10 Best AI Inference Platforms

Artificial Intelligence has evolved from the laboratory and is now barely constrained by human-computer interaction. Driving this revolution is inference technology, which allows artificial intelligence to interpret the world and act upon it. Models can now make predictions and generate text.

In addition, with the help of large language models, new systems can understand speech and interpret images (i.e. become human-like in their interaction with the world). These advances in technology are widespread, flexible, and continuously improving. Depending on the requirements of a given workload, businesses can utilize systems like Groq, Fireworks AI, and others to create next-generation products and services.

What Is an AI Inference Platform?

An AI inference platform provides the technology for deploying machine learning models at scale. The term “inference” is used in the context of machine learning to describe the process of making predictions based on the trained knowledge of the model. One example is the classification of an image or the generation of text.

Platforms offer serverless architecture and Graphics Processing Units (GPUs) to ensure thousands of inferences can be processed in real-time. Platforms improve latency and throughput of systems through a variety of methods.

Businesses can provide real-time AI applications to consumers at an affordable price by using inference platforms. Examples of real-time AI applications include autonomous vehicles and other systems as well as recommendation systems and chatbots.

Key Features to Look for in an AI Inference Platform

Support for Varied Workloads

Supports various types of workloads including LLMs, and vision and audio workloads, if needed, and also supports deployment of custom workloads.

Varied Deployment Methods

Supports deployment of various applications through APIs, serverless computing, or through a managed infrastructure computing cluster.

Real Time Processing

Suppports processing of a large number of requests using techniques such as batch processing, and request caching, and also supports real time processing using request streaming.

Request Concurrency

Supports processing of a large number of requests, and also supports scaling of compute resources to zero when not needed.

Pricing

Charges based on a variety of models including a request charge, or a charge based on the compute resources (GPU time) consumed, or time a model is active.

Security and Compliance

Provides controls to manage customer data, and also supports varying levels of data governance required by different industries (e.g. Finance, Healthcare, Government).

Key Points

Platform	Best For	Key Point
Together AI	Broad catalog, OSS + proprietary	Flexible deployment (serverless + dedicated)
Fireworks AI	Compliance‑sensitive enterprises	Enterprise‑grade controls with fine‑tuning support
Groq	Latency‑critical workloads	Ultra‑low time‑to‑first‑token (~0.74s)
Baseten	Custom/proprietary model hosting	UI‑first deployment with strong infra control
DeepInfra	Cost‑sensitive OSS workloads	Lowest per‑token rates with wide OSS catalog
Replicate	Multimodal experimentation	Supports image, video, and audio inference
Anyscale	Complex pipelines on Ray	Seamless distributed framework integration
Modal	BYOC (bring‑your‑own‑container)	Code‑first deployment flexibility
RunPod	Raw GPU hosting	Cost‑efficient full‑stack GPU control
OpenRouter	Multi‑model routing	Avoids vendor lock‑in with flexible routing

1. Together AI

Together AI provides an inference platform to run both proprietary and public models. Its capability to run LLMs and multimodel workloads attracts different types of customers from enterprises to research. Its models can be run on the cloud, or through an API or a serverless architecture on a device equipped with a GPU.

Together AI’s models boast high throughput with cache and batch processing. To increase cost efficiency, the platform automatically scales up and down to zero based on usage. It charges based on two metrics.

For transatory work, customers are charged per token, and for batched or long-running work, customers are charged for GPU time. The company’s main point of differentiation is its flexible deployment of models based on customer needs.

Best For: Teams of all sizes with the budget for enterprise options that want to run both serverless and CPU‑accelerated workloads. Best for teams looking to balance both affordability and advanced configuration options.

Pros:

1. Broad OSS and proprietary model catalog
2. Support for serverless and GPU workloads
3. Advanced configuration for enterprise customers
4. Exceptional autoscaling and auto‑zero workloads

Cons:

1. Complex pricing (per token and GPU hour)
2. Variable latency with autoscaling
3. Fewer multimodal capabilities compared to Replicate
4. Infrastructure setup requires some learning curve

2. Fireworks AI

Fireworks AI models focuses on enterprise work in regulated industries. Like other models, it offers LLM and multimodal models. Models can be deployed via an API and managed GPU clusters.

