GuideFebruary 16, 2026

Best Data Centers in Washington State (2026)

This comprehensive guide covers everything you need to know about best data centers in washington state (2026). As AI workloads continue to reshape the data center landscape in 2026, having the right infrastructure knowledge is critical for making informed deployment decisions.

Overview

The data center industry is undergoing its most significant transformation in decades, driven by the explosive growth of AI training and inference workloads. Organizations deploying AI infrastructure need facilities that can support unprecedented power densities, advanced cooling systems, and high-bandwidth networking — all while maintaining the reliability, security, and compliance that enterprise workloads demand.

Understanding the nuances of data center infrastructure — from power architecture and cooling technology to network design and compliance frameworks — is essential for anyone responsible for AI infrastructure decisions. This guide provides the knowledge foundation you need.

Key Considerations

When evaluating data center options for any deployment, several fundamental factors should guide your decision-making process:

  • Power capacity and density: Modern AI workloads require 30-80+ kW per rack, far exceeding traditional enterprise densities of 5-15 kW. Ensure your facility can deliver the power density you need today and as you scale. Look for facilities with direct utility feeds, redundant power paths (2N minimum), and room for power growth.
  • Cooling infrastructure: High-density AI racks generate enormous heat that traditional air cooling cannot efficiently manage. Liquid cooling — whether direct-to-chip, rear-door heat exchangers, or immersion — is increasingly necessary. Evaluate whether the facility has liquid cooling infrastructure in place or planned, and whether it can support your specific hardware configurations.
  • Network connectivity: AI training clusters require high-bandwidth, low-latency interconnects (typically InfiniBand at 400 Gbps+). For inference workloads, proximity to end users and cloud on-ramps matters. Assess carrier diversity, cloud connectivity options, and available bandwidth at each facility.
  • Location and latency: For AI training, power cost and cooling efficiency often matter more than location. For inference, latency to end users is critical. Consider your workload mix when choosing locations — training in cheap-power markets, inference at the edge.
  • Compliance and certifications: SOC 2 Type II is the baseline. Depending on your industry, you may also need ISO 27001, PCI-DSS, HIPAA compliance, or FedRAMP authorization. Verify that certifications cover the specific facility and services you'll use.

Market Analysis

The North American data center market continues to grow at an extraordinary pace, with several key trends shaping the landscape in 2026:

  • AI-driven demand: Artificial intelligence is the single largest driver of new data center capacity. AI training clusters consuming 50-200 MW are becoming common, and the total US data center power demand is projected to reach 35 GW by 2030.
  • Power constraints: Grid capacity has become the primary bottleneck in many markets. Northern Virginia, the world's largest data center market, faces multi-year utility connection delays. This is pushing development to secondary markets with available power.
  • Liquid cooling adoption: The transition from air cooling to liquid cooling is accelerating. Most new AI-focused facilities are being built with liquid cooling from day one, and existing facilities are retrofitting to support higher power densities.
  • Sustainability pressure: ESG commitments and regulatory requirements are driving operators to procure renewable energy, improve PUE, and reduce water usage. Nuclear power (including small modular reactors) is gaining interest as a clean baseload option.
  • Geographic diversification: New markets are emerging as traditional hubs face power constraints. Columbus (OH), Portland (OR), Salt Lake City, and Reno are attracting significant investment.

Technology Trends

Several technology trends are reshaping data center design and operations:

  • 800G networking: 800 Gbps Ethernet and InfiniBand are becoming available, doubling the bandwidth of previous-generation 400G. This enables larger GPU clusters with better scaling efficiency.
  • Composable infrastructure: Disaggregated architectures that separate compute, memory, and storage allow more flexible resource allocation. This is particularly relevant for AI workloads with varying resource requirements.
  • AI-powered operations: Data center operators are increasingly using AI for predictive maintenance, cooling optimization, and capacity planning. AI models can predict equipment failures hours or days before they occur and optimize cooling in real time to reduce energy consumption.
  • Edge-cloud continuum: The boundary between edge and cloud is blurring, with orchestration platforms that can seamlessly place workloads across edge, regional, and central facilities based on latency, cost, and availability requirements.

Cost Optimization

Optimizing data center costs requires attention to several levers:

  • Power efficiency: A PUE improvement from 1.4 to 1.2 saves 14% on electricity costs. For a 10 MW deployment at $0.08/kWh, that's $980K/year in savings.
  • Contract negotiation: Multi-year commitments, volume discounts, and flexible power scaling provisions can significantly reduce per-kW costs. Negotiate power pricing separately from space pricing.
  • Geographic arbitrage: Power costs vary 3-4x across US markets ($0.04/kWh in Oregon vs $0.14/kWh in NYC). For power-intensive AI workloads, choosing the right market can save millions annually.
  • Workload placement: Match workloads to the most cost-effective infrastructure. AI training (latency-insensitive, power-hungry) should go to cheap-power markets. AI inference (latency-sensitive, distributed) should go to edge/metro locations.

Getting Started

Ready to find the right data center for your needs? Here are your next steps: