Most teams begin by connecting their applications directly to an LLM provider.
The application sends a request to the provider's API, receives a response, and uses it within the product. For an initial AI feature, this approach is usually straightforward.
The architecture changes as the application adopts additional models or providers. Each integration may have its own endpoint, authentication method, request format, response structure, and operational requirements. Provider credentials may also end up distributed across several services.
An LLM proxy introduces an intermediary between the application and those providers. Instead of maintaining direct connections from every service to every model provider, the application sends requests to the proxy. The proxy handles the provider-facing communication and returns the result.
This arrangement is related to an AI gateway and an LLM router, although the terms describe different responsibilities. A proxy primarily manages communication. A router determines where a request should go. A gateway may combine both functions with controls for resilience, governance, rate limiting, and observability.
This guide explains how LLM proxies work, what they can help with, where their limits lie, and when a broader AI gateway may be more appropriate.
What Is an LLM Proxy?
An LLM proxy is a service positioned between an application and one or more large language model providers.
Without a proxy, an application might send requests directly to OpenAI, Anthropic, or another provider. With a proxy, the application sends the request to the proxy endpoint. The proxy authenticates with the upstream provider, forwards the request, receives the response, and passes it back to the application.
The request path looks like this:
Application → LLM Proxy → Model Provider → LLM Proxy → Application
The proxy becomes the application-facing integration point. It can hide provider credentials, translate request formats, normalize responses, and collect information about traffic, depending on how it is configured.
Some proxies provide a common interface across several providers. Others focus on forwarding requests to a single provider while adding authentication, logging, or access controls.
The term therefore describes an architectural role rather than a fixed feature set. A lightweight proxy may only relay requests, while a more advanced product may include capabilities commonly associated with an AI gateway.
How an LLM Proxy Works
An application that connects directly to a provider typically contains the provider endpoint, credentials, model identifier, and request format.
The direct request path is:
Application
↓
Provider API
↓
Model Response
When the application uses a proxy, the provider-specific connection moves into the intermediary layer:
Application
↓
LLM Proxy
↓
Provider API
↓
LLM Proxy
↓
Application
The proxy handles each stage of that exchange.
Receiving the application request
The application sends the model request to the proxy endpoint. Depending on the implementation, the request may follow a standardized format that resembles the APIs of several providers.
The request can include the model name, messages, generation parameters, tool definitions, streaming preferences, and application metadata.
The proxy may authenticate the calling application before accepting the request. It can also validate required fields and reject malformed or unauthorized traffic before contacting the provider.
Preparing the upstream request
The proxy prepares the request for the selected provider.
In a simple setup, this may involve adding the provider's API key and forwarding the payload with minimal changes. In a multi-provider setup, the proxy may translate field names, adjust message formats, or convert the request into the provider's expected schema.
It can also attach metadata for logging, usage tracking, or policy evaluation.
The amount of transformation depends on the proxy. Applications should verify how the layer handles provider-specific features such as streaming, tool calls, structured outputs, and multimodal inputs.
Calling the model provider
The proxy sends the prepared request to the upstream provider.
The provider processes the request and returns a response, which may contain generated text, tool calls, usage information, or an error.
The proxy can record the provider response and timing information before returning the result to the application.
Returning the response
The proxy passes the response back through the application-facing interface.
When several providers are connected, the proxy may normalize response fields so the application can use a consistent structure. Normalization can simplify integration, although it may not eliminate every difference in model behavior or feature support.
The proxy also remains part of the application's request path. Its availability, latency, and capacity can therefore affect the product.
What an LLM Proxy Does Well
Centralizing provider access
A proxy gives multiple application services a shared route to model providers.
Without one, each service may maintain its own provider endpoint, credentials, client library, logging, and error handling. The proxy moves those provider-facing concerns into a central service.
This arrangement can be useful when several teams or products use the same models. Provider configuration can be managed in one place instead of being repeated across application codebases.
Protecting provider credentials
Direct integrations often require provider credentials to be available wherever model requests are made.
With a proxy, application services can authenticate with the proxy while the proxy manages credentials for upstream providers. This reduces the number of services that need access to those credentials and makes rotation easier to coordinate.
The proxy does not replace broader security practices. It still needs access controls, secure secret storage, and monitoring for unauthorized use.
Providing a common request interface
Providers differ in their APIs and supported features. A proxy can expose a consistent interface to applications while handling provider-specific request construction behind the scenes.
This can reduce duplicated integration work when an application uses several providers. It also gives teams a defined location for managing changes to provider endpoints or client libraries.
