Top 10 AI Inference Platforms in 2025

The development of Large Language Model (LLM) applications is accelerating rapidly, driven by the need for automation, operational efficiency, and advanced insights. These breakthroughs rely on AI inferencing platforms, which enable natural language understanding and generation at scale.

Selecting the right platform is pivotal to ensuring optimal performance, scalability, and cost-effectiveness for your AI products.

In this guide, we highlight the top AI inferencing platforms in 2025, including Together AI, Fireworks AI, Hugging Face, and others to help you identify the ideal option for your needs. If you're exploring alternatives to OpenAI, this guide will help you make an informed decision.

Top AI Inferencing Platforms Overview

1. Together AI
2. Fireworks AI
3. OpenRouter
4. Hyperbolic
5. Replicate
6. Hugging Face
7. Groq
8. DeepInfra
9. Perplexity AI
10. Anyscale

1. Together AI

Best for: Large-scale model training with a focus on privacy and cost efficiency.

What is Together AI?

Together AI offers high-performance inference for 200+ open-source LLMs with sub-100ms latency, automated optimization, and horizontal scaling - all at a lower cost than proprietary solutions. Their infrastructure handles token caching, model quantization, and load balancing, letting developers focus on prompt engineering and application logic rather than managing infrastructure.

Why do companies use Together AI?

Together AI's pricing makes it up to 11x more affordable than GPT-4 when using Llama-3, 4x faster throughput than Amazon Bedrock, and 2x faster than Azure AI.

Developers can access 200+ open-source models including Llama 3, RedPajama, and Falcon with just a few lines of Python, making it straightforward to swap between models or run parallel inference jobs without managing separate deployments or wrestling with CUDA configurations.

Together AI Pricing

Free tier available; pay per token or GPU usage for serverless options.

Bottom Line

Together AI is ideal for developers who wants access to a wide range of open-source models. With flexible pricing and high-performance infrastructure, it's a strong choice for companies that require custom LLMs and a scalable solution that is optimized for AI workloads.

Adding LLM Observability to Together AI

Create an Helicone account, then change your baseurl. See docs for details.

# old endpoint
https://api.together.xyz/v1/

# switch to new endpoint with Helicone
https://together.helicone.ai/v1/

# add the following header
"Helicone-Auth": "Bearer [HELICONE_API_KEY]"

2. Fireworks AI

Best for: Speed and scalability in multi-modal AI tasks.

What is Fireworks AI?

Fireworks AI has one of the fastest model APIs. It uses its proprietary optimized FireAttention inference engine to power text, image, and audio inferencing, all while prioritizing data privacy with HIPAA and SOC2 compliance. It also offers on-demand deployment as well as fine-tuning text models to use either serverless or on-demand.

Why do companies use Fireworks AI?

Fireworks makes it easy to integrate state-of-the-art multi-modal AI models like FireLLaVA-13B for applications that require both text and image processing capabilities. Fireworks AI has 4x lower latency than other popular open-source LLM engines like vLLM, and ensures data privacy and compliance requirements with HIPAA and SOC2 compliance.

Fireworks AI Pricing

All services are pay-as-you-go. Get started here.

Bottom Line

Fireworks is ideal for companies looking to scale their AI applications. Moreover, developers can integrate Fireworks with Helicone to get production-grade LLM infrastructure with built-in observability and real-time cost and usage monitoring.

Adding LLM Observability to Fireworks AI

Create an Helicone account, then change your baseurl. See docs for details.

# old endpoint
https://api.fireworks.ai/inference/v1/chat/completions

# switch to new endpoint with Helicone
https://fireworks.helicone.ai/inference/v1/chat/completions

# add the following header
"Helicone-Auth": "Bearer [HELICONE_API_KEY]"

3. OpenRouter

Best for: Routing traffic across multiple LLMs.

What is OpenRouter?

OpenRouter is an inference marketplace that provides access to over 300 models from all of the top providers through a unified OpenAI-compatible API. This API enables seamless integration with models from OpenAI, Anthropic, Google, Bedrock and many others.

Why do companies use OpenRouter?

Companies choose OpenRouter for its ability to provide simple access to multiple AI models through a single API interface. The platform offers automatic failovers and competitive pricing while eliminating the need to integrate and manage multiple provider APIs separately.

OpenRouter Pricing

• Pay-as-you-go model, with specific pricing listed for each model on openrouter.ai/models.
• Users have flexible payment options, including traditional payment methods, cryptocurrency, and API-based payments.

Bottom Line

OpenRouter is a great option for developers who want flexibility in switching between LLM providers. With a single API, you can access hundreds of AI models while getting full funcionality for production deployments.

Adding LLM Observability to OpenRouter

Create an Helicone account, then change your baseurl. See docs for details.

# old endpoint
https://openrouter.ai/api/v1/chat/completions

# switch to new endpoint with Helicone
https://openrouter.helicone.ai/api/v1/chat/completions

# add the following header
"Helicone-Auth": "Bearer [HELICONE_API_KEY]"

4. Hyperbolic

Best for: Developers looking for cost-effective GPU rental and API access.

What is Hyperbolic?

Hyperbolic is a platform that provides AI inferencing service, affordable GPUs, and accessible compute for anyone who interacts with the AI system — AI researchers, developers, and startups to build AI projects at any scale.

Why do companies use Hyperbolic?

Hyperbolic provides access to top-performing models for Base, Text, Image, and Audio generation at up to 80% less than the cost of traditional providers without compromising quality. They also guarantee the most competitive GPU prices compared to large cloud providers like AWS. To close the loop in the AI ecosystem, Hyperbolic partners with data centers and individuals who have idle GPUs.

Hyperbolic Pricing

The base plan is free to start, catered to startups and small to medium-sized enterprises that need higher throughput and advanced features. Premium pricing model is geared toward academic and advanced enterprise use. Get started here.

Bottom Line

Hyperbolic's strength lies in providing inference and compute at a fraction of the cost, with one of the fastest times to support new models such as Deepseek R1 and R1-Zero as they become available. For those looking to serve state-of-the-art models at a competitive price, Hyperbolic would be a suitable option.

Adding LLM Observability to Hyperbolic

Create an Helicone account, then change your baseurl. See docs for details.

# old endpoint
https://api.hyperbolic.xyz/v1/

# switch to new endpoint with Helicone
https://hyperbolic.helicone.ai/v1/

# add the following header
"Helicone-Auth": "Bearer [HELICONE_API_KEY]"

5. Replicate

Best for: Rapid prototyping and experimenting with open-source or custom models.

What is Replicate?

Replicate is a cloud-based platform that simplifies machine learning model deployment and scaling. Replicate uses an open-source tool called Cog to package and deploy models, and supports a diverse range of large language models like Llama 2, image generation models like Stable Diffusion, and many others.

Why do companies use Replicate?

Replicate is great for quick experiments and building MVPs (model performance varies based on user uploads). Replicate has thousands of pre-built, open-source models covering a wide range of applications like text generation, image processing, and music generation - and getting started requires just one line of code.

Replicate Pricing

Based on usage with a pay-per-inference model. Get started here.

Bottom Line

Replicate scales well for small to medium workloads but may need extra infrastructure for high-volume apps. It's a great choice for experimentation and for developers who need quick access to models without the setup and overhead.

Adding LLM Observability to Replicate

Create an Helicone account, then change your baseurl. See gateway docs for details.

# old endpoint
https://api.replicate.com/v1/predictions

# switch to new endpoint with Helicone
https://gateway.helicone.ai/v1/predictions

# then add the following headers
"Helicone-Auth": "Bearer [HELICONE_API_KEY]",
"Helicone-Target-Url": "https://api.replicate.com",
"Helicone-Target-Provider": "Replicate",

6. HuggingFace

Best for: Getting started with Natural Language Processing (NLP) projects.

What is HuggingFace?

HuggingFace is an open-source community where developers can build, train, and share machine learning models and datasets. It's most popularly known for its transformer library. HuggingFace makes it easy to collaborate, and it's a great starting point for many NLP projects.

Why do companies use HuggingFace?

HuggingFace has an extensive model hub with over 100,000 pre-trained models such as BERT and GPT. It also integrates with different languages and cloud platforms, providing scalable APIs that easily extend to services like AWS.

HuggingFace Pricing

Free for basic use; enterprise plans available. Get started here.

Bottom Line

HuggingFace has a strong emphasis on open-source development, so you may find inconsistency in documentation, or have trouble finding examples for complex use cases. However, HuggingFace is a great library of pre-trained models for fine-tuning and AI inferencing — which is useful for many NLP use cases.

Adding LLM Observability to HuggingFace

Create an Helicone account, then change your baseurl. See gateway docs for details.

# old endpoint
https://api-inference.huggingface.co/v1/

# switch to new endpoint with Helicone
https://gateway.helicone.ai/v1/

# then add the following headers
"Helicone-Auth": "Bearer [HELICONE_API_KEY]",
"Helicone-Target-Url": "https://api-inference.huggingface.co",
"Helicone-Target-Provider": "HuggingFace",

7. Groq

Best for: High-performance inferencing with hardware optimization.

What is Groq?

Groq specializes in hardware optimized for high-speed inference. Its Language Processing Unit (LPU), a specialized chip built for ultra-fast AI inference, significantly outperforms traditional GPUs, providing up to 18x faster processing speeds for latency-critical AI applications.

Why do companies use Groq?

Groq scales exceptionally well in performance-critical applications. In addition, Groq provides both cloud and on-premises solutions, making it a suitable option for high-performance AI applications across industries. Groq is suited for enterprises that require high-performance, on-premises solutions.

Groq Pricing

Token-based pricing, geared towards enterprise use. Get started here.

Bottom Line

If ultra-low latency and hardware-level optimization are critical for your application, using LPU can give you a significant advantage. However, you may need to adapt your existing AI workflows to leverage the LPU architecture.

Adding LLM Observability to Groq

Create an Helicone account, then change your baseurl. See docs for details.

# old endpoint
https://api.groq.com/openai/v1

# switch to new endpoint with Helicone
https://groq.helicone.ai/openai/v1

# add the following header
"Helicone-Auth": "Bearer [HELICONE_API_KEY]"

8. DeepInfra

Best for: Cloud-based hosting of large-scale AI models.

What is DeepInfra?

DeepInfra offers a robust platform for running large AI models on cloud infrastructure. It's easy to use for managing large datasets and models. Its cloud-centric approach is best for enterprises needing to host large models.

Why do companies use DeepInfra?

DeepInfra's inference API takes care of servers, GPUs, scaling, and monitoring, and accessing the API takes just a few lines of code. It supports most OpenAI APIs to help enterprises migrate and benefit from the cost savings. You can also run a dedicated instance of your public or private LLM on DeepInfra infrastructure.

DeepInfra Pricing

Usage-based, billed by token or at execution time. Get started here.

Bottom Line

DeepInfra is a good option for projects that need to process large volumes of requests without compromising performance.

Adding LLM Observability to DeepInfra

Create an Helicone account, then change your baseurl. See docs for details.

# old endpoint
https://api.deepinfra.com/v1/

# switch to new endpoint with Helicone
https://deepinfra.helicone.ai/v1/

# add the following header
"Helicone-Auth": "Bearer [HELICONE_API_KEY]"

9. Perplexity AI

Best for: AI-driven search and knowledge applications.

What is Perplexity?

Perplexity AI is known for its AI-powered search and answer engine. While primarily a consumer-facing service, they offer APIs for developers to access intelligent search capabilities. pplx-api is a new service designed for fast access to various open-source language models.

Why do companies use Perplexity?

Developers can quickly integrate state-of-the-art open-source models via the familiar REST API. Perplexity is also rapidly including new open-source models like Llama and Mistral within hours of launch.

Perplexity Pricing

Usage or subscription-based. Pro users receive a recurring $5 monthly pplx-api credit. For all other users, pricing will be determined based on usage. Get started here.

Bottom Line

Perplexity AI is suitable for developers looking to incorporate advanced search and Q&A capabilities into their applications. If improving information retrieval is a crucial aspect of your project, using Perplexity can be a good move.

Adding LLM Observability to Perplexity AI

Create an Helicone account, then change your baseurl. See gateway docs for details.

# old endpoint
https://api.perplexity.ai/chat/completions

# switch to new endpoint with Helicone
https://gateway.helicone.ai/chat/completions

# then add the following headers
"Helicone-Auth": "Bearer [HELICONE_API_KEY]",
"Helicone-Target-Url": "https://api.perplexity.ai",
"Helicone-Target-Provider": "Perplexity",

10. Anyscale

Best for: End-to-end AI development and deployment and applications requiring high scalability.

What is Anyscale?

Anyscale is a platform for scaling compute-intensive AI workloads ranging from model training to serving to batch processing. Anyscale is the company behind Ray, the open-source AI compute engine used by companies like Uber, Spotify, and Airbnb as the foundation of their AI platforms.

Why do companies use Anyscale?

Anyscale offers governance, admin, and billing controls as well as security and privacy features suitable for enterprise-grade applications. Anyscale is also compatible with any cloud, accelerator, or stack, and has expert support from Ray, AI, and ML specialists.

Anyscale Pricing

Usage-based, enterprise pricing available. Get started here.

