Comparing 6 Leading Platforms for Hosting Autonomous AI Agents in 2025
When you need an army of minions but are also on a budget
If there is something I have learned over the last 48 months of building agents, is that it’s expensive, from a CapEx perspective, to run inference locally. Running those services online might be cheaper, as we can expect that the costs of running agents in serviced environments to go down as more compute is added to data centers around the world.
In this post, I am trying to provide a light comparison of several platform providers I thought were interesting and their respective agent products.
Here are my 6 vendors of choice:
To evaluate their suitability for this purpose, I use the following criteria
Support for agent frameworks and custom workflows,
Flexibility in changing the underlying LLMs to mitigate vendor lock-in, and
The pricing structures offered by each platform.
In general, I think that should give a high-level frame of reference, helping to compare the different providers. But of course, your choice of the most appropriate platform ultimately depends on your specific requirements, technical expertise, and budget constraints.
So you want to build an agent?
In general, you’d want your agent swarms (teams of agents?) to interact with human users when needed, exchange information with other AI agents efficiently as part of autonomous operations, access various systems through tools to complete their plan and reach their goal, and get rewarded.
Agents are a fascinating technology. The increased interest in and adoption of agent technology stems from their potential to augment human capabilities, streamline daily operations, and even collaborate as virtual employees.
And specifically, the last one is a game changer.
As we all know, for an autonomous agent to function effectively, several key components are essential.
But at minimum, it should be this (for 1 unit of agent).
A planning module that enables the agent to break down complex goals into actionable steps,
An LLM capable of reasoning to “think” through a problem,
A memory system for retaining context and the various types of past experiences, and,
The capability to utilize tools such as APIs or databases to interact with the external world.
The growing prevalence of AI agents in professional environments, as evidenced by a study indicating that over half of professionals were already using them, highlights the critical need for reliable and efficient platforms to host and manage these sophisticated systems.
However, given all these complexities, actually running a fast, accurate, and reliable multi-agent setup is not easy.
Why are hosting platforms so important?
In your agent architecture, you will likely use a selection of Large Language Models to serve as the core intelligence of your autonomous agents. Of course, you can use the same LLM everywhere, but in general, there are tasks where smaller, less expensive models are better to use. I.e., simple worker-drone document search vs reasoning and report writing.
Hosting platforms are important as they provide the underlying infrastructure required to run and scale these computationally intensive models, offering services such as managed compute resources, API access, and deployment tools. Also, don’t underestimate security considerations. For you, it should be critical how well these providers are able to run your agent framework. As these frameworks provide a structured approach for building, managing, and operating LLM-based agents, the need for efficient integration is obvious.
A crucial aspect for you, developing autonomous agents, will be the ability to manage model risk. I.e., switch between different LLMs. This is important as prices, capabilities, and availability change. I expect we will reach a point of maturity over the next 18-24 months, but we might also see innovative breakthroughs no one expected. Having this flexibility helps avoid vendor lock-in.
Platform Exploration
Hugging Face
Hugging Face has established itself as a central hub for the open-source machine learning community, offering a vast repository of pre-trained models and a comprehensive suite of tools for developing and deploying AI applications.
Its platform is widely recognized for its extensive collection of Natural Language Processing (NLP) models, including state-of-the-art Transformers, which are fundamental to building intelligent autonomous agents.
Hugging Face provides significant support for building autonomous agents through its transformers and, more recently a focuses “smolagents” library. Besides the dumb name, this library offers functionalities that enable developers to implement crucial agent capabilities easily. Smolagents specifically also accommodates the development of multi-agent systems, where multiple specialized agents can collaborate to solve complex problems. Beyond its agent libraries, Hugging Face integrates with a visual workflow automation platform like n8n. This integration allows for the creation of sophisticated and intelligent agent workflows by connecting Hugging Face's powerful language models with n8n's automation capabilities, enabling the construction of chatbots, data querying assistants, and other autonomous systems.
