Eagles LLM Gateway: How to Turn Complexity into Governance for AI Use
Executive Summary
With the expansion of artificial intelligence use in applications, automations, and internal agents, Squadra began operating a growing ecosystem of APIs, models, and providers.
This growth brought challenges in visibility, security, and costs: credentials and accesses were distributed across projects, making it difficult to understand in a unified way what was being consumed, by whom, and at what cost.
In response, the Eagles LLM Gateway was developed, an enterprise layer between applications and AI providers to centralize routing, governance, security, and observability.
Eagles expanded control over consumption, budgets, accesses, and availability, with over 10 providers integrated, provider switching without code changes, and latency overhead below 25
The company
Squadra maintains an ecosystem of digital solutions that incorporates artificial intelligence into applications, automations, agents, and internal products. With the evolution of these initiatives, different projects began consuming language models and APIs from multiple providers.
The scenario
As new AI initiatives emerged, each application demanded access to specific models and credentials. This growth increased management complexity and highlighted the need for a unified view of consumption, costs, security, performance, and availability.
Given this scenario, the need arose to centralize the management of AI resources, establishing a governance structure capable of keeping pace with the expansion of these initiatives.
The Challenge
Decentralized governance
Centralize the management of projects, models, credentials, and usage policies in a single layer.
Visibility of consumption and costs
Track tokens, costs, latency, success rate, and consumption projections by project, model, key, and user.
Security and access management
Reduce exposure of provider credentials and establish corporate authentication, granular permissions, and protection policies.
Provider dependency
Reduce the risk associated with unavailability, quota limits, and dependency on a single supplier or protocol.
Scalability of integrations
Make new models and providers available without requiring specific integrations and code rework in each application.
The Solution
Squadra developed the Eagles LLM Gateway as a single entry point for enterprise AI consumption. Positioned between applications and language model providers, the gateway receives requests, applies authentication, security, and governance policies, records usage data, and directs each call to the configured backend. The architecture allows operating multiple providers under a common API contract, with routing, caching, guardrails, fallback, and observability transparently for applications.
Key deliverables
Multi-provider enterprise gateway
Single layer for connecting with different AI providers and models, compatible with applications, SDKs, CLIs, and automations.
Governance by project and key
Budgets, request limits, authorized models, and traceability by project, key, and user.
Security and access control
RBAC, groups, and profiles, MFA, corporate authentication, and guardrails for jailbreak, PII, secrets, and custom rules.
Orchestration, fallback, and cache
Routing rules between providers, fallback in configured failure or limit scenarios, and caching to avoid unnecessary reprocessing.
Observability and reports
Dashboards, audit logs, cost projections, and reports by project, provider, and model, with tracking of tokens, latency, status, and cost.
Results
Results and Impact
Over 10 providers integrated
The gateway brings together different AI providers in a common layer, expanding the options available to teams and reducing isolated integrations.
Provider switching without code changes
The abstraction created by Eagles allows switching models or providers without rework in the consuming application.
Overhead below 25 ms
The additional governance and observability layer was structured to maintain low latency impact on requests.
More control and resilience for AI
The centralization of budgets, consumption, logs, security policies, and fallback increases traceability and reduces dependency on a single supplier.
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