Bottom Line

Anyscale is ideal for developers building applications that require high scalability and performance. If your project uses Python and you are at the scaling stage, Anyscale can be a good option.

Adding LLM Observability to Anyscale

Create an Helicone account, then change your baseurl. See docs for details.

# old endpoint
https://api.endpoints.anyscale.com/v1

# switch to new endpoint with Helicone
https://oai.helicone.ai/v1/

# then add the following headers
"Helicone-Auth": "Bearer [HELICONE_API_KEY]",
"Helicone-OpenAI-API-Base": "https://api.endpoints.anyscale.com/v1",

Choosing the Right API Provider

When choosing an AI inferencing platform, it's essential to consider your specific project requirements, whether it's affordability, speed, scalability, or advanced functionality.

• For high performance and privacy: Together AI offers high-quality responses, faster response time, and lower cost, with a focus on privacy and scalability.
• For cost-effective solutions: Hyperbolic provides access to top-performing models at a fraction of the cost, with competitive GPU prices.
• For rapid prototyping and experimentation: Replicate simplifies machine learning model deployment and scaling, ideal for quick experiments and building MVPs.
• For NLP projects and open-source models: HuggingFace provides an extensive library of pre-trained models and a strong open-source community.
• For ultra-low latency applications: Groq specializes in hardware optimized for high-speed inference with their Language Processing Unit (LPU).
• For large-scale AI applications: DeepInfra excels in hosting and managing large AI models on cloud infrastructure.
• For flexibility across multiple LLM providers: OpenRouter allows routing traffic between multiple LLM providers for optimal performance.
• For AI-driven search and knowledge applications: Perplexity AI specializes in AI-powered search engines and knowledge retrieval.

Remember to consider factors such as pricing, model variety, ease of integration, and scalability when making your final decision. It's often beneficial to start with a small-scale test before committing to a provider for large-scale deployment.

Frequently Asked Questions

What are LLM API providers?

LLM API providers offer cloud-based platforms for accessing and utilizing Large Language Models (LLMs) through Application Programming Interfaces (APIs). They allow developers to integrate advanced AI capabilities into their applications without having to train or host the models themselves.

Why should I choose an LLM API provider instead of just using OpenAI?

While OpenAI is a popular choice, using alternative LLM API providers has several benefits:

• Lower costs, especially for high-volume usage
• Access to diverse, specialized models
• Easier fine-tuning and customization
• Better data privacy control
• Faster performance with optimized hardware
• Flexibility to switch between models or providers
• Support for open-source development

How do I choose the right LLM API provider for my project?

Consider factors such as performance, cost, available models, scalability, ease of integration, specialized features, infrastructure reliability, data privacy, and community support. Your choice should align with your specific project requirements and budget.

Are open-source models as good as proprietary ones?

Open-source models have made significant advancements and can often compete with proprietary models in performance. Providers like Together AI and Fireworks AI offer high-quality open-source models that can outperform some proprietary alternatives.

What's the most cost-effective LLM API provider?

Cost-effectiveness varies based on your usage. Hyperbolic claims to offer up to 80% cost reduction compared to traditional providers. However, it's best to compare pricing models across providers based on your expected usage patterns.

Which provider offers the fastest inference?

Groq specializes in ultra-fast AI inference with their Language Processing Unit (LPU). Fireworks AI also claims to have one of the fastest model APIs. However, actual performance may vary based on specific use cases and models.

What if I need to fine-tune models for my specific use case?

Providers like Together AI, Replicate, and HuggingFace offer capabilities for fine-tuning models. Check each provider's documentation for specific instructions on model customization.

Can these LLM API providers handle multi-modal AI tasks (e.g., text and image processing)?

Yes, some providers offer multi-modal capabilities. For example, Fireworks AI supports models like FireLLaVA-13B for both text and image processing.

What's the difference between serverless and on-demand deployment options?

Serverless options, offered by providers like Fireworks AI, automatically scale resources based on demand. On-demand deployment gives you more control over the infrastructure but requires more management.

Are these LLM API providers suitable for enterprise-level applications?

Yes, many of these providers offer enterprise-grade solutions. Anyscale, DeepInfra, and Together AI, for example, provide scalable solutions suitable for large-scale enterprise applications.

How do I get started with using an LLM API provider?

Most providers offer documentation and quickstart guides. Generally, you'll need to sign up for an account, obtain an API key, and then you can start making API calls to the models. Some providers also offer free tiers or credits for initial experimentation.