The abstraction has limits. A common interface may not represent every provider capability, and applications may still need provider-specific handling for advanced features.
Collecting request information
Because requests pass through the proxy, it can record operational data such as request counts, response times, errors, selected models, and usage information.
This data can help teams understand traffic and identify provider-related failures.
A proxy's logs do not necessarily provide complete visibility into an AI workflow. Agent steps, retrieval operations, tool calls, retries, and output quality may require tracing at the application or gateway level.
Reducing direct provider dependencies
When the application communicates with a proxy rather than a provider endpoint, provider-facing changes can often be handled without modifying every application service.
The proxy can update credentials, endpoints, request translation, or provider configuration centrally.
This does not make provider changes invisible. Models may differ in output quality, context limits, tool behavior, latency, and pricing. The proxy reduces integration work, but teams still need to evaluate application behavior after a provider change.
What an LLM Proxy Does Not Do by Default
A proxy can centralize communication, but it does not automatically make decisions about every request or provide every production control.
It does not automatically choose the right model
A proxy may expose several models without deciding which one should handle a particular request.
Model selection requires routing logic. That logic may consider the task, expected quality, latency target, cost limit, context size, or provider availability.
A proxy forwards a request according to its configuration. A router evaluates the request and selects a destination.
It does not guarantee failover
If the configured provider is unavailable, a basic proxy may return the provider's error to the application.
Failover requires additional behavior, such as health checks, retry policies, fallback providers, alternate models, and rules for deciding when to switch destinations. Those controls need to be configured and tested rather than assumed to exist because a proxy is present.
It does not reduce costs on its own
A proxy can centralize usage data, but centralization does not change the cost of the models being called.
Cost management depends on decisions such as selecting a suitable model for each task, limiting unnecessary output, caching repeat requests, and controlling token consumption. A proxy can support those policies, but it does not create them automatically.
It does not establish complete governance
A proxy can enforce authentication and basic access rules. Broader governance may require controls for data handling, model permissions, audit records, rate limits, retention, and policy violations.
Those controls depend on the proxy's features and configuration. Sending traffic through a central endpoint is not, by itself, a governance strategy.
It does not provide a complete view of an AI workflow
A proxy can show that a model request occurred, which provider handled it, and how long the call took.
That information may be insufficient for applications that use agents, retrieval, tools, retries, or several model calls in one user interaction. Understanding the final result requires tracing the full execution across those components.
LLM Proxy vs LLM Gateway vs LLM Router
These terms overlap because products may combine several functions. Their primary responsibilities remain different.
An LLM proxy manages communication between applications and providers.
An LLM router selects the model or provider that should receive a request.
An AI gateway provides a broader management layer that may include proxying and routing alongside operational and governance controls.
LLM proxy
The proxy's central role is to receive application requests and communicate with upstream model providers.
It may handle authentication, request translation, response normalization, logging, and provider configuration. Its main abstraction separates application code from provider connections.
LLM router
A router evaluates a request and chooses a destination.
For example, it may send a classification task to a lower-cost model and reserve a more capable model for complex reasoning. Routing rules can also account for latency, provider health, geographic requirements, context limits, or quality targets.
The router's defining function is destination selection.
AI gateway
An AI gateway manages AI traffic across applications, models, and providers.
It may include proxying and routing, along with rate limiting, failover, policy enforcement, usage controls, observability, and governance. The exact scope varies by product, so teams should assess the capabilities available in the implementation rather than rely on the label alone.
When an LLM Proxy Is Enough
A proxy may be appropriate when the application has a stable model configuration and the main requirement is centralized provider access.
For example, a team may use one provider, have predictable traffic, and already operate separate systems for monitoring, authentication, rate limiting, and incident handling. In that environment, a proxy can provide a shared integration point without adding routing or policy logic that the application does not need.
It can also suit teams that want a common interface for several providers while keeping model selection decisions inside the application or another existing service.
The key consideration is whether the intermediary only needs to manage connections or must also make decisions about traffic.
When You Need More Than a Proxy
A broader gateway becomes useful when AI traffic requires centralized decisions or controls.
You use different models for different workloads
Applications that handle several task types may need different models for quality, speed, or cost reasons.
If each service contains its own model-selection logic, changes become harder to coordinate. A routing layer can centralize those rules and apply them consistently.
Provider failures affect user-facing features
A provider outage, rate limit, or latency increase can interrupt the application when there is only one configured path.
A gateway with health checks and fallback policies can redirect eligible requests when the primary route is unavailable. The fallback behavior should be defined according to the task because not every model is an equivalent replacement.
