An LLM gateway is a proxy layer that sits between applications and model providers, giving one consistent API, centralized authentication, rate limiting, cost tracking and failover across multiple LLM providers instead of every application team integrating each provider's SDK separately. Tools like LiteLLM and Portkey normalize the request and response format across OpenAI, Anthropic, self-hosted vLLM endpoints and other providers, so switching or adding a model becomes a configuration change rather than a code change across every calling application. A gateway becomes worth adopting once an organization uses more than one model provider, needs centralized cost and usage tracking across teams, or wants automatic failover to a backup provider or self-hosted model when a primary provider has an outage. Additional value comes from caching repeated requests to cut cost and latency, enforcing per-team budget limits, and applying consistent guardrails such as PII redaction or content filtering in one place rather than duplicated across every application. For a team calling a single provider from a single application, a gateway adds operational overhead without much benefit, so it is not universally necessary at small scale. Nanobase AI deploys LiteLLM or Portkey as the routing layer for clients running multiple models or providers, giving centralized cost control and failover without touching application code.
The real question is where routing logic should live
Every application calling an LLM provider directly ends up reimplementing the same logic eventually: retries, fallback to a backup model, cost tracking, rate limiting. The question is not whether that logic is needed but where it should live, duplicated inside every application, or centralized in a proxy layer every application calls through instead. A gateway is not a new capability so much as a relocation of logic that already needs to exist somewhere, and centralizing it removes the drift that happens when five application teams each implement retry and fallback slightly differently.
Self-host, managed, or build your own
| Option | Setup effort | Ongoing operational load | Best fit |
|---|---|---|---|
| Self-hosted LiteLLM | Moderate, deploy and operate it yourself | You own uptime, scaling and upgrades | Teams needing full data control or air-gapped operation |
| Managed Portkey (or similar) | Low, API-key-based onboarding | Vendor owns uptime, but adds a dependency | Teams prioritizing speed over infrastructure control |
| Custom thin proxy | High, building routing, retries and tracking from scratch | You own everything, including feature parity with mature tools | Rarely justified once a mature open-source option like LiteLLM exists |
Building a custom proxy is worth it far less often than teams assume, since the interesting engineering value is in what the gateway enables downstream, cost visibility, failover, evaluation gating, not in the proxy logic itself, which mature open-source tools have already solved.
The gateway becomes a new single point of failure
Centralizing routing through a gateway also centralizes risk: if the gateway goes down, every application behind it loses LLM access simultaneously, which is a different failure profile than each application having its own independent, if inconsistent, connection to a provider. Mitigating this means running the gateway itself with redundancy, multiple replicas behind a load balancer, health checks, and a documented fallback path if the gateway layer itself becomes unavailable, the same operational rigor applied to any other critical piece of shared infrastructure.
A practical adoption path
- Start without a gateway when calling a single provider from a single application; the operational overhead is not justified yet.
- Add a gateway once a second provider or self-hosted model enters the picture, since that is the point where routing and failover logic actually starts paying for itself.
- Layer in cost tagging and budget caps as soon as more than one team's spend needs separate visibility, typically the same trigger point as tracking LLM cost per team.
- Add caching and guardrails last, once the routing and cost layers are stable, since these are optimizations on top of a working gateway rather than reasons to adopt one in the first place.
Frequently asked questions
Does a gateway add noticeable latency to every request?
A well-run gateway adds a small, generally negligible amount of latency compared to model inference time itself, though it is worth benchmarking under realistic load before assuming this is true for a specific deployment, since a misconfigured or under-provisioned gateway instance can become a bottleneck.
Can a gateway route between a self-hosted model and third-party providers seamlessly?
Yes, this is one of the main reasons to adopt one; both LiteLLM and Portkey normalize requests across a self-hosted vLLM endpoint and providers like OpenAI or Anthropic behind one consistent API, making failover between them a routing rule rather than application code.
Is a gateway a compliance requirement under regulations like the EU AI Act?
Not directly, but the centralized logging and access control a gateway makes easier to implement supports the audit and governance obligations many regulated deployments face, so it often ends up part of a compliance-driven architecture even though it is not itself a legal requirement.
How Nanobase AI helps
Nanobase AI deploys LiteLLM or Portkey as the routing layer for clients running multiple models or providers, sized and made redundant so the gateway does not become the weakest link in the stack, and connects it to the cost tracking and budget infrastructure a growing AI program needs.
Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.