costs. A gateway becomes valuable when these problems stop being isolated incidents and start affecting customers.
An AI gateway sits between an application and its model providers. Depending on the product, it can standardize APIs, route requests, enforce access policies, record usage, and recover from provider failures. But not every gateway offers the same depth of control. Some prioritize developer simplicity; others are designed for platform teams operating AI across an organization.
This guide compares seven options through practical production questions: What happens when a provider fails? Can different teams share infrastructure without sharing unrestricted credentials? How much work is required to operate the gateway? And can engineers diagnose a slow or expensive request after the fact?
1. TrueFoundry – best AI gateway for Enterprises
TrueFoundry is a strong choice when several engineering teams are shipping AI features and the platform team needs one place to manage model access. Its AI Gateway supports more than 2,000 models and brings routing, observability, security and cost controls into a common layer. Rather than each application independently implementing provider integrations and policies, teams can use a shared gateway while retaining visibility into usage.
The distinction matters when an organization moves beyond a single chatbot. A support assistant may use one model for routine answers, a larger model for complex questions and an agent workflow to retrieve information from approved tools. TrueFoundry’s support for MCP servers and agent workflows makes it relevant to these architectures, although the surrounding application still needs to implement appropriate tool permissions and error handling.
Key features of TrueFoundry
- Unified model access: Connect supported models through a common interface instead of maintaining separate integrations in every service.
- Model routing: Configure which models handle requests based on the workload and operational requirements.
- Fallbacks: Send eligible requests to a backup model when the preferred route fails.
- Observability: Review request behavior, latency and failures to investigate production incidents.
- Cost controls: Track consumption and manage spending across applications and teams.
- Governance and MCP support: Apply access policies while supporting agent connections to external tools.
Where it fits
Imagine a SaaS business with three AI products: a document assistant, a support copilot and an internal research agent. Each has a different traffic pattern and different model needs. With a central gateway, the platform team can standardize access and monitor costs while product engineers continue iterating on prompts and application logic. TrueFoundry is particularly compelling in this shared-platform scenario.
Trade-off: Teams running one lightweight prototype should weigh the operational features against setup and governance work. A proof of concept should test the exact models, deployment approach and policy requirements before committing.
2. Cloudflare AI Gateway — best for request visibility and traffic controls
Cloudflare AI Gateway gives developers a layer for observing and controlling AI requests. It is especially relevant when a team already uses Cloudflare infrastructure or wants to understand request volumes, latency and provider errors without building a custom logging service.
Key features of Cloudflare AI Gateway
Request analytics: Track volumes, latency and errors across supported model traffic.
Caching: Reuse eligible identical responses to avoid unnecessary model calls.
Rate limiting: Apply controls that protect budgets and provider quotas.
Request logging: Inspect supported request information while considering sensitive-data handling.
Routing controls: Configure supported provider selection and fallback behavior.
Where it fits
A customer-facing application might experience sharp traffic increases after a product launch. Cloudflare can help operators see whether slow responses stem from higher request volume or provider behavior. Caching may reduce costs for repeated, non-personalized requests, but should not be treated as a universal optimization for dynamic or sensitive prompts.
Trade-off: Evaluate whether its controls satisfy your organization’s internal governance, identity and deployment requirements rather than assuming edge traffic management covers every enterprise policy.
3. Vercel AI Gateway — best for web product teams moving quickly
Vercel AI Gateway appeals to developers who want access to multiple models without maintaining a different client integration for every provider. It is a natural candidate for teams building AI features into web products, particularly when they already use Vercel’s application development ecosystem.
Key features of Vercel AI Gateway
Unified API: Reach supported models through one gateway interface.
Provider fallbacks: Configure alternative providers for supported requests.
Usage tracking: Review token consumption and model spending.
Budget controls: Set supported spending limits for projects or teams.
Developer tooling: Integrate with Vercel’s AI SDK and supported frameworks.
Where it fits
Consider a startup adding a writing assistant to its dashboard. Engineers need to test models, ship changes and understand costs, but may not yet need a dedicated internal AI platform team. A developer-oriented gateway can shorten the path from experiment to usable feature.
Trade-off: A larger enterprise should separately validate cross-team permissions, audit expectations and deployment constraints. Developer convenience and enterprise governance are related but not identical requirements.
4. Azure AI — best for organizations standardized on Microsoft cloud
Azure’s AI ecosystem, including Microsoft Foundry and associated gateway and routing capabilities, can suit organizations that already run applications, identity and data infrastructure on Azure. The main appeal is integration with an established cloud operating model, rather than treating the AI gateway as an isolated purchase.
Key features of Azure AI
Managed model access: Deploy and consume supported AI models through Azure services.
Routing options: Use supported model routing capabilities for selected workloads.
Identity integration: Apply Azure identity and access controls to AI resources.
Monitoring: Observe supported applications through Azure operational tooling.
Cloud integration: Connect AI workloads with existing Azure services and policies.
Where it fits
A regulated business may already have approved Azure subscriptions, identity processes and security reviews. Building its AI application in that environment may simplify ownership and procurement. The evaluation should still identify which exact Azure services provide gateway functionality and how requests are logged, routed and billed.
Trade-off: The broader cloud architecture can be more complex than a standalone gateway. Compare the complete implementation and operational burden, not just the advertised model catalog.
