Database Performance for Large Language Models: The Key to Agentic AI Success

Why Enterprises Can’t Afford to Ignore Optimised Data Services in a Multi-Tenant AI Cloud

In our previous blog, we explored how Node4’s DBA Services and Zadara’s Multi-Tenant Cloud Infrastructure provide a foundation for AI innovation. Now, as Large Language Models (LLMs) become central to enterprise AI, a new urgency emerges: your database performance isn’t just important—it’s mission critical.

The Shift to Agentic AI: A Database Wake-Up Call

We’re entering an era where AI agents—self-directed systems that observe, reason, and act—require seamless access to structured and unstructured data to function effectively. These LLM-powered agents depend on real-time database interactions to:

  • Interpret prompts and act on user intent
  • Retrieve business logic or contextual records
  • Orchestrate tools, APIs, and internal systems dynamically

Without high-throughput, low-latency database access, these systems stall, degrade, or fail—leading to a loss of trust and productivity.

What Happens When Databases Fall Behind

Let’s be clear: LLMs are only as fast and smart as the data pipeline behind them.

Poorly tuned or underperforming databases result in:

  • Slow token generation that frustrates users
  • Inaccurate or outdated responses from AI agents
  • Higher infrastructure costs due to inefficient compute usage
  • Security blind spots from unsegmented tenant data or delayed syncs
  • Failure to meet SLAs in AI-driven apps

As the load from multiple tenants and concurrent agents increases, these impacts scale fast—and so do the business risks. You simply cannot afford for your AI Projects to fail because of poorly defined and integrated data services. Not all infrastructure is the same, you have to consider the needs of each application and its ability to serve an ever increasing workload that will certainly come from these integrations with LLM’s and agentic workloads. If your systems are waiting for Database services to return information then you are also wasting expensive GPU time.

Optimised Data Services = Faster, Smarter AI

That’s why enterprises building AI solutions must look beyond model performance to data performance.

Zadara, in partnership with Node4, offers Database Management-as-a-Service (DBaaS) that’s purpose-built for modern AI workloads:

  • Predictable, low-latency access to mission-critical data
  • Scalable performance across thousands of concurrent LLM queries
  • Granular multi-tenant isolation to protect and optimise agent activity
  • Proactive tuning and health checks managed by experts
  • Seamless integration with NVIDIA-powered AI clouds

Whether you’re fine-tuning private LLMs, deploying AI copilots, or enabling autonomous agents across departments, Zadara and Node4 ensure your databases don’t just keep up—they lead.

The Enterprise Advantage

In today’s agentic AI world, enterprises that invest in data optimisation see:

  • Faster time to insight with real-time, query-ready data
  • Lower total cost of AI ownership by maximising utilisation
  • Increased trust and adoption of AI by end-users
  • Compliance-ready, secure operations at every layer

 

It’s no longer just about training the smartest model—it’s about feeding it the right data at the right time.

Ready to Optimise Your AI Stack?

Don’t let your database be the bottleneck in your AI journey.
Contact us today to see how Zadara and Node4 can optimise your data services for LLMs and autonomous AI agents—at scale and with confidence.

Schedule a free consultation
Learn more about our DBaaS for AI



Picture of Steve Costigan

Steve Costigan

Steve Costigan, Field CTO EMEA at Zadara, is an experienced IT professional, with over 30 years of experience across many technologies and systems within the data center and cloud arena. Steve is skilled in taking complex technical subjects and making a simplified solution achievable–especially around storage, virtualization, and cloud technologies.

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