TL;DR

• AI gateways sit between applications and LLM providers to centralize routing, security, observability, and cost control, replacing fragile direct model integrations spread across services.

• Gateways exist because LLM access breaks at scale when model selection, retries, logging, encryption, and budgets are handled in application code rather than in shared infrastructure.

• Enterprises and startups evaluate gateways differently: enterprises prioritize data protection, auditability, and provider neutrality, while startups favor fast integration, flexible routing, and low operational overhead.

• OLLM leads for sensitive and regulated workloads, using zero-knowledge, encrypted routing to aggregate hundreds of models behind one API without exposing prompt data to providers.

• Other gateways optimize for different tradeoffs, including fast experimentation (OpenRouter), agent-centric programmability (LiteLLM), API-level governance (Kong), and observability-driven cost control (PortKey).

What Are AI Gateways

AI gateways are control layers that sit between applications and large language model providers. Instead of calling models directly, applications route every prompt and response through the gateway. This single placement allows teams to manage routing, security, observability, and cost consistently across all AI workloads.

At a basic level, an AI gateway standardizes how applications access models:

• Receives AI requests from applications

• Routes requests to one or more LLM providers based on rules

• Applies policies before returning responses

Applications remain unaware of which model is used or where it runs.

Gateways exist because these responsibilities break when handled inside application code. As AI usage grows, teams need shared infrastructure to own concerns that don’t belong in business logic:

• Model routing: dynamic provider selection, fallbacks, and vendor flexibility

• Security controls: encryption, access rules, and prompt handling in one place

• Observability: unified visibility into prompts, latency, failures, and usage

• Cost control: budgets, limits, and traffic shaping before spend escalates

This control is possible because of where the gateway sits in the request path. Every prompt and response flows through the gateway:

• Application generates a prompt

• Prompt is sent to the AI gateway

• Gateway applies routing and policy rules

• Request is forwarded to the selected model provider

• Response returns through the gateway to the application

This placement provides full visibility without changing application behavior.

Routing becomes configuration-driven rather than code-driven. Instead of hard-coding providers, the gateway selects models based on availability, cost, latency, or data sensitivity. Traffic can shift automatically when providers fail or become expensive, without redeploying services.

Direct model calls vs gateway-mediated calls

By owning the request path, AI gateways turn LLM access into managed infrastructure. This is what enables consistent security, flexible routing, and scalable governance as AI usage expands across teams and products.

How Enterprise and Startup Teams Evaluate AI Gateways for Production Use

Evaluation starts with risk rather than features. Enterprise and startup teams both rely on AI gateways to control LLM access, but they prioritize different constraints based on data sensitivity and operational maturity.

Enterprises focus on control and assurance:

• Encryption, isolation, and prompt handling guarantees

• Clear audit trails across providers

• Vendor-neutral routing

• Predictable behavior under load

Startups focus on speed with guardrails:

• Fast integration and low friction

• Flexible model routing

• Early cost visibility

• Minimal operational overhead

The same core dimensions apply to both. Routing, security, observability, and scalability matter in every environment. Which of these changes will occur first as AI traffic becomes persistent and business-critical?

Best AI Gateways in 2026

1. OLLM: Confidential AI Routing for Zero-Knowledge and Regulated Environments

OLLM is built for teams that treat AI access as a security boundary, not a convenience layer. It operates as a confidential routing layer that keeps prompts and responses protected end to end, even when traffic is distributed across multiple LLM providers. The design goal is simple: enable broad model access without exposing sensitive data or sacrificing control.

Confidentiality is enforced using confidential computing rather than zero-knowledge abstractions. OLLM runs models on confidential computing chips using Trusted Execution Environments (TEEs), ensuring prompts are decrypted only inside secure hardware enclaves. Plaintext content is never accessible to the host OS, cloud provider, or OLLM itself, and no prompt or response data is retained after processing. Encryption is verifiable at rest, in transit, and during execution.  

Flexibility comes from aggregation, not lock-in. OLLM exposes hundreds of models behind a single API while keeping routing rules centralized. Teams can shift traffic, set policies, and audit usage without touching application code. The gateway remains stable even as providers change pricing, APIs, or availability.

