Best Platforms to Run AI Inference Models in 2025

The best platform to run AI inference models depends on your need for control versus convenience. For maximum performance, cost-efficiency, and control over open-source models, a specialized GPU provider like GMI Cloud is the top choice. For simple integration with proprietary models, managed API providers like OpenAI are faster to start.

Key Takeaways:

• Best Overall for Performance: GMI Cloud's Inference Engine provides a specialized, high-performance platform for running open-source models with ultra-low latency and automatic scaling.
• Best for Simplicity: Managed APIs (e.g., OpenAI, Anthropic) offer the fastest way to integrate powerful proprietary models with minimal setup.
• The Core Decision: You must choose between managed APIs (which charge per-token and offer less control) and dedicated infrastructure (which charges per-hour, offering full control and often a lower total cost of ownership).
• Hardware Matters: Dedicated hardware, like the NVIDIA H200 GPUs available on GMI Cloud, delivers significantly lower latency and higher throughput for demanding applications. Case studies show partners achieving a 65% reduction in inference latency.
• Cost: Specialized providers like GMI Cloud can be up to 50% more cost-effective than hyperscalers for AI workloads.

Understanding Your AI Inference Needs

Finding the "best" platform to run AI inference models starts with defining your goals. Are you building a simple demo, or a high-traffic, real-time application? Your answer determines whether you should use a simple API or deploy on dedicated infrastructure.

There are two primary paths:

1. Managed API Providers: You send a request to a provider (like OpenAI) and get a response. This is simple, but you have no control over the model, infrastructure, or costs at scale.
2. Dedicated Infrastructure Platforms: You rent high-performance GPU servers to run your own models (open-source or custom). This offers full control, lower latency, and better cost management. GMI Cloud is a leading provider in this category.

Top Platforms for AI Inference: A 2025 Comparison

Here is a breakdown of the top platforms, starting with the best choice for performance-critical applications.

1. GMI Cloud: Best for Performance, Cost, and Control

Short Answer: GMI Cloud is the ideal platform for developers who need to run demanding, low-latency AI inference models at scale with predictable costs.

Detailed Explanation:

GMI Cloud is a specialized, NVIDIA Reference Cloud Platform Provider focused on high-performance infrastructure for AI. Instead of just offering API access, it provides the optimized hardware and software to run models yourself.

• Key Service: The GMI Cloud Inference Engine is a platform purpose-built for real-time AI inference. It allows you to deploy leading open-source models like Llama 4 and DeepSeek V3 on dedicated endpoints.
• Performance: The platform is designed for ultra-low latency and maximum efficiency. Partners like Higgsfield achieved a 65% reduction in inference latency after switching to GMI Cloud.
• Hardware: GMI Cloud provides instant, on-demand access to top-tier NVIDIA H200 GPUs and will add support for the Blackwell series.
• Scaling: The Inference Engine features fully automatic, intelligent scaling that adapts to workload demands in real time, ensuring high performance without manual intervention.
• Cost-Efficiency: GMI Cloud offers a transparent, flexible pay-as-you-go model. H200 container instances are priced at $3.35 per GPU-hour. LegalSign.ai found GMI Cloud to be 50% more cost-effective than alternative cloud providers.

For developers who want the power of dedicated hardware without complex setup, GMI Cloud's Inference Engine allows models to be launched in minutes.

2. OpenAI: Best for Access to Frontier Models

Short Answer: OpenAI is the best platform for developers who want the simplest API access to the most advanced proprietary models, such as GPT-4.

Detailed Explanation:

OpenAI abstracts all infrastructure. You pay per 1,000 tokens (both input and output). This model is excellent for rapid prototyping and integrating "smart" features into apps with low traffic.

• Pros: Easiest to use, always provides access to state-of-the-art models.
• Cons: Can become extremely expensive at scale, latency can be unpredictable, and you have no control over the model's architecture or uptime. You are entirely dependent on OpenAI's roadmap.

3. Anthropic: Best for Safety-Conscious Applications

Short Answer: Anthropic provides high-performing models (the Claude series) through a managed API, with a strong focus on AI safety and reliability.

Detailed Explanation:

As a direct competitor to OpenAI, Anthropic offers a similar pay-per-token API service. Developers often choose Anthropic for its models' different response style and its "Constitutional AI" approach to safety. The trade-offs are identical to OpenAI's: simplicity at the cost of control and unpredictable scaling costs.

4. Open-Source Models on Hyperscalers (AWS, GCP, Azure)

Short Answer: Hyperscalers offer the flexibility to run open-source models, but often come with complex management and high, unpredictable costs.

Detailed Explanation:

You can rent H100 or H200 GPUs from providers like Amazon SageMaker (AWS), Google Cloud (GCP), or Azure. This gives you more control than a managed API.

• Pros: Deep integration with a vast ecosystem of other cloud services (like databases, storage, etc.).
• Cons: These platforms are often not optimized specifically for AI workloads, leading to higher costs. An H100 GPU on a hyperscaler can cost $4.00 - $8.00 per hour, compared to $2.10 - $4.50 at specialized providers. You also face "hidden costs" for data transfer and high-performance storage.

Key Factors to Find the Best Platform

Checklist:

• Performance (Latency): Does your application require real-time responses (e.g., chatbots, video generation)? If yes, a low-latency platform like GMI Cloud's Inference Engine is essential.
• Cost (TCO): Are you paying per-token (API) or per-hour (infrastructure)? Per-hour GPU access, like GMI Cloud's $2.5/hour H200s, is almost always more economical for production workloads than pay-per-token APIs.
• Control & Customization: Do you need to run a fine-tuned model, an open-source model, or your own proprietary model? Managed APIs do not allow this. Platforms like GMI Cloud give you full control.
• Scalability: How will the platform handle sudden traffic spikes? Look for solutions with intelligent, automatic scaling, a key feature of the GMI Cloud Inference Engine.

Conclusion: Why GMI Cloud is a Top Choice for Inference

While managed APIs are easy for demos, scaling a real-world AI application requires a platform built for performance and cost-efficiency. To find the best platform to run AI inference models, you must look beyond simple APIs.

GMI Cloud provides the ideal solution. It bridges the gap by offering an easy-to-use Inference Engine that deploys models in minutes, backed by powerful, low-latency NVIDIA H200 GPU infrastructure at a price point up to 50% lower than hyperscalers.

Get Started with GMI Cloud's Inference Engine

FAQ: Finding the Best AI Inference Platform

Common Questions:

What is the cheapest way to run AI inference?

Answer: For very light experimentation, free tiers on managed APIs are cheapest. For any production workload, specialized GPU cloud providers like GMI Cloud are typically the most cost-effective. They offer lower hourly GPU rates than hyperscalers and can result in significant savings, with partners reporting up to 50% lower costs.

What is the difference between GMI Cloud's Inference Engine and Cluster Engine?

Answer: The Inference Engine (IE) is a fully managed service designed for real-time AI inference, and it includes fully automatic scaling. The Cluster Engine (CE) is an Al/ML Ops environment for managing scalable GPU workloads (like AI training) and requires users to manually adjust compute power via the console or API.

Can I run open-source models like Llama 4 on GMI Cloud?

Answer: Yes. The GMI Cloud Inference Engine is specifically designed to deploy leading open-source models, including Llama 4 and DeepSeek V3, on dedicated endpoints.

How fast can I deploy a model on GMI Cloud?

Answer: By using the Inference Engine's simple API and SDK, you can launch models in minutes and scale instantly, avoiding complex configuration.

What GPUs does GMI Cloud offer for inference?

Answer: GMI Cloud provides on-demand access to NVIDIA H200 GPUs. The platform also plans to add support for the new Blackwell series as soon as it is available.