Model Catalog and Pricing
What's in the Catalog
Apinizer AI Gateway ships with a built-in catalog of LLM providers and models, pre-loaded with default pricing — you don't have to manually enter model names or unit prices to start tracking usage and cost. The catalog spans 17+ providers and 100+ models across the major cloud and self-hosted LLM families, and grows as new provider/model combinations are added.
What the Catalog Stores per Model
Provider and model name, so a request for "gpt-4o" or "claude-3-sonnet" resolves to the right connection automatically.
Input, output, and cached-token price per 1M tokens — used to calculate the cost of every request that uses the model.
Which endpoint types a model supports (chat completions, embeddings, audio, image), so requests aren't routed to an incompatible model.
Models you add yourself — for a fine-tuned deployment or a provider not yet in the catalog — with pricing you set.
Editing Pricing
Every catalog entry is editable. If a provider changes its pricing, or you negotiate a custom rate, update the model's price and new requests immediately use the updated value — historical logs keep their original cost. Pricing is managed from AI Cost Settings.
How the Catalog Feeds Cost Tracking
Every request that reaches a provider is metered — input tokens, output tokens, and (when the provider supports it) cached tokens — and priced against the catalog entry for the model that served it. That cost flows into:
- Reports and Analytics — cost breakdowns by person, team, model, and deployment type
- Token Quotas and Rate Limiting — monthly USD budgets are evaluated against the same catalog price
- Routing and Failover — cost-aware routing and per-leg cost caps use catalog pricing to decide where a request should go
Catalog pricing is stored in USD. For an introduction to how tokens are counted and priced, see AI Fundamentals.
Price Change Propagation
Every LLM provider connection keeps its own embedded copy of the models it exposes, and that copy is what the gateway prices live traffic against. When you edit a catalog model's price and save it, those embedded copies are now refreshed automatically. Previously the copy was written once and kept its original price indefinitely, so a catalog price change never reached the connections actually serving requests.
This goes beyond reporting: the embedded price is also what USD budgets and spend limits are enforced against — see Token Quotas and Rate Limiting. A stale price therefore produces both an inaccurate cost report and a quota that cuts off at the wrong point.
Which connections are refreshed depends on who owns the catalog entry:
- A system-defined (built-in) or admin-scoped entry refreshes every matching connection in the installation.
- A project-owned entry refreshes only the connections that belong to that project.
In both cases the connection's provider type must match the catalog entry's provider type.
Only pricing is propagated — input, output, and cached-token price per 1M tokens, price per image, price per audio minute, and price per TTS character — together with the model's deprecated flag. Context window, maximum output tokens, capabilities, and modality are left untouched; they remain per-connection overrides, because an administrator may have narrowed them on purpose.
If no price actually changed, nothing is saved and no worker deployment is triggered — the same value written at a different scale (2.50 versus 2.5) does not count as a change. Enabled connections are redeployed automatically, so the gateway enforces the new price straight away. A disabled connection still has its stored price refreshed, but it is not deployed and stays disabled; it picks up the current price the next time you enable it.
Importing model definitions does not trigger this synchronization. After an import, save the catalog entry once from the UI so its price reaches the LLM connections that use the model.
Adding a Custom Model
In the Apinizer UI, go to AI Gateway → LLM Providers, then open the Model Catalog tab.
Choose the provider connection the model belongs to.
Provide the model name, the endpoint type(s) it supports, and the input/output/cached-token price (USD per 1M tokens).
Click Save. The model becomes selectable in any AI Gateway routing to that connection immediately.
Provider Type Catalog
Alongside the model pricing catalog, there is a provider type catalog. These are two different concepts — don't confuse them:
- LLM provider connection (see LLM Providers and Connections) — a configured, running connection instance with credentials.
- LLM provider definition — a template for a provider type: its default endpoint, default API version, default auth scheme, and so on. When you create a new connection, the form pre-fills from this definition; the definition itself carries no secrets.
The catalog is the union of the built-in definitions shipped with Apinizer and any project-specific custom definitions. It has its own UI surface: AI Gateway → LLM Providers → Definitions tab, where every definition can be viewed, and a custom one can also be exported or deleted.
Definitions seeded at install (OpenAI, Anthropic, Azure, and so on) can be viewed but not modified or deleted. There's no creation form on the Definitions tab — a custom provider definition is created only by importing a Provider Definition package through the Export/Import Wizard or through the APIops REST API.
The provider type catalog can also be managed through the APIops REST API; see API Reference: LLM Provider Definitions.