StreamAI: Secure telemetry data with Cribl's AI platform

Cribl, recognised for its AI Platform for Telemetry, has unveiled StreamAI, an enterprise AI gateway featuring a model router integrated into its open data platform. The new tool aims to effectively manage vast volumes of data, offering organisations enhanced control over AI traffic, usage tokens, and associated costs and risks.

Following an assessment of AI model performance on telemetry data, Cribl developed a model router to optimise AI inference processes. StreamAI channelises AI prompts and workloads to the appropriate model, ensuring cost-effectiveness while enhancing visibility and governance over AI operations. Furthermore, Cribl provides free inference for clients utilising its automatic data routing to verified AI models, thereby avoiding unexpected charges.

Optimising predictions and costs

With rising AI integration in business operations, predicting and managing expenditures on AI inference poses increasing challenges. Costs of infrastructure can surge unexpectedly, particularly when agents execute complex tasks with minimal human oversight. StreamAI offers real-time financial management by ensuring reliable AI inferencing across various proprietary and open-source models, setting budget caps and spending limits.

StreamAI offers real-time financial management by ensuring reliable AI inferencing

Clint Sharp, co-founder and CEO of Cribl, commented on the nuances of AI usage: “AI is not one-size-fits-all. The right model depends on the job, the context, and the economics of the request. 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.”

Model evaluation and financial efficiency

The model router utilised within StreamAI is based on Cribl’s SecIT Bench research, which analysed 20 AI models across 30 real-world IT and security scenarios. These scenarios included issues like security breaches and service outages.

The study highlighted a 17% gap in diagnostic accuracy against a 20-fold difference in investigative expenditure, suggesting that higher cost models are not invariably optimal for all tasks. In instances where AI-driven applications hit financial barriers, StreamAI automatically selects an alternate model to maintain functionality. With integrated circuit breakers, IT teams can control token usage and avoid exceeding budgetary limits.

Enhancing Security and Compliance

StreamAI is crafted to guide organisations in embracing AI without being tied to a singular model

StreamAI is engineered for enterprise deployment, embedding essential security and compliance measures within the routing process. It extends Cribl-Privacy's capabilities, the telemetry-focused model behind Cribl Guard’s background detection, attuned for high-volume, semi-structured data frequently encountered in telemetry. Security controls apply to every AI interaction, safeguarding sensitive data, thwarting malicious inputs, and regulating model access to prevent hazardous outputs.

StreamAI facilitates bidirectional redaction of sensitive data, protecting proprietary information, credentials, and sensitive content in outgoing prompts as well as responses. It also systematically records each model invocation and routing option as standardised, audit-ready telemetry, assuring complete transparency and evidential governance for all AI interactions.

StreamAI is crafted to guide organisations in embracing AI without being tied to a singular model, provider, or proprietary platform. The platform is set to be available shortly, with opportunities for early access available through Cribl account representatives.

Show full press release

Cribl, the AI Platform for Telemetry, announces StreamAI, the latest addition to its AI capabilities: an enterprise AI gateway with a model router built into Cribl’s open data platform. Cribl brings its proven ability in managing and routing data at a massive scale to help organisations control runaway AI traffic, token usage, costs, and risk.

After evaluating how various AI models performed on telemetry data, Cribl built a model router to enable more efficient AI inference. StreamAI routes AI prompts and workloads to the right model at the right price while gaining visibility and control over AI. Cribl is offering free inference for customers using automatic data routing to benchmarked AI models, eliminating surprise token bills.

High-performance inference

As more enterprises ramp up 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.”

Most expensive model

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.

Shielding proprietary information

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 normalised, audit-ready telemetry, giving organisations complete visibility and evidence for governance across all routed AI traffic.

StreamAI is designed to help organisations adopt AI without locking their observability strategy to one model, provider, or closed platform. StreamAI will be available soon. Customers interested in early access can contact their Cribl account team.

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