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Aporia Emerges from Stealth

Aporia left stealth and announced the launch of its customizable monitoring platform for Machine Learning models with full support for private and public clouds.

The company also revealed $5M in seed funding from Vertex Ventures and TLV Partners and is already being used by multi-billion dollar companies.

Aporia enables data scientists to quickly and easily build their own monitors, so they can keep track of their Machine Learning models' performance, ensure data integrity and provide responsible AI.

"AI needs guardrails", says Liran Hason, CEO of Aporia. "Companies need to have confidence in their Machine Learning models, and the only way to get there is by robust monitoring to ensure they're doing what they're supposed to do."

With Aporia, data scientists can create bespoke monitors for their Machine Learning models in just a couple of clicks, and set alerts of different severity to be sent to email or sources like Slack.

Aporia's monitors are extremely flexible, allowing data science teams to watch the right things for their own unique models and business cases.

Aporia can be installed with a few lines of code and monitors asynchronously, handling workloads of billions of daily predictions with no impact on latency. The user interface is full-featured, clean and simple, making it easy to create, maintain and modify monitors. Once Aporia's platform reveals an issue, data scientists can often quickly track down the cause of the problem and decide how to address it, whether via a logic change, a bug fix or retraining the ML model when necessary.

Concerns regarding data security and regulations make many companies apprehensive about adopting public cloud monitoring tools. Alongside its public cloud deployment, Aporia offers an innovative "managed on-prem" solution, giving peace of mind to large companies and corporations with high data privacy and security requirements.

Liran Hason, the founding CEO of Aporia, is a veteran of the IDF's elite 81 intelligence unit. He was one of the first employees of Adallom (acquired by Microsoft), where he led the ML production architecture, serving millions of users. Before starting Aporia, Hason was part of Vertex Ventures' investment team, and was involved in over 30 investments including Axonius, Spot.io and others.

"Companies are struggling to keep watch of their AI in the ways that matter for their specific Machine Learning model and use case," said Hason. "Aporia makes monitoring simple, fast and secure, bringing engineering and DevOps best-practices into the new field of MLOps and ensuring that data science teams can keep their models performing accurately and fairly."

Emanuel Timor, General Partner at Vertex Ventures says "AI adoption is soaring and requires a proper technological stack to handle the new challenges that come with it. Aporia is a vital part in the new MLOps stack, filling a critical gap in production readiness of AI."

Rona Segev, Founding Partner at TLV Partners: "Monitoring production workloads is a well-established software engineering practice, and it's past time for machine learning to be monitored at the same level. Aporia's team has strong production-engineering experience, which makes their solution stand out as simple, secure and robust."

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Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

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Aporia Emerges from Stealth

Aporia left stealth and announced the launch of its customizable monitoring platform for Machine Learning models with full support for private and public clouds.

The company also revealed $5M in seed funding from Vertex Ventures and TLV Partners and is already being used by multi-billion dollar companies.

Aporia enables data scientists to quickly and easily build their own monitors, so they can keep track of their Machine Learning models' performance, ensure data integrity and provide responsible AI.

"AI needs guardrails", says Liran Hason, CEO of Aporia. "Companies need to have confidence in their Machine Learning models, and the only way to get there is by robust monitoring to ensure they're doing what they're supposed to do."

With Aporia, data scientists can create bespoke monitors for their Machine Learning models in just a couple of clicks, and set alerts of different severity to be sent to email or sources like Slack.

Aporia's monitors are extremely flexible, allowing data science teams to watch the right things for their own unique models and business cases.

Aporia can be installed with a few lines of code and monitors asynchronously, handling workloads of billions of daily predictions with no impact on latency. The user interface is full-featured, clean and simple, making it easy to create, maintain and modify monitors. Once Aporia's platform reveals an issue, data scientists can often quickly track down the cause of the problem and decide how to address it, whether via a logic change, a bug fix or retraining the ML model when necessary.

Concerns regarding data security and regulations make many companies apprehensive about adopting public cloud monitoring tools. Alongside its public cloud deployment, Aporia offers an innovative "managed on-prem" solution, giving peace of mind to large companies and corporations with high data privacy and security requirements.

Liran Hason, the founding CEO of Aporia, is a veteran of the IDF's elite 81 intelligence unit. He was one of the first employees of Adallom (acquired by Microsoft), where he led the ML production architecture, serving millions of users. Before starting Aporia, Hason was part of Vertex Ventures' investment team, and was involved in over 30 investments including Axonius, Spot.io and others.

"Companies are struggling to keep watch of their AI in the ways that matter for their specific Machine Learning model and use case," said Hason. "Aporia makes monitoring simple, fast and secure, bringing engineering and DevOps best-practices into the new field of MLOps and ensuring that data science teams can keep their models performing accurately and fairly."

Emanuel Timor, General Partner at Vertex Ventures says "AI adoption is soaring and requires a proper technological stack to handle the new challenges that come with it. Aporia is a vital part in the new MLOps stack, filling a critical gap in production readiness of AI."

Rona Segev, Founding Partner at TLV Partners: "Monitoring production workloads is a well-established software engineering practice, and it's past time for machine learning to be monitored at the same level. Aporia's team has strong production-engineering experience, which makes their solution stand out as simple, secure and robust."

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In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...