Building the Next Generation of AI Infrastructure with Hydrolix

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Building the Next Generation of AI Infrastructure with Hydrolix
Learn how Hydrolix is building the next generation of AI tools and providing AI-ready data for enterprises.
Ashley Vassell-Robertson
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Published:
Sep 30, 2025
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4 minute read
Use Cases

In an era defined by data, a significant challenge persists: 38% of data specialists report difficulties in extracting actionable insights from their log data, all while log volumes continue to rise and logs remain critical for troubleshooting. Fortunately, advancements in artificial intelligence offer promising solutions. At Hydrolix, we are empowering AI-driven enterprises to train accurate models and integrating AI directly into our platform, ensuring that enterprises are uniquely positioned to navigate the complexities of their data and unlock its full potential.

Challenges in Data Management

At NAB 2025, Loic Barbou, Head of Technology at Bloomberg, described two challenges facing AI adoption: “There are two big problems. Indexing is tied to the model you select. Then, the second problem is around the cost of storage. You pay for retrieval, and the data isn’t readily available when you need it.” This highlights a critical need for enterprises to secure data storage solutions that are not only cost-effective but also capable of supporting AI initiatives, whether for storing AI logs or providing data for model training. The sheer volume and accessibility requirements of data for effective model training directly compound these issues.

For AI to be accurate, models need access to extensive historical data, typically a minimum of one full seasonality cycle. Furthermore, this data must be rapidly accessible to ensure the efficiency of training processes, directly relating to Barbou’s point about data availability. Unfortunately, the cost of long-term retention for data at scale is exorbitantly high with many vendors, leading to a difficult choice where teams must choose between accurate models and keeping costs down.

For many traditional storage and data platform providers, accommodating such vast quantities of data is either infeasible due to time range limitations or prohibitively expensive, due in large part to the high cost of storage and retrieval, which has historically led to data sampling or even discarding data altogether. Data sampling can introduce several problems:

Sampling bias: Skewed representation of groups/characteristics that can lead to inaccurate predictions (such as focus on user preferences from one demographic).
Selection bias: Data points are systematically included or excluded due to collection practices, distorting distributions.
Class imbalance: Models favor majority classes and labels due to infrequent minority classes and labels.
Insufficient data: Small sample sizes increase prediction variance and instability.

Ultimately, these challenges underscore a pervasive need for innovative data management solutions that can economically and efficiently support the full lifecycle of AI, from training to operational monitoring.

Hydrolix, AI, and the Future

Hydrolix provides a competitive edge to enterprises by offering cost-effective, full-fidelity data for model training, real-time historical data analysis, and real-time streaming pipelines. The architectural design of Hydrolix directly addresses the most formidable challenges encountered by enterprises within AIOps:

Massive scale: Handle daily petabyte-scale event data ingestion with scalable streaming.
Cost-effective: Achieve 20x-50x compression rates for affordable long-term storage.
Real-time performance: Maintain consistent “hot” query performance for both recent and historical data.
Full ETL capabilities: Standardize, structure, normalize, and transform event data.
Zero egress costs: Keep all analytics within AWS infrastructure.
Proven use cases: Enable applications like custom bot detection systems and ad performance optimization.
Flexible deployment: Both managed (Hydrolix for AWS) and self-hosted options available.

We also offer the Hydrolix Connector for Apache Spark across the 3 biggest Apache Spark vendors: Databricks, AWS and Microsoft. The Hydrolix Connector for Apache Spark makes it easy to access the full fidelity of Hydrolix data in an environment that is ready-made for data preparation and applying machine learning techniques.

As Nathaniel Stone noted: “Hydrolix isn’t just another log analytics tool—it’s a catalyst for redefining how enterprises handle data. By solving the cost-performance paradox and enabling real-time AI-driven insights, Hydrolix is poised to become a cornerstone of modern data infrastructure.”

We are developing fresh features to address some of the biggest challenges facing the data community today. Given the scale of Hydrolix data, this is a significant undertaking. We have an exciting AI roadmap planned for the next few quarters, with a core objective of reducing barriers to data insights. The roadmap includes:

Democratizing data through AI SQL generation: Writing effective SQL is notoriously difficult. Luckily, AI can help translate data needs into effective SQL queries and that will reduce time to insights for customers.
Anomaly detection combined with AI for natural language data correlation: Transforming complete, raw logs into context-rich, predictive, and actionable insights that drive smarter decisions is hard. AI can offer substantial assistance via the rapid extraction of actionable insights, particularly when dealing with petabyte-scale data volumes.
Self-service ingestion of new data sources featuring auto schema detection: A key advantage of Hydrolix is its flexible schema, allowing you to store data from many different sources in a single tble. The integration of AI, especially large language models (LLMs), will further streamline the challenge of transforming diverse schemas, making the process more automated and easily accessible through self-service.