Model spending requires active control
Usage data is useful when teams can connect it to operational decisions.
A gateway can apply policies for model selection, token limits, caching, and request allocation. These controls help teams manage spending as traffic grows without placing every cost-related decision inside application code.
Policies must apply across applications
Organizations with several AI applications may need consistent rules for model access, sensitive data, usage limits, and auditability.
Applying those rules separately in each service can lead to gaps and inconsistent enforcement. A centralized gateway can provide a shared policy layer.
Workflows include several AI operations
Agents, retrieval systems, and tool-using applications may generate multiple model calls for one user request.
At that point, teams need to understand the relationship between those calls, the tools they invoked, and the final result. A gateway can contribute centralized traffic controls, while end-to-end tracing may require integration with application observability systems.
Open Source and Managed LLM Proxy Options
Teams can run proxy infrastructure themselves or use a managed service.
Open source options
Self-hosted proxies provide control over deployment, networking, data handling, and customization.
They may be suitable when the proxy must run in a particular environment or integrate with internal infrastructure. The team remains responsible for availability, scaling, upgrades, security, secret management, and incident response.
The operational workload should be considered alongside the software's features. Running a proxy reliably requires more than deploying its initial configuration.
Managed options
A managed service handles the infrastructure required to operate the proxy.
Teams can focus on application integration, provider configuration, and traffic policies while the service manages deployment and scaling. Some managed platforms also include routing, failover, governance, and observability.
The decision depends on the required capabilities and the level of operational ownership the organization wants. Product categories can be misleading, so evaluation should focus on supported features, deployment requirements, data handling, and the controls available for production traffic.
How to Set Up an LLM Proxy
The setup process varies by product, but the main steps are similar.
First, identify the applications and providers that should use the proxy. Configure the upstream provider connections, store their credentials securely, and define how application services will authenticate with the proxy.
Next, update the application endpoint so requests are sent to the proxy. Confirm that the proxy supports the request features the application uses, including streaming, tool calls, structured outputs, multimodal content, and usage reporting.
Test both successful and unsuccessful requests. Check response formats, error handling, timeouts, retries, and provider-specific behavior. A request can reach the provider successfully while still producing application issues if the proxy changes streaming or response semantics.
The proxy should also be monitored as part of the production request path. Track its availability, latency, capacity, upstream errors, and resource usage. If it connects to several providers, test provider outages, rate limits, and fallback behavior before relying on the configuration in production.
Beyond a Proxy: Orq.ai's AI Gateway
A proxy can centralize model access. Orq.ai's AI Gateway extends that layer with intelligent routing, token-aware rate limiting, failover, governance, and observability across 400+ models.
This allows teams to manage provider decisions and production controls centrally instead of distributing them across application services.
Intelligent model routing
Orq.ai's AI Gateway can route requests across models and providers according to workload requirements.
Teams can define policies that account for cost, performance, availability, and other operational criteria. Model-selection logic can therefore remain in the gateway rather than being repeated throughout application code.
Failover and resilience
Provider failures can affect applications even when the application code is functioning correctly.
The gateway can support fallback behavior for situations such as provider outages, rate limits, or unacceptable latency. Teams can define how requests should be redirected and which alternatives are appropriate for each workload.
Token-aware rate limiting
Request counts do not fully represent the resource demands of AI traffic. Two requests can consume very different numbers of input and output tokens.
Token-aware rate limiting allows teams to manage usage according to token consumption. This provides a closer relationship between traffic controls and the resources used by model requests.
Governance across models
A multi-provider environment can make access and policy management difficult to maintain.
A centralized gateway gives teams a shared location for managing model permissions, usage policies, and other controls across applications and providers.
Observability
Gateway-level data can show model usage, latency, costs, errors, and routing decisions.
Orq.ai connects this information with observability capabilities so teams can investigate how requests move through the AI stack. Application-level tracing may still be needed for workflows that include retrieval, tools, or multiple model calls, but the gateway provides a consistent view of provider traffic.
Conclusion
An LLM proxy separates application code from direct provider connections.
It can centralize credentials, standardize integrations, collect request data, and reduce the work involved in managing several provider endpoints. Those capabilities are often sufficient for applications with stable traffic and limited routing requirements.
As the system grows, teams may need the infrastructure to select models, respond to provider failures, control token usage, enforce policies, and explain multi-step AI workflows. Those responsibilities require capabilities beyond basic request forwarding.
The right choice depends on the problem the architecture needs to solve. A proxy addresses provider connectivity. An AI gateway adds centralized control over how production AI traffic is routed, governed, and observed.
