AIOps observability platform helps DevOps and SRE teams proactively improve service performance with new AI-powered tools
Senser, the pioneer of zero-instrumentation production intelligence, announced the launch of their AI-powered product suite to continue supporting enterprises in the cybersecurity, financial services, and software and technology sectors effectively manage service level agreements (SLAs) and service level objectives (SLOs). The new offering extends Senser’s core technology to assist DevOps and site reliability engineering (SRE) teams by intelligently and proactively managing SLAs using SLOs and service level indicators (SLIs).
SLAs and SLOs are critical benchmarks companies use to ensure the delivery of high-quality services and maintain optimal system performance. SLAs outline the formal agreements between a service provider and its customers, while SLOs set internal performance goals for the provider to meet customer expectations, usually bounded by SLIs. For many companies, the process of managing SLAs and SLOs is fragmented, ad hoc, and reactive. DevOps and SRE leaders often report a lack of clarity on what metrics to track, creating frustration toward learning about service risks only once an SLA or SLO has been breached.
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Senser’s new SLA/SLO suite offers enterprises the following:
- Single source of truth:Â Workflows to quickly import SLAs and SLOs from other systems for a single source of truth
- Intelligent SLI generation: Automatic generation of SLIs based on service incidents, along with recommended benchmarks and alerting thresholds driven by ML-powered analysis of a customer’s environment
- Predictive monitoring and alerting: Continuous tracking of performance and machine learning (ML)-powered predictive notifications when a customer is in danger of exceeding the error budget for a given SLA or SLO – along with automated root cause analysis pinpointing exactly what’s driving the variance.
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“Many AIOps and DevOps teams heavily rely on SLAs and SLOs to run their business, but often find them to be scattered across multiple systems and reactively managed, rather than proactively monitored,” said Amir Krayden, Senser co-founder and CEO. “Following the company’s recent funding, Senser has been dedicated to creating an SLA/SLO management suite to meet the evolving needs of our customers and the landscape of increasingly complex, distributed production environments.”
Senser harnesses the power of groundbreaking extended Berkeley Packet Filter (eBPF) technology and machine learning (ML) to continuously and non-intrusively collect data on a company’s production environment; automatically create a topology of their infrastructure, network, applications, and APIs; and deliver automated insights into root cause and business impact when issues like outages or service degradations arise.
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