UsageGuard

UsageGuard puts one API in front of OpenAI, Anthropic, Meta Llama and Google Gemini, among others. Your application calls the UsageGuard endpoint, and the platform routes the request to whichever provider and model you configured. Switching models, or running several side by side for comparison, becomes a settings change rather than a rewrite. The platform supports real-time streaming, so responses still arrive token by token, and it handles the format differences between providers so the calling code stays the same.

The observability side gives you a single view of what your AI systems are doing. It monitors performance, logs requests and traces traffic, and it tracks usage patterns so you can see which features and which teams drive load. For engineering teams, that means prompts and responses are inspectable for debugging, and for managers, latency and error data sit next to the cost figures. The goal is to make an LLM feature as measurable as any other service in the stack.

Cost control is built into the gateway rather than bolted on afterward. The platform tracks usage and lets you set budgets, so an expensive model or a runaway loop hits a limit you set instead of a surprise invoice. Because every call passes through the same endpoint, LLM cost control is accurate by design: spend can be attributed by connection, project or environment, which is the granularity finance usually asks for. The company cites cost reduction as a headline outcome, and the mechanism is simply visibility plus enforced ceilings.

The security layer sits in the same request path as the model call. It scans prompts for injection attempts and other malicious input before the request reaches the provider. It also filters content and guards PII so sensitive data does not leave your boundary unchecked. That is prompt injection protection and AI security governance in one place rather than two bolted-on tools. Policies are set per connection, so you can run different rules for dev, staging and prod, or for different teams, from one control plane. Compliance-oriented features round this out for regulated buyers.

UsageGuard lets you create many connections, each with its own API key, security policies, usage limits and configuration. That is how a single organization separates projects, teams or environments without standing up separate installations. A development connection can run loose limits and test policies while a production connection enforces the strict ones, and the analytics can still be read together. For platform teams, this is the feature that turns a gateway into a governance tool.

The platform supports several deployment models. It runs as a managed service on AWS across US and European regions, which suits teams that want the compliance and data residency of a major cloud. It also offers private cloud and on-premise options. These cover air-gapped deployments with custom security policies for organizations that need data isolation and full control over their infrastructure. The same product runs in each case, so the choice is about where the data lives rather than which features you get.

Because the gateway abstracts the providers, your integration does not hard-code one vendor. The documented setup is close to a drop-in: update your API endpoint to point at UsageGuard and add your UsageGuard API key and connection ID. That keeps the door open to newer or cheaper models without a migration project. It also means an app can use a strong model for one task and a cheaper one for another, with the routing handled centrally rather than in each service.