Cribl has introduced StreamAI, an enterprise AI gateway with a model router integrated into its open data platform. StreamAI is designed to give organizations greater visibility and control over AI traffic, token consumption, costs, and risk by directing prompts and workloads to appropriate AI models based on performance and cost. Cribl is also offering free inference for customers using automatic data routing to benchmarked AI models, helping reduce unpredictable token expenses.
As more enterprises ramp AI use in daily operations, spending on AI inference is becoming harder to predict and control. Infrastructure costs can spike with little warning, particularly when agents are coordinating multi-step tasks with minimal human intervention. StreamAI delivers real-time cost control by providing reliable, high-performance inference across proprietary and open-source models while enforcing budgets and spending cutoffs.
“AI is not one-size-fits-all. The right model depends on the job, the context, and the economics of the request,” said Clint Sharp, co-founder and CEO of Cribl. “Customers shouldn’t have to send every prompt to the most expensive model or build their AI strategy around a single provider. StreamAI gives them an intelligent control plane that routes each request to the model best suited to the work, helping reduce token costs while preserving the choice and flexibility to use the models they want.”
StreamAI’s model router builds on Cribl’s SecIT Bench research, which evaluated 20 AI models across 30 real-world IT and security investigations. It gave AI models the same messy incident data such as a security breach, service outage, or performance problem. Initial results revealed a striking insight: a 17% spread in diagnostic accuracy compared with a 20x range in investigation spend, proving that the most expensive model is not automatically the best fit for every workflow.
When an AI-powered enterprise application reaches a cost limit, StreamAI automatically falls back to an alternative model to keep usage running without disruption. Built-in circuit breakers give IT teams control over token consumption, helping prevent applications from blowing through quotas and costs.
To ensure enterprise readiness, StreamAI incorporates critical security and compliance controls directly into the routing layer. It builds on Cribl-Privacy, the custom telemetry-focused model behind Cribl Guard’s background detection, which is designed for the high-volume, semi-structured data found in production telemetry. StreamAI applies security controls throughout every AI interaction, helping protect sensitive data, block malicious requests, control what models can access, and reduce the risk of unsafe responses or actions.
It also provides bidirectional sensitive data redaction, shielding proprietary information, credentials, and sensitive data in both outgoing prompts and incoming responses. Additionally, it automatically records every model call and routing decision as normalized, audit-ready telemetry, giving organizations complete visibility and evidence for governance across all routed AI traffic.
StreamAI is designed to help organizations adopt AI without locking their observability strategy to one model, provider, or closed platform.
For more information, Visit: https://cribl.io/
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