LiteLLM is an open source proxy and SDK that translates requests to over one hundred LLM providers, including OpenAI, Anthropic, self-hosted vLLM, and NVIDIA NIM, into a single consistent OpenAI-compatible format, adding centralized API key management, per-team rate limiting, budget tracking, load balancing across providers, and fallback routing if one endpoint fails. Whether you need an LLM gateway depends on scale and governance needs rather than raw traffic volume: a single team calling one model directly may not need one, but any organization with multiple teams, multiple models, or a mix of self-hosted and third-party APIs benefits from centralizing authentication, cost visibility, and rate limits in one place instead of duplicating that logic in every application. Gateways also make model migrations and A/B testing between engines or providers far easier, since application code talks to the gateway rather than to any specific backend. The tradeoff is an added network hop and another piece of infrastructure to operate and secure, so smaller single-model deployments can reasonably defer this until usage or team count grows. Nanobase AI, an NVIDIA Inception program member, sets up LiteLLM or equivalent gateway layers as part of larger enterprise LLM platform builds.
What a gateway sits between, exactly
An LLM gateway sits between your application code and every model backend it talks to, whether that's OpenAI, Anthropic, or a self-hosted vLLM or NIM endpoint, presenting one consistent interface outward while handling authentication, routing, and policy enforcement inward. LiteLLM is the most widely adopted open source implementation of this pattern, but it is a pattern, not a single tool, and several alternatives implement it with different tradeoffs.
The architectural value is decoupling application code from any specific provider's API details, so a model or provider change becomes a gateway configuration change instead of an application code deployment.
LiteLLM and its alternatives, compared
| Tool | Deployment model | Notable strength |
|---|---|---|
| LiteLLM | Self-hosted proxy or embedded SDK, open source | Broadest provider coverage, active community, straightforward self-hosted setup |
| Kong AI Gateway | Plugin on the established Kong API gateway | Fits organizations already standardized on Kong for general API management |
| Envoy AI Gateway | Built on the Envoy proxy, open source | Fits organizations already running Envoy as core infrastructure |
| Portkey | Hosted and self-hosted options, commercial | Strong built-in observability and caching features out of the box |
| Custom-built internal gateway | Fully bespoke | Full control, but ongoing engineering ownership of a solved problem |
Most of these implement the same core capability set: unified API format, key management, rate limiting, and routing; the differentiator is usually how well each fits an organization's existing infrastructure stack rather than a fundamental capability gap between them.
What a gateway actually centralizes
- Authentication: One place to issue, rotate, and revoke API keys per team or application, instead of credentials scattered across codebases.
- Cost and usage tracking: Per-team or per-application budget visibility across every provider and model, which is otherwise assembled manually from separate billing dashboards.
- Rate limiting: Enforced centrally rather than duplicated in every application that calls a model.
- Routing and fallback: Automatic failover to a backup provider or model if a primary endpoint errors or degrades, without application-level retry logic for every caller.
- A/B testing and gradual rollout: Routing a percentage of traffic to a new model or provider without redeploying calling applications.
A checklist for deciding whether you need one
| Situation | Gateway generally justified |
|---|---|
| Single team, single model, direct integration | Often not yet needed |
| Multiple teams calling different models independently | Yes, for centralized cost and key visibility |
| Mix of self-hosted and third-party provider APIs | Yes, to unify the interface application code depends on |
| Active migration between providers or engines | Yes, to make cutover a routing change, not a code change |
| Compliance requirement for centralized audit logging of model calls | Yes, almost always |
| Early-stage single-model prototype | Usually not yet, added infrastructure outweighs the benefit at this stage |
The tradeoff most discussions skip
A gateway adds a network hop and a new piece of infrastructure that needs its own uptime, monitoring, and security posture, since every model call now depends on the gateway being healthy in addition to the model backend itself. For a small, single-model deployment, this overhead can outweigh the benefit, and deferring the decision until team count or provider diversity actually grows is a reasonable default rather than adopting a gateway preemptively. Once that trigger point is reached, gateways are also the natural place to route traffic during a provider migration, as described in replacing the OpenAI API with a self-hosted model.
Frequently asked questions
Does adding a gateway meaningfully increase latency?
A well-deployed gateway, run close to the calling application and the model backends, adds a small, generally low-single-digit-millisecond overhead per request, which is typically negligible compared to model inference time itself, though it should still be measured in your own environment.
Can LiteLLM route to a self-hosted vLLM deployment alongside OpenAI and Anthropic?
Yes, this is one of its core use cases: presenting self-hosted and third-party model endpoints behind the same OpenAI-compatible interface, which is exactly what enables gradual migration and multi-provider fallback strategies.
Is a commercial gateway like Portkey worth it over self-hosting LiteLLM?
It depends on whether the built-in observability, caching, and support a commercial option provides outweighs the operational cost of self-hosting and maintaining an open source alternative; teams without spare platform engineering capacity often lean toward the commercial option for that reason.
Should a gateway be introduced before or after choosing a self-hosted serving engine?
Either order can work, but introducing the gateway first makes the subsequent engine choice, and any later engine change, a lower-risk routing decision rather than an application-wide code change, which is a meaningful advantage during active platform evolution.
How Nanobase AI helps
Nanobase AI, an NVIDIA Inception Program member, sets up LiteLLM and equivalent gateway layers as part of larger enterprise LLM platform builds, centralizing authentication, cost tracking, and routing across self-hosted and third-party model backends for organizations running more than one team or model.
Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.