AI SECURITY
Govern LLM usage and spend. Every prompt, every token. Varonis Atlas puts one control point between your apps, agents, and employees and the models they use.
Trusted by 8,000+ customers
OVERVIEW
Teams call models from different providers, each with its own keys, pricing and usage reports. No one can say what AI costs by team, model, or provider until the invoice arrives. The LLM Gateway gives you one place to see it and control it.
Know which models are in use, how many tokens they consume, who's using them and for what, with alerts on spikes and trends.
Know which models are in use, how many tokens they consume, who's using them and for what, with alerts on spikes and trends.
Limit spend by token, model, provider and format for any user or group. Requests over budget are blocked before they cost anything.
Limit spend by token, model, provider and format for any user or group. Requests over budget are blocked before they cost anything.
Apps call the Atlas AI Gateway instead of the provider. The gateway holds the provider credential, so users continue working in their tools without disruption.
Apps call the Atlas AI Gateway instead of the provider. The gateway holds the provider credential, so users continue working in their tools without disruption.
Focus on the permissions and exposed data that could increase the impact of a breach.
Bring data discovery, access visibility, and activity monitoring together to support your compliance program.
DASHBOARD
The LLM Gateway dashboard shows all model usage across your organization in one place. Filter by time range, endpoint, model, provider, app or user, and track requests, tokens, total cost, latency and success rate at a glance.
When a team shares one API key, everyone draws from the same budget and no one can trace a spike to who caused it. The Atlas AI Gateway fixes that. Set limits by token, model, provider and format for any user or group, as a shared pool for a team, an allowance for each user or a cap for one person.
USER EXPERIENCE
Governance runs in the request path, so nothing changes for users. They open the same chat app, IDE or copilot, ask a question and get an answer. Behind the scenes, the gateway checks identity, budgets and prompt policy, injects the provider credential, calls the model and checks the response on the way back.
FAQs
It sits between your AI apps, agents, copilots and IDEs and the model providers they call. Every request goes through 1 governed endpoint, so you can see and control model access, token usage and spend.
More than 95 providers and 3,000 models, including OpenAI, Anthropic, Amazon Bedrock, Azure AI Foundry and Databricks.
No. Requests are routed through the gateway. The gateway holds and injects the provider credentials.
Set limits as a shared pool, per user or for a single user, each with its own budget and daily, weekly or monthly reset. Per-user and per-group limits use identity from your IdP, so no one spends against someone else's budget. When a limit is exhausted, the request is blocked before it reaches the provider.
Yes. Cost attribution breaks spend down by endpoint, user and model, and you can drill from any spike to the exact requests behind it.
Yes. It's part of the Atlas AI Gateway, so Runtime Guardrails check each prompt before it leaves and each response before it returns, and can alert, modify, block or audit.
The LLM Gateway is part of the Atlas AI Gateway, alongside Runtime Guardrails and the MCP Gateway. Every request is available in AI Investigation and can be sent to your SIEM and SOAR tools.
