Real-world implementation · Enterprise · IT
Enterprise AI Gateway for Model Routing and Cost Governance
BrainShift has announced BrainShift AI Gateway as a central control layer for enterprise AI workloads. This use case describes how IT could apply its published capabilities to govern model access, protect shared capacity, and make consumption visible to finance and security teams. It is an implementation pattern, not a claim of customer deployment.
Business challenge
Departments often connect applications to model providers independently. IT must reconcile separate credentials, access rules, and consumption records while finance struggles to explain which teams and applications drive AI spending.
Shared inference resources introduce competing demands. A document-processing batch can contend with an employee-facing assistant, while sensitive requests need routing rules that distinguish approved public services from private or on-premises models.
How BrainShift solves it
BrainShift Gateway provides a single governed entry point for participating AI applications. Administrators can define rate limits, token quotas, and budgets by team, user, application, or model, with priority and fair-use policies to manage competing workloads.
Requests can be routed across public providers, private models, and on-premises clusters according to policy, cost, latency, and data sensitivity. Automatic fallback and load balancing support availability, while request-level usage, cost, and latency records give IT, security, and finance a shared operational view.
How it works
Step-by-step workflow
- 01
1. Inventory AI traffic
Identify applications, model endpoints, credentials, workload owners, and data classifications before deciding which traffic must pass through the gateway.
- 02
2. Establish access policies
Map users and applications to approved models. Configure access controls, API key management, masking rules, and content policies.
- 03
3. Allocate consumption
Set team and application budgets, token quotas, and rate limits. Define priority workloads and fair-use rules for shared capacity.
- 04
4. Configure routing
Select eligible endpoints by sensitivity, cost, and latency. Review fallback paths so alternatives remain within the same approved data boundaries.
- 05
5. Validate before rollout
Test representative requests, quota enforcement, masking, and provider failures. Obtain IT and security approval before redirecting production traffic.
- 06
6. Review and adjust
Monitor usage, cost, and latency dashboards. Review exceptions with workload owners and approve material budget or routing changes.
In practice
Example requests
"Which applications are driving our AI spending?"
The system provides usage and cost records for review by application and owner.
"Keep confidential document requests on private models."
Administrators configure sensitivity-based routing and review eligible fallback endpoints before activation.
"Protect our service assistant during batch-processing peaks."
Administrators assign priority policies and constrain competing workloads with quotas and rate limits.
BrainShift components
What each module does
BrainShift Gateway
Centralizes enterprise AI access, policy-based routing, workload limits, consumption monitoring, and auditable model usage.
Enterprise integrations
Systems it can connect to
Configurable integration options, confirmed during implementation.
- Identity providers: Configurable identity integration for enterprise authentication and access-policy alignment.
- REST APIs: Connect approved AI applications and model endpoints through a governed interface.
- Microsoft 365: Route model requests from configured productivity assistants through gateway policies.
- Document management: Apply gateway controls to model calls from configured document-processing applications.
Data sources
- AI request and response metadata
- Model usage, cost, and latency records
- Team, user, and application ownership mappings
- Approved model endpoint and deployment inventories
- Data classifications, routing policies, and budget allocations
Business outcomes
Potential benefits
Clearer spending accountability
Usage records could help finance attribute consumption to responsible teams and applications.
More predictable consumption
Quotas and budgets could constrain unexpected demand before it dominates shared resources.
Better workload continuity
Approved fallback paths could reduce disruption when individual endpoints become unavailable.
Consistent model access
Central policies could reduce differences in how applications enforce model permissions.
Stronger governance evidence
Request-level records could support investigations, policy reviews, and internal audit preparation.
Suggested KPIs
Security and deployment
Published deployment options include on-premises, private cloud, and sovereign environments on Kubernetes or OpenShift. An implementation should configure SSO through the selected identity provider, role-based access, API key controls, data masking, and audit trails. Review request-log content, retention, and endpoint eligibility so monitoring does not create unintended exposure.
Human oversight
IT owns routing and capacity policies; security approves sensitive-data boundaries; finance reviews budget allocations. High-impact changes, including new external providers, expanded permissions, and material budget increases, require human review and approval. Routine requests operate within approved policies.
FAQ
Frequently asked questions
Can the gateway combine public and private models?
Yes. Published routing capabilities cover public providers, private models, and on-premises inference clusters.
Can finance review consumption by department?
Teams can use usage and cost records mapped to team and application ownership.
Does fallback bypass data restrictions?
Fallback policies should permit only endpoints approved for the request's data classification.
Is this a documented customer deployment?
No. The capabilities come from published BrainShift announcements; this page describes an implementation pattern.
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