AI Model Gateway
Connect AI models through one managed gateway
Change providers, set selection rules and track usage without rebuilding every application.
Model list, selection rules and limits are configured for the customer's applications.
Draft a reply using the approved support knowledge base.
Every new model creates another integration to maintain
Providers use different request formats, keys, limits, prices and error rules. Applications end up carrying infrastructure logic that should be managed once.
Different interfaces
Each provider adds its own request format, model names and response details.
Scattered keys and limits
Credentials, request caps and budgets are repeated across teams and applications.
Provider changes reach the product code
Switching a route or handling an outage requires changes in every direct integration.
One access point for external and local models
Applications send one agreed request. The gateway applies the project's rule, chooses an approved route and records the result.
The route follows the task, data rule and cost limit
The example keeps the selection decision visible instead of burying it in application code.
Example, not a customer record
1. Task
Summarize an internal document. Keep the response inside the project's cost limit.
2. Selection rules
3. Decision
Change routes without rebuilding applications
Teams keep one request format while model, limit and retry policies are managed by project.
Selection rules
Choose a model by task, data policy, cost limit and availability.
Backup path
Retry or switch to an approved reserve when the primary route fails.
Usage limits
Set request and spending limits for separate projects and keys.
Usage history
Record the selected model, response time, error and cost for each request.
One managed model access layer
- A single connection point for language models
- A common request format
- Model selection based on customer rules
- A fallback route in case of failure
- Request and spend limits
- Separate keys for individual projects
- Usage and error history
- External and local models
- Works together with the AI Security Gateway
- Integration into the customer’s existing applications
See which route was used and what it cost
Project keys, request limits and a common history make model usage visible without collecting reports from every application.
Check data before routing and inspect the response
The model gateway works with the AI Security Gateway when a workflow needs data checks, masking or route restrictions.
One model access layer for teams and products
Internal assistants
Route different employee tasks to approved external or local models through one access point.
Document processing
Choose routes for extraction, classification and drafting while keeping project limits visible.
Sales and support
Give customer-facing workflows a controlled primary and backup route with a common usage history.
Analytics workflows
Keep model selection outside analytical applications and adjust the route as requirements change.
Start with the applications and models you already use
We agree one request format, selection rules and limits, then connect a bounded workflow before adding more routes.
List applications and models
Select the first workflow, required providers, local models and placement constraints.
Set routes and limits
Agree primary and backup selection, project keys, request limits and spending rules.
Connect and verify
Connect the first application, inspect route decisions and usage history, then add the next workflow.
Questions about the model gateway
Which models can we connect?
How is a model selected?
What happens if the primary model fails?
Can requests be checked before routing?
Discuss your model routes and usage rules
Tell us which applications and models you use, how routes should be selected and what limits matter. We will prepare an implementation outline for your workflow.