Building autonomous AI agents is hard enough without infrastructure problems getting in the way. Last month, I was helping a client debug their agent system when we realized their hosting platform simply couldn’t handle the memory requirements. We ended up spending weeks refactoring code that should have just worked.
That’s the frustrating reality of agent deployment in 2025. Writing good agent code is only half the battle - you also need infrastructure that can keep up with agents that need to think, remember, and act on their own terms.
In this comparison, we’ll look at six platforms for hosting autonomous agents, with a focus on what each does best and where they fall short. While we developed Raindrop, we’ve done our best to present a balanced view of all platforms based on their technical capabilities.
Before diving into specific platforms, it’s important to understand why specialized infrastructure for autonomous agents matters. Unlike traditional applications or even simple AI chatbots, autonomous AI agents require:
When autonomous agents work with customer data or make business-critical decisions, the underlying agentic AI infrastructure must provide both flexibility and control. Human oversight becomes crucial, especially in dynamic environments where agents need supervision to ensure they’re meeting customer needs.
We’ve evaluated each platform based on these important factors for agentic AI hosting:
Raindrop is our platform specifically designed for agentic AI infrastructure. It is built with a declarative approach to application development - you define your agent system in a manifest file - and GitHub-style versioning for both code and data.
SmartBuckets is one of its first smart technologies: object storage with built-in AI capabilities for retrieval augmented generation and agent search. This eliminates most of the data pipeline work required on other platforms. As a newer offering in the market, Raindrop focuses mostlly on supporting agentic workloads.
Raindrop offers tiered pricing with a free developer tier for experimentation. Professional and Enterprise tiers scale with usage. Unlike other platforms where vector operations and storage are billed separately, the pricing includes these AI-enhanced features, which makes cost prediction more straightforward.
Raindrop works well for teams building sophisticated multi-agent systems, AI-native applications with RAG components, and business agents that need versioning and governance. The integrated approach can speed development for teams that don’t want to stitch together multiple services.
Vercel is a popular serverless platform focused on web applications, particularly Next.js deployments. While not explicitly designed for agentic AI, it provides a developer-friendly environment for deploying front-end integrated AI features like chatbots or virtual assistants. Vercel excels at delivering global, low-latency experiences, but its serverless functions are inherently short-lived by design, which limits the autonomous agents’ ability to perform long-running tasks without human intervention.
Vercel offers a Hobby (free) plan and a Pro plan at $20 per user/month. The Pro plan includes generous serverless execution quota (~1,000 GB-hours of function time per month). Beyond included limits, costