Artificial Intelligence (AI) data center expansion is fueling a new wave of demand for inference platforms as enterprises push Generative AI (Gen AI) into real-world production environments. Although foundation model training has dominated AI workload capacity, to date, inference is expected to become the larger power consumer over time.
ABI Research forecasts that inference workloads will overtake training workloads by 2033. Better token economics, a more diverse compute landscape, and rapid improvements in Large Language Model (LLM) capabilities are key catalysts for the shift.
To help clarify this fast-growing market, the following five questions examine the role of AI inference platform providers, recent market shifts, leading vendors, future power requirements, and the challenges ahead.
Table of Contents
- What is an AI inference platform provider?
- How has AI inference changed over the past 12 months?
- Who are key inference platform providers?
- How much power will AI inference require?
- What are the market challenges for inference platform providers?
What is an AI inference platform provider?
AI inference platform providers help enterprises deploy and run AI models without having to build and manage their own inference infrastructure. These AI specialists focus on serving models efficiently in production, handling everything from Graphics Processing Unit (GPU) scheduling and model deployment to performance optimization across multi-cloud environments.
Most inference providers aggregate compute from hyperscalers, neoclouds (also a growing competitive challenger), and specialized GPU providers. Customers gain quick access to AI compute without being tied to a single cloud platform. Inference providers tend to leverage NVIDIA hardware for compute power, but support for alternative accelerators, including AMD, is steadily expanding.
How has AI inference changed over the past 12 months?
Over the past year, the AI inference market has become defined by consolidation, increased model training support, and a growing shift toward open-source solutions.
- Market Consolidation: Cloud service providers and large inference providers have made several acquisitions over the last 12 months. Neoclouds CoreWeave and Nebius acquired Weights & Biases and Clarifai, respectively; AI inference platforms Fireworks AI and Baseten absorbed Hathora and Parsed; Cloudflare acquired Replicate; and Qualcomm acquired Modular. Market consolidation is emblematic of AI inference providers’ goal to extend their full stack and fill crucial AI skills gaps. We can expect subsequent acquisitions over the coming months and years as the market accommodates growing enterprise AI requirements.
- Model Training Development: Once pure inference providers, many market players are offering AI model training solutions to position themselves as full model lifecycle management suppliers. Fine-tuning and reinforcement learning are the top use cases.
- Open-Source Model Adoption: In 2025, open-source models accounted for just 35% of global enterprise Gen AI spending, with closed-source making up the rest. For 2026, ABI Research forecasts open-source models to account for 55% of total spend. This rapid uplift is a response to the scalability and cost challenges that AI-native and digital startups have been experiencing with running proprietary models for production.
Who are key inference platform providers?
All based out of Northern California, the top AI inference platform providers assessed by ABI Research are Baseten, Fireworks AI, FriendliAI, Modular, DeepInfra, and Novita AI.
- Baseten: AI inference platform serving hundreds of enterprises and billions of daily inference calls. The San Francisco-based company differentiates itself through an optimized inference stack built on open-source engines (vLLM, SGLang, and TensorRT-LLM) with proprietary optimizations. Recent acquisitions of Parsed and Inferless strengthen Baseten’s complementary model training solutions.
- Fireworks AI: High-performance inference platform processing 15 trillion tokens per day across 400+ models. Known for its proprietary inference stack and Microsoft Azure partnership, Fireworks AI delivers a low-latency, high-throughput model serving more than 10,000 customers.
- FriendliAI: Inference platform focused on real-time AI agents, optimizing long-context inference, tool calling, and low-latency streaming. Expanded enterprise reach exists through a strategic partnership with Samsung SDS and Samsung Cloud Platform.
- Modular: AI infrastructure company taking a unique approach with its Mojo programming language and MAX inference engine. Its hardware-agnostic architecture supports NVIDIA, AMD, Arm, and Apple Silicon from a single codebase, leading to its acquisition by Qualcomm.
- DeepInfra: Cloud inference provider focused on cost-efficient, high-throughput deployment of more than 190 open-source models. Combines TensorRT-LLM and vLLM with optimized token caching, while expanding GPU capacity following major funding.
- Novita AI: Open-source-focused inference platform offering serverless Application Programming Interfaces (APIs) and dedicated endpoints for more than 200 AI models. It strengthened its ecosystem through partnerships with vLLM, SGLang, and Hugging Face, while expanding into secure infrastructure for AI agents.
How much power will AI inference require?
ABI Research forecasts that AI inference workloads will consume 46 Gigawatts (GW) of power capacity by 2035, up from just 2 GW in 2026. We pinpoint the year 2033 as when inference workloads overtake training workloads, driven by:
- Improved token economics for large-scale production environments
- The more heterogeneous compute landscape
- Improved model capabilities
Text generation will remain the top inference workload until 2027, when code generation becomes the predominant use case. Growing at a 52% Compound Annual Growth Rate (CAGR), code generation workloads will consume 23.6 GW of capacity by 2035—more than half of total AI inference capacity.
What are the market challenges for inference platform providers?
AI inference platform providers face commercial challenges stemming from several competitive, technical, and enterprise factors. First, frontier model providers are vertically expanding their solutions to move beyond just model capabilities. Such comprehensive inference platforms enable enterprises to build end-to-end production workflows in one environment, diminishing the perceived value of smaller independent inference providers.
Second, many inference providers rely heavily on AI-native startups for revenue. This creates commercial risk because many tech startups often bring AI workloads in-house as they scale. Diversifying the customer base will be essential for inference providers to maintain market relevance.
Third, the AI compute landscape is becoming more fragmented. Enterprises increasingly deploy workloads across GPUs and other specialized accelerators. Inference providers that fail to optimize their platforms across multiple hardware architectures risk falling behind competitors that deliver better performance and lower costs.
The final challenge is the gradual migration of AI inference closer to where data are generated. As enterprises deploy more AI workstations and edge infrastructure, inference workloads will increasingly migrate away from the cloud to reduce costs, minimize latency, and strengthen data security.
Conclusion
Skills gaps have been a persistent hindrance to scaling AI systems, with an MIT study indicating that 95% of Gen AI pilots fail. ABI Research survey results show that a lack of expertise is the third-biggest challenge to deploying new technologies. AI inference platforms play a unique role in the AI economy by addressing the task-specific model quality, inference latency, and scalability concerns plaguing enterprises and developers.
Key trends shaping the ascending inference market include vendor consolidation, an expansion to more vertical inference solutions, and a shift toward open-source AI tools. With inference power consumption demand exploding over the next decade, technology providers must be prepared to fend off several commercial threats: larger frontier model providers, excessive dependence on a single customer base, heterogeneous compute hardware, and the proliferation of on-premises inference.
If your organization needs guidance on how AI inference-stack differentiation can be achieved, reach out to ABI Research today. We would be happy to discuss our advisory solutions tailored to the business objectives of AI tech suppliers, cloud service providers, and enterprise end users.
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