Where OLLM fits best

• Enterprises handling sensitive or regulated data

• Platform teams standardizing AI access across products

• Startups building with future compliance in mind

Strengths

• Zero data retention by design

• Plaintext isolation using confidential computing (TEEs)

• Verifiable encryption during execution

• Vendor-neutral access to multiple LLM providers

• Centralized governance and auditability

OLLM reflects how high-risk AI systems are increasingly structured in production. By separating applications from model providers through a confidential routing layer, it allows teams to scale AI usage while maintaining clear boundaries around data exposure and control. This approach fits environments where AI must operate under the same security and governance expectations as other core infrastructure components.

2. OpenRouter: Flexible Model Routing for Fast Experimentation

OpenRouter focuses on making many LLMs accessible through a single, simple routing layer. It abstracts model-specific APIs and exposes a unified interface that lets teams switch between providers quickly. This makes it well suited for environments where testing, comparison, and iteration across models happen frequently.

Routing flexibility is the primary value OpenRouter provides. Teams can direct traffic to different models based on cost, latency, or availability without rewriting application code. When a provider becomes slow or expensive, traffic can shift with minimal disruption. This approach lowers the friction of experimenting with new models as they emerge.

Operational simplicity shapes how OpenRouter is used in practice. The gateway minimizes setup and configuration so teams can focus on building features rather than managing infrastructure. Observability and usage tracking are available, but security and governance controls remain lighter than enterprise-focused gateways.

Where OpenRouter fits best

• Startups and small teams iterating on AI features

• Research and evaluation workflows across many models

• Products prioritizing speed over deep governance

Strengths

• Broad access to many LLM providers

• Simple routing abstraction

• Fast onboarding and low setup overhead

3. LiteLLM: Programmable Routing Embedded in Agent Workflows

LiteLLM approaches AI gateways as a lightweight, programmable layer rather than a standalone platform. It provides a consistent interface over multiple LLM providers and is often embedded directly into application or agent frameworks, especially when used with LangChain. This makes it attractive for teams that want routing control without introducing a heavy external system.

Routing logic lives close to the application. LiteLLM allows developers to define how requests are forwarded, retried, or rate-limited using configuration and code. Model switching, fallback behavior, and provider abstraction happen inside the same environment where agents and chains are defined. For teams already building complex agent workflows, this reduces context switching.

Flexibility comes from composability, not centralized governance. LiteLLM fits naturally into developer-led stacks where control is managed through configuration files and framework conventions. Observability and security controls depend heavily on how the tool is deployed and integrated, which gives teams freedom but also places more responsibility on implementation discipline.

Where LiteLLM fits best

• Teams building agent-heavy systems with LangChain

• Startups and platform teams are comfortable with code-driven control

• Environments where flexibility matters more than centralized policy enforcement

Strengths

• Simple abstraction over many LLM providers

• Tight integration with LangChain workflows

• Highly configurable routing behavior

• Low overhead and easy customization

4. Kong AI Gateway: Extending API Governance to LLM Traffic

Kong AI Gateway applies familiar API gateway controls to AI requests. It builds on Kong’s existing strengths in traffic management, authentication, and policy enforcement, extending them to LLM calls. For teams already running Kong in production, this creates a clear path to bring AI traffic under the same governance model used for APIs and microservices.

Policy enforcement is the primary capability Kong brings to AI routing. Rate limits, authentication, access controls, and request validation are applied consistently before traffic reaches model providers. This approach fits organizations that prioritize standardization and compliance across all external calls, including AI-generated ones.

Routing and observability follow established infrastructure patterns. AI requests are treated as another class of managed traffic rather than a special-case system. This keeps platform operations consistent, but it also means advanced AI-native features, such as semantic routing or model-specific optimization, are less central to the design.

Where Kong AI Gateway fits best

• Enterprises with existing Kong deployments

• Platform teams standardizing governance across APIs and AI

• Environments where policy consistency matters more than model experimentation

Strengths

• Strong authentication, rate limiting, and access control

• Familiar tooling for infrastructure and platform teams

• Clear separation between applications and external providers

5. PortKey AI Gateway: Observability-First Control for LLM Usage

PortKey centers AI gateway design around visibility and cost control. It provides a unified layer to track prompts, responses, latency, errors, and spend across multiple LLM providers. This makes it easier to understand how AI features behave in production and where usage grows unexpectedly.

