When choosing the right bare metal dedicated server provider, attention to detail is what keeps you from insufficiency and overkill. While you don't want to waste money, having enough capacity is crucial to avoid letting your customers down.

AI, machine learning, and large-scale data processing are becoming increasingly central to enterprise strategy. Among the contenders in this space, DigitalOcean and Latitude.sh are two distinct players offering servers tailored for compute-intensive workloads.

While both promise dedicated performance and infrastructure control, their offerings diverge significantly regarding transparency, scalability, and target audience. 

This article explores how each company approaches bare metal GPU hosting, comparing their infrastructure, networking capabilities, pricing models, and more.

Latitude.sh

Latitude.sh is an infrastructure provider focused on delivering high-performance bare metal servers across a global network. 

Designed for teams that need speed, control, and scalability, Latitude.sh offers on-demand provisioning, full API access, and customizable network configurations. 

With deployments in under five minutes and data centers in key regions worldwide, the platform provides the flexibility of the cloud with the power of single-tenant hardware—ideal for latency-sensitive workloads and demanding applications.

DigitalOcean

DigitalOcean is a cloud platform aimed at simplifying infrastructure for developers and small to medium-sized businesses. 

Known for its clean interface and straightforward pricing, it offers a range of products including virtual machines (Droplets), managed databases, object storage, and Kubernetes clusters. 

DigitalOcean is often chosen for its ease of use and rapid deployment capabilities, making it a popular option for startups and individual developers looking to get projects off the ground quickly.

GPU Infrastructure: Clusters vs Customization

DigitalOcean’s foray into bare metal GPUs is notably focused on scale. Their flagship servers come preconfigured with top-tier GPUs (8 per node): NVIDIA H100, NVIDIA H200, or AMD’s MI300X. 

This architecture is built for maximum throughput and caters to organizations that need to train large language models or deploy tightly coupled inference pipelines across multiple GPUs with high-speed interconnects.

Latitude.sh, by contrast, emphasizes composability. Users can choose from a broader range of GPUs—including NVIDIA’s H100, GH200, L40s, and A100—and scale from one to eight GPUs per server. 

This flexibility appeals to both enterprise workloads and smaller, more agile teams who may not need full racks of GPUs from the outset. 

Whether you’re running inference on a single A100 or training LLMs with eight H100s, Latitude.sh enables infrastructure to evolve with your needs without overcommitting from day one.

• Takeaway: DigitalOcean is ideal if your goal is "go big or go home." Latitude.sh offers more granular entry points for cost-sensitive or experimental use cases.

Performance and Hardware Specifications

DigitalOcean: Built for Massive Throughput

• GPUs: NVIDIA H100 (80GB), H200 (141GB), AMD MI300X (192GB)

• Storage: 8 x 7.68 TB NVMe SSDs (~61.44 TB total)

• Networking:

  • Public: Up to 40 Gbps

  • Private: East-West up to 400 Gbps

  • GPU Interconnect: 3.2 Tbps (NVLink for NVIDIA, Infinity Fabric for AMD)

• Deployment Model: 8-GPU-per-node, high-density servers

• Use Cases: LLM training, inference at scale, large-scale HPC applications

DigitalOcean’s bare metal architecture tends to be really attractive to AI labs and Fortune 500 innovation teams. Everything from storage to interconnect speed is dialed up for parallelism. 

This design minimizes bottlenecks when training foundation models or building inference pipelines that span hundreds of billions of parameters.

Latitude.sh: High Speed Meets High Flexibility

• GPUs: NVIDIA H100, GH200, A100, L40s (1 to 8 per server)

• Storage: Up to 4 x 8 TB NVMe SSDs (32 TB total on high-end configs)

• Networking:

  • Up to 2 x 100 Gbps (200 Gbps total bandwidth)

  • Global private network included

  • 20+ TB traffic allowance per month

• Deployment Model: Customizable GPU allocation per server

• Use Cases: Scalable ML workloads, experimentation, edge inference, and hybrid AI stacks.