Working toward decreasing tail latency gives Fireworks AI a differentiator from other models. It achieves this through batch and quantized processing. For long-running tasks, it uses a combination of hardware and software to optimize computational performance.

It charges per token, and for long-running work, customers are charged for GPU time. Its main point of differentiation is its focus on enterprises in finance, healthcare, and government.

Fireworks AI

Best For: Regulated enterprises needing robust compliance features in addition to reliable inference pipelines and fine‑tuning options.

Pros:

1. Advanced compliance solutions
2. Fine‑tuning
3. Consistent latency improvements
4. Managed GPU clusters

Cons:

1. More expensive than OSS options
2. Shallow model catalog
3. Slower Product Improvement
4. Inadequate multimodal capabilities

3. Groq

Groq offers models and architecture to run transformer-based models to run inference and training on large volumes of data. It’s built for various LLMs for running text-based agents and other structured data workloads. For enterprise use cases, customers can deploy models via API.

Time-to-first-token is less than one second on average. Custom hardware and runtime optimizations set their performance benchmarks. Their business model offers free access for developers and charges enterprises based on usage. Due to their focus on latency, they have become the industry standard for URGENT workloads.

Best For: Customers prioritizing latency for chatbots and other applications requiring AI interaction.

Pros:

1. Exceptional latency (< 1s TTFB)
2. Hardware acceleration
3. Scalability
4. Free Tier

Cons:

1. Shallow OSS catalog
2. Enterprise Tier pricing
3. No multimodality
4. Narrow focus

4. Baseten

Baseten provides a user interface for model deployment. They provide infrastructure for deployment of custom pipelines, LLMs, and multimodal models. Deployment is facilitated by managed GPU instances and API endpoints.

Speed and ease of deployment differentiate Baseten. Autoscaling and concurrency empower developers to fully control deployment infrastructure. Pricing is based on token usage and GPU time.

Best For: Users who want quick deployments for proprietary models.

Pros:

1. Rapid deployment
2. Infrastructure control
3. Support for custom end‑to‑end workflows
4. Model scaling

Cons:

1. Pricing model
2. No free tier
3. Per-minute billing can inflate costs
4. Smaller list of available tools

5. DeepInfra

DeepInfra provides cost effective inference for LLMs and multimodal models. They offer a broad library of models. Deployment is facilitated by serverless GPU instances.

Pricing and performance are strengths of DeepInfra. Batched inference and other performance improvements reduce the true cost of inference. Idle compute is automatically scaled to zero.

Best for: Teams and startups who use Open Source Large Language Models (LLMs) and want to minimize costs.

Pros:

1. Most affordable token rates
2. More diverse OSS LLM offerings
3. Clear pricing
4. Ease of use for serverless deployments

Cons:

1. Fewer enterprise compliance offerings
2. Less support for multimodal models
3. Weaker guarantees for tail latency
4. Less customization for infrastructure

6. Replicate

Replicate provides a multimodal inference API. Models for inference include audio, video, text, and images. Multimodal deployment is facilitated by Replicate’s containerized deployment units and APIs. Speed and breadth of offerings differentiate the platform.

Optimized for multimodal workloads. Batching and caching make for efficient pipelines. Support for concurrent workloads at scale. Price per second of compute for flexible cost control. Best-in-class multimodal support. Great for creating AI apps.

Best for: Workloads requiring multimodal LLMs in order to process inputs in multiple formats (e.g. text, image, video, audio, etc.).

Pros:

1. Strong offerings for multimodal LLMs
2. Provides container environments
3. Flexible workflows
4. Per-second billing

Cons:

1. Uncertain and fluctuating prices
2. Less enterprise compliance
3. Variance for latency in multimodal pipelines
4. Smaller OSS LLM models

7. Anyscale

Anyscale is a Ray-based platform for building and running end-to-end AI applications and intelligent systems. Anyscale provides a set of tools for building data pipelines and distributed computing workloads.