Monitoring and analytics drive most routing decisions. Teams can compare providers, identify slow or failing models, and enforce usage limits before costs spike. Routing is typically guided by performance and budget signals rather than deep security policies, which keeps setup lightweight and developer-friendly.

Control is practical rather than prescriptive. PortKey focuses on making AI usage measurable and debuggable across environments. This approach works well for teams that need quick insight into behavior and spend, while relying on surrounding infrastructure for stricter security or compliance guarantees.

Where PortKey fits best

• Developer-first teams tracking LLM usage closely

• Startups optimizing cost and performance

• Products where observability is the primary concern

Strengths

• Strong dashboards for usage, latency, and errors

• Clear cost attribution across models and teams

• Fast onboarding with minimal configuration

AI Gateway Comparison and Selection Based on Risk and Control Needs

The gateways above solve different problems, even when they share surface features. The table below compares how each option behaves once AI traffic becomes persistent and business-critical.

Selection depends on which constraint hardens first. Teams handling sensitive data gravitate toward gateways that enforce confidentiality all the way down to execution, including hardware-level protections such as confidential computing and Trusted Execution Environments. Teams optimizing for iteration speed or cost tend to prioritize flexibility and visibility instead. As AI usage expands, the gateway that aligns with a team’s risk profile and execution model usually becomes long-lived infrastructure rather than a replaceable tool.

Taken together, these gateways reflect how AI infrastructure is evolving in 2026. Some teams prioritize confidentiality and strict control as AI touches sensitive systems. Others focus on speed, observability, or ease of experimentation as they iterate on products. AI gateways sit at the center of that tradeoff, shaping how models are accessed, governed, and scaled over time.

The right choice depends on what needs to stay protected and what needs to move fast. As AI workloads grow more persistent and business-critical, gateways increasingly function as long-term infrastructure rather than short-term tooling. Teams that treat them that way tend to avoid painful rewrites later.

Conclusion

AI gateways have become a core part of how production systems interact with large language models. They shape how requests are routed, what data is exposed, and how teams maintain visibility as AI usage grows across products and teams. The gateways covered here show that there is no single “best” approach; rather, there are different trade-offs among confidentiality, flexibility, and operational control.

As AI systems move deeper into business-critical workflows, gateway choices tend to last. The next step is to examine how your AI traffic behaves today and how it is likely to evolve. Teams that align gateway design with data sensitivity and long-term scale avoid costly rewrites and regain control as model usage expands.

FAQ

1. What is an AI gateway and how is it different from calling an LLM API directly?

An AI gateway sits between applications and LLM providers to control routing, security, and observability. Unlike direct LLM API calls, it centralizes model selection, retries, logging, and policy enforcement. This allows teams to switch models, apply guardrails, and monitor usage without changing application code.

2. How do enterprise AI gateways handle data privacy, encryption, and compliance?

Enterprise AI gateways reduce data exposure through encryption, execution isolation, and strict data handling controls. Some platforms, such as OLLM, use confidential computing with Trusted Execution Environments (TEEs) to ensure prompts are decrypted only inside secure hardware enclaves and are never retained after execution. This enables verifiable confidentiality and auditability without embedding sensitive data handling logic into application code.

3. How is an AI gateway different from an API gateway or service mesh?

API gateways manage HTTP traffic and authentication, while AI gateways handle LLM-specific concerns like prompt handling, model routing, token usage, and cost controls. Service meshes focus on internal service communication, whereas AI gateways manage external AI traffic to model providers.

4. What is the difference between fine-tuning a model and routing requests across multiple models?

Fine-tuning changes a model’s weights to improve task performance but increases cost and vendor lock-in. Model routing keeps models unchanged and selects the best one per request based on cost, latency, or task type. Routing enables faster iteration and greater provider flexibility.

5. How does AI observability differ from traditional application monitoring?

AI observability tracks prompts, responses, token usage, and model behavior, not just uptime and latency. Traditional monitoring shows system health, while AI observability reveals correctness, cost drift, and unexpected outputs in AI-driven workflows.