So far for orchestration. How about hosting?
Hugging Face offers several options for hosting the LLMs that power autonomous agents. My favorite is Inference Endpoints, which provide fully managed infrastructure for deploying models.
Another method is Spaces, which offers a more community-oriented platform for sharing and running ML applications. In my opinion, a key advantage of using Hugging Face is the ease to switch between different models available on the platform. The platform's architecture inherently promotes this flexibility, making it a strong contender for developers who are evaluating models and therefore prioritize the ability to easily change and compare different underlying LLMs (and other models).
Hugging Face's pricing structure is designed to accommodate a wide range of use-cases. Inference Endpoints offer a pay-as-you-go model, where users are billed based on the compute resources and time their endpoints are active. Dedicated instances are also available for more demanding workloads. Spaces provides a free tier with basic CPU resources, allowing individuals and small teams to get started without incurring costs. For users requiring more computational power or persistent storage, upgraded plans are available. Hugging Face offers a Pro subscription that provides enhanced features such as increased private storage, advanced collaboration tools, and priority support.
Replicate
Replicate is a cloud-based platform specifically designed to simplify the process of running, fine-tuning, and deploying open-source machine learning models. While Replicate itself is not an agent framework, it provides a layer for serving the models that underpin autonomous agents built with other frameworks. So it might be worthwhile considering if you want to roll your own. Similar to Hugging Face, these models can be accessed and utilized with minimal code. Replicate leverages containerization technology to ensure the scalable and cost-effective deployment of both its pre-built models and custom models brought by users. This flexibility addresses the need to avoid vendor lock-in
Replicate employs a straightforward pay-for-what-you-use pricing model.
Users are billed based on the actual compute time consumed while running their models, with different rates for CPU and various tiers of GPU resources. The platform automatically scales up or down to handle fluctuations in demand, ensuring that users only pay for the compute resources they are actively utilizing.
This usage-based pricing model offers transparency and can be particularly cost-effective for projects with variable workloads or those in the experimentation phase.
Fireworks AI
Fireworks AI is also a platform designed for running efficient inference for AI models. Fireworks AI offers key functionalities that support the development of autonomous agents. While Fireworks does not outright support agents, it provides support for function calling. Fireworks AI supports the fine-tuning of open-source models, allowing you to customize them with your own data for improved performance on specific use cases. Fireworks AI employs a fully pay-as-you-go pricing model for its serverless text model inference, with costs determined by the number of tokens processed.
Seems cheap enough
Google Vertex AI
Google Vertex AI is a comprehensive platform offered on Google Cloud designed to support the entire agent lifecycle, from data preparation to model deployment and monitoring. Vertex notably supports Vertex AI Agent Builder, which, together with Agent Development Kit (ADK), simplifies agent creation. Agent Garden offers a way to start agent development from a template. The relatively new Agent2Agent (A2A) protocol enables cross-platform interoperability, allowing agents built on different frameworks or by different vendors to collaborate, critical for scaling multi-agent systems.
Vertex AI also limits LLM vendor lock-in by letting users choose models from its Model Garden or plug in alternatives. This includes support for custom deployments and retrieval-augmented generation (RAG).
From a pricing perspective, usage is metered (pay as you go) per vCPU and GiB-hour, while model usage is based on token input/output as listed in the Model Garden. Tool and prebuilt agent costs depend on integration specifics. Though not the cheapest, the platform offers a pricing calculator and custom quote options.
They also seem to be working on an agent marketplace, but it doesn’t seem to do much as of May 2025.
Azure AI
Offered by Microsoft, Azure AI is an early partner to OpenAI and, most importantly, is tightly integrated with the broader Microsoft ecosystem, making it a suitable choice for Enterprise organizations already invested in Microsoft products and services. But there is more. Azure AI offers an interesting approach to supporting the development of autonomous agents natively.
