Definition
Model Gateway
A model gateway is a unified layer that sits between an application and one or more AI model providers, routing requests through a single interface. It centralizes provider abstraction, authentication, rate limiting, cost tracking, caching, and failover — so an app can swap models, enforce spending budgets, and observe usage without rewiring every call site.
Key takeaways
- A model gateway is a unified layer between an application and one or more AI providers, routing every request through a single interface.
- It centralizes provider abstraction, auth, rate limiting, cost tracking, caching, and failover, so swapping models is a config change.
- For multi-tenant products it can gate and meter each workspace's AI spend against its own budget, preventing one tenant from exhausting another's.
- It is the control plane for AI consumption — the single chokepoint where usage is observed, capped, and governed.
Calling AI providers directly from scattered places in an application scales badly: each provider has its own SDK and quirks, credentials spread everywhere, and there is no central view of cost or usage. A model gateway solves this by becoming the single chokepoint every model request passes through. The application talks to one consistent interface, and the gateway handles which provider and model actually serve each call.
Centralizing the path unlocks the controls that matter in production. The gateway can meter and cap spend per team or tenant, enforce rate limits, cache repeated requests, log every call for observability, and fail over to an alternate provider when one is down or rate-limited. Switching or A/B-testing models becomes a configuration change rather than a code change across the codebase.
For multi-tenant products the budget and isolation aspects are especially valuable: a gateway can gate each workspace's AI usage against its own allowance and record spend per tenant, so one customer's heavy use cannot run up another's bill or exhaust a shared quota. It is the control plane for AI consumption.
Planoda routes every model call through an AI gateway with per-workspace budget gating, so spend is metered and capped per tenant and the underlying model can change without touching feature code.
Related terms
- Large Language Model (LLM)A large language model is an AI system trained on vast amounts of text to predict and generate language, enabling it to answer questions, summarize, write, and reason over natural language. LLMs power modern AI assistants and agents. They are probabilistic next-token predictors, which makes them remarkably capable but also prone to confident errors.
- InferenceInference is the act of running a trained model to produce output from new input — the moment a model is actually used, as opposed to trained. Every prompt sent to a language model triggers an inference pass. Inference dominates the running cost and latency of AI features, since it happens on every request while training happens once.
- TokenA token is the unit a language model reads and generates — typically a word fragment of a few characters rather than a whole word. Text is split into tokens before processing, and models bill, limit, and reason in tokens, not characters. As a rough guide, one token is about four characters or three-quarters of a word in English.
- Multi-TenancyMulti-tenancy is an architecture where one running application and database serve many independent customers (tenants), with each tenant's data strictly isolated from the others. It lets a SaaS product share infrastructure for efficiency while guaranteeing that one workspace can never see another's data — a guarantee enforced in the data layer, not left to hope.
- GuardrailsGuardrails are the controls that constrain what an AI system is allowed to do or say, enforced around the model rather than left to the model's judgment. They validate inputs and outputs, block unsafe or off-policy actions, scope which tools an agent may use, and route risky operations through approval — turning a probabilistic model into a system safe to deploy.