Along with traditional machine learning models, Anyscale allows users to incorporate Large Language Models (LLMs) and other advanced ML models in their workflows.

Anyscale’s strongest suit is its ability to integrate Ray for managing clusters and distributing workloads. Anyscale provides numerous features for managing Ray applications.

Because of its deep integration with Ray, Anyscale is a good choice for users that want to integrate advanced ML models in their distributed AI systems and applications.

Best for: Building distributed AI applications and workflows using the ray toolkit.

Pros:

1. Extended capabilities with ray
2. Balanced scheduling
3. Support for concurrent tasks
4. Flexible pipelines

Cons:

1.ray can be difficult to learn

2. Smaller catalog
3. Undocumented prices
4. Less support for multimodal LLMs

8. Modal

Modal is a developer focussed, code first, AI application development and deployment platform. It provides control to developers at the layer where AI application containerization is done and provides means to build and deploy custom application runtimes. Modal makes use of caching to alleviate latency in application development.

Additionally, it provides means to decouple computational load from the AI application development workbench. Modal provides an integrated development environment (IDE). Given its focus on development of high end AI applications with control in the hands of developers, it offers devops grade flexibility and control.

Best for: Developers wanting flexibility to code first and bring their own containers for deployment and usage.

Pros:

1. Flexibility to bring your own container
2. Code first deployment
3. Strong batching and caching
4. Pay per GPU

Cons:

1. Less enterprise compliance
2. Smaller OSS catalog
3. Requires additional scaling work by user
4. Less support for multimodal LLMs

9. RunPod

RunPod is a GPU hosting service. It provides testing and deployment services for large language models and other large models. It hosts dedicated GPUs and provides API endpoints for its customers.

It’susers have said thatRunPod’s performance has been limited by the GPUs that users could purchase. Some users reported batching and caching to improve performance. Manual and semi-automated scaling were available to some users, depending on their plan.

Because RunPod charges by the hour for the use of each GPU, users reported that it could be more cost-efficient for repeat, large batch processing. RunPod’s primary differentiator is that it provides clients with full control of GPUs.

Best for: Users needing an inexpensive and fully controlled approach to using raw GPUs.

Pros

1. Low-cost GPU hosting
2. Control of all infrastructure
3. Many deployment options
4. Hourly pricing transparency

Cons

1. Manual scaling
2. Few managed services
3. Smaller OSS catalog
4. Less enterprise compliance

10. OpenRouter

OpenRouter distributes requests to multiple inference providers. It currently works with various vendors for LLMs and other multimodal models.

It has been reported that OpenRouter’s performance is limited by the providers it works with. OpenRouter employs both batch and cache mechanisms. OpenRouter’s scaling mechanisms work across multiple providers.

OpenRouter charges a small premium over what the providers charge. OpenRouter’s main differentiator is that it provides clients with the ability to work with multiple vendors for inference.

Best For: Routed inference across many providers using a single API to prevent vendor lock-in.

Pros

1. Vendor flexibility
2. Multi-model/multi-provider routing
3. Transparent Pricing (markup)
4. Avoid lock-in

Cons

1. Performance is dependent on the vendor
2. No control over infrastructure
3. Weaker enterprise compliance
4. Limited OSS multimodel

Conclusion

AI inference platforms let companies put trained AI models into production to provide real-time predictions and other AI-related services. Some platforms give access to a range of AI services. Compared to other platforms, AI and Fireworks AI are more flexible and more compliant with regulations.

Groq is the best choice when latency is a concern. DeepInfra and Replicate are the cheapest options. Other platforms, like Baseten and Modal, are better choices for services that require raw computation, like custom ML pipelines.

RunPod and OpenRouter are good choices for AI services where latency is a concern. Other platforms like Anyscale and OpenRouter are good choices for vendors looking for routing and hosting services.

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