Azure AI Agent Service is a fully managed service designed to empower developers to securely build, deploy, and scale high-quality AI agents without needing to manage the underlying compute and storage resources.
Azure AI Foundry serves as a platform for designing, customizing, and managing AI applications and agents, providing a broad set of AI capabilities and tools through a unified portal, SDK, and APIs.
Furthermore, Microsoft's Semantic Kernel is an open-source SDK that enables developers to build AI agents that I haven’t tried yet. Together with Autogen, this trinity of managed services and flexible SDKs provides a range of options.
While Azure AI provides default support for OpenAI models and a growing catalog of other models, the process of integrating and switching to LLMs from completely different, non-Microsoft-aligned providers might require more effort compared to platforms with a greater emphasis on open-source model flexibility.
The pricing for Azure AI services is structured at the deployment level, meaning that users are billed for each specific AI service and model they consume.
An estimator is here.
It's worth noting that while the Azure AI Foundry platform itself is free to use, the underlying AI services and compute resources that power autonomous agents are billed according to their respective pricing models.
For Azure OpenAI Service, pricing is primarily based on the number of tokens processed, with different rates for various models and the option for batch API usage, which can offer cost savings for certain workloads.
Azure AI also charges for AI-assisted evaluations used to assess the quality and safety of AI applications, as well as for the use of built-in tools like Computer Use and File Search.
AWS Bedrock
Amazon Web Services Bedrock (Agents) provides access to a selection of high-performing foundation models, including an effective framework to access models from various foundation providers. Amazon Bedrock Agents specifically allows developers to build and configure autonomous agents for their applications.
Bedrock Agents offer features such as action groups, which enable developers to define the actions their agents can take, and support multi-agent collaboration. Furthermore, Bedrock allows for the integration of knowledge bases to augment the agent's responses with relevant information.
The pricing for AWS Bedrock is primarily on-demand and based on the number of input and output tokens processed by the chosen foundation model without any time-based term commitments. The specific rates vary depending on the model selected.
Bedrock agents can also be run in “batch mode” where you can provide a set of prompts as a single input file and receive responses as a single output file.
On-demand pricing for Deepseek would look like this:
In closing
The table illustrates the diverse approaches taken by each platform.
My journey into agents evolved largely around Hugging Face. Hugging Face provides a highly flexible, code-centric environment, empowering developers with fine-grained control. But the main tech vendors have caught up. Google Vertex AI, Azure AI, and AWS Bedrock seem by far more advanced and offer more managed, enterprise-ready solutions with varying degrees of integration into their respective cloud ecosystems. Replicate focuses on simplifying model serving, making it a strong choice for running a wide range of open-source models.
Recommendations
Selecting the most suitable platform for hosting LLMs with agent frameworks for autonomous agents requires careful consideration of the specific needs and priorities of the organization. For research and experimentation, platforms like Hugging Face, Replicate, or Fireworks provide solid starting points. These platforms allow developers to explore different models and agent architectures without significant upfront costs.
For organizations requiring enterprise-grade deployments with robust scalability, security, and integration with a comprehensive cloud ecosystem, Google Vertex AI, Azure AI, and AWS Bedrock are unavoidable. These platforms offer a wide range of services and enterprise-level support, making them suitable for mission-critical applications.
Ultimately, the decision which provider to select should be based on a thorough evaluation of the team's technical expertise, the organization's existing cloud infrastructure (if any), the specific requirements of the autonomous agents being developed, and the allocated budget. It is often beneficial to experiment with different platforms and frameworks to determine the best fit for a particular use case.
The findings indicate that while all platforms offer solutions for hosting LLMs and supporting agent development, they differ significantly in their strengths, weaknesses, and target user bases. Platforms like Hugging Face, Replicate, and Fireworks AI stand out for their flexibility in model selection and open-source focus, while Google Vertex AI, Azure AI, and AWS Bedrock provide more comprehensive, enterprise-grade ecosystems tightly integrated with their respective cloud services.