5. Envoy — best for teams building a customized gateway layer
Envoy is an open-source proxy rather than a turnkey AI gateway product. It is included because infrastructure teams sometimes use it as a foundation for routing, authentication and observability around AI services. This route offers flexibility but also puts more implementation responsibility on the team.
Key features of Envoy
Traffic routing: Direct requests between services using configurable rules.
Load balancing: Distribute supported traffic across available backends.
Authorization integration: Connect request handling to external policy systems.
Metrics and tracing: Export telemetry to compatible observability tools.
Extensibility: Add filters and custom logic for specialized traffic requirements.
Where it fits
A platform team already operating Envoy across microservices might extend that infrastructure to route traffic to model-serving endpoints. It can preserve consistency with existing service operations, but model-aware budgets, token accounting and agent governance may require additional software or custom engineering.
Trade-off: Do not confuse proxy flexibility with out-of-the-box AI operations. Estimate the engineering time needed to deliver and maintain the missing AI-specific features.
6. Helicone — best for debugging and understanding LLM requests
Helicone is worth considering when a team’s most immediate problem is understanding what happened during an AI request. Request-level visibility can help diagnose slow responses, unexpected costs and differences between models or prompts. This is especially useful during the transition from prototype to production, when errors are difficult to reproduce.
Key features of Helicone
Request logging: Inspect supported model interactions for debugging.
Cost analysis: Identify expensive requests and changing usage patterns.
Latency monitoring: Compare response times across models and workloads.
Usage analytics: Review request trends over time.
Developer investigation: Connect operational symptoms to individual requests.
Where it fits
Suppose a document-processing feature becomes slower after a prompt update. Engineers need to determine whether the change increased input tokens, triggered longer outputs or coincided with a provider issue. Detailed request telemetry can turn that investigation into a repeatable process instead of guesswork.
Trade-off: Check the specific gateway, deployment and policy features you need. Strong observability does not automatically mean the product replaces an organization’s broader model governance stack.
7. Kong AI Gateway — best for mature API platform teams
Kong extends familiar API gateway practices into AI traffic management. For companies that already use Kong to secure and operate APIs, applying similar policies to AI requests can be attractive. The value is partly organizational: existing platform engineers may already know how to operate the gateway and integrate it into service workflows.
Key features of Kong AI Gateway
AI request routing: Manage supported model traffic through gateway configurations.
Authentication: Enforce access policies for applications using AI endpoints.
Rate limiting: Restrict excessive traffic and protect shared services.
Traffic policies: Apply gateway controls to AI-related API requests.
Platform integration: Use Kong’s API management capabilities alongside AI services.
Where it fits
An enterprise with dozens of internal APIs may prefer to extend its existing gateway approach to AI rather than introduce an entirely separate operational model. This can be sensible when ownership, authentication and deployment processes are already standardized around Kong.
Trade-off: Confirm the AI-specific features available in the chosen Kong edition and deployment. Existing API gateway expertise helps, but model behavior introduces new cost and observability questions.
How to evaluate an AI gateway before deployment
A feature comparison is useful for building a shortlist, but it will not tell you how a gateway behaves with your own applications. Run a small, repeatable proof of concept using the same workloads across candidates. Include a straightforward request, a long-context request and a request that calls an external tool if your application uses agents.
Test failure handling, not just successful requests
Simulate a provider timeout, a rate-limit response and a temporarily unavailable model. Record whether the gateway retries, falls back or returns an error, and how much additional latency each path creates. A fallback is only useful if the substitute model is compatible with the task and the application can safely handle its output.
Check security boundaries
Create two test applications with different permissions. Verify that one cannot use the other’s credentials, models or restricted routes. Inspect how secrets are stored and rotated, what request content is logged and whether sensitive information can be excluded or redacted. For agent systems, separately test permissions for external tools.
Measure costs and operational overhead
Track both provider charges and the work required to operate the gateway. A self-managed proxy may have a low software cost but demand engineering time for upgrades, monitoring and incident response. A managed platform may reduce that burden while introducing different pricing and deployment considerations.
Inspect what operators can actually see
Generate a slow request and a failed request, then ask an engineer who did not build the test to diagnose both. Can they identify the application, model, latency, error type and relevant usage data? Clear telemetry matters more than a dashboard that looks impressive but cannot answer operational questions.
Which gateway should you choose?
Choose based on the problem your team needs to solve first. Cloudflare AI Gateway is attractive for request analytics and traffic controls. Vercel AI Gateway fits teams prioritizing developer workflows in web applications. Azure AI may align with organizations already committed to Microsoft’s cloud stack. Envoy suits experienced infrastructure teams prepared to build custom capabilities, while Helicone emphasizes request-level diagnostics and Kong builds on established API gateway operations.
TrueFoundry stands out when the priority is a centralized operating layer across multiple models, applications and agent workflows. Its combination of routing, governance, observability and cost management makes it particularly relevant for platform teams supporting several production AI products. That is an editorial recommendation based on feature fit, not a claim that one gateway has won an independent performance benchmark.
Conclusion
The right AI gateway is not simply the one with the longest list of supported models. It is the one your team can operate confidently when a provider fails, a workload spikes or a security policy changes. Start with a small production-like test, measure reliability and costs, and choose the level of control your organization actually needs. For teams coordinating many AI applications and agents, TrueFoundry deserves a place at the top of the shortlist.