Latitude.sh’s focus is configurability. You don’t need to rent eight H100s to access serious performance. You can start small, iterate fast, and scale on demand. It’s infrastructure that grows with your team’s ambitions rather than dictating them.

• Observation: DigitalOcean is optimized for throughput-heavy, all-in workloads. Latitude.sh offers a more elastic model for evolving GPU needs.

Provisioning and Deployment

DigitalOcean

DigitalOcean’s bare metal GPU nodes are not available for self-service provisioning. Instead, they follow a contract-driven process: teams must contact sales, negotiate terms, and sign a usage agreement. 

While this makes sense for planned, high-budget deployments, it’s less appealing for fast-paced teams or startups iterating weekly.

Latitude.sh

Latitude.sh, true to its bare metal roots, enables users to provision GPU-powered servers in seconds. 

Whether you deploy via the platform's dashboard, API calls, or command-line interface, the experience mirrors that of a cloud-native platform, with the performance benefits of single-tenant hardware.

• Developer Highlight: The Latitude.sh API supports automating provisioning, scaling, monitoring, and decommissioning, giving DevOps engineers complete infrastructure-as-code control. Latitude.sh’s fast deployment capability is a major differentiator in a space historically known for slow deployments.

Pricing Transparency

Latitude.sh: What You See Is What You Pay

One of the most user-friendly aspects of Latitude.sh’s model is the publicly available pricing. 

For example:

• g3.h100.small (1 x NVIDIA H100 GPU):

  • $1,756/month or $2.41/hour

  • No contract, no quotes, no hidden fees

  • Deployment available in locations like Chicago, São Paulo, and Singapore

Every configuration is documented on their website, including hardware specs, pricing, and available regions. This level of clarity is crucial for budget-conscious teams and finance departments trying to forecast costs.

DigitalOcean: Enterprise Negotiation Required

In contrast, DigitalOcean does not publicly list pricing for its bare metal GPU servers. Users must contact sales to begin the provisioning process, and pricing is negotiated privately. 

While this approach suits larger organizations familiar with procurement cycles, it’s less suited for startups or developers who prefer transparent billing models and pay-as-you-go flexibility.

• Takeaway: Latitude.sh lowers the friction to entry and avoids vendor opacity. DigitalOcean prioritizes a curated, enterprise-driven sales motion.

Software & Ecosystem

Both providers support Linux environments and give users root access, allowing them to install the frameworks, libraries, and tools needed for AI/ML workloads. But their approaches to software readiness differ slightly:

DigitalOcean

• Preloaded with Ubuntu 22.04 or 24.04

• Includes GPU drivers (e.g., NVIDIA 535 for H100)

• A plug-and-play environment for fast onboarding to standard ML stacks

Latitude.sh

• ML-in-a-Box OS: Users can deploy Ubuntu 22 alongside CUDA, Docker, and PyTorch drivers in 5 seconds.

• Developers can also install different OS options from pre-loaded versions and custom images

• Ideal for users with custom kernels, edge workloads, or HPC environments

  • Note: While DigitalOcean is tuned for rapid AI stack adoption, Latitude.sh offers a sandbox for power users who want more OS-level control.

Choose the Infrastructure That Matches Your Velocity

DigitalOcean and Latitude.sh are not rivals, but reflections of different infrastructure philosophies.

Latitude.sh is the disruptor, offering cloud-like agility on bare metal hardware, with pricing transparency, flexible GPU configurations, and a developer-centric API. If your team values speed, control, and raw power, Latitude.sh makes a compelling case, especially when every GPU cycle (and budget line) matters.

DigitalOcean is the heavyweight, built for larger workloads, streamlined AI pipelines, and teams that plan infrastructure in months, not days. Their tightly coupled 8-GPU nodes offer undeniable power but require upfront negotiation and commitment.

Ultimately, the best choice depends on your stage, scale, and speed of innovation. 

But in a world where AI is evolving by the week, Latitude.sh’s fast deployment model and transparent pricing structure feel well-aligned with the pace of the future.

Ready to get the most out of your AI instances with the power of Bare metal? Get started on Latitude.sh today! Need to talk to our sales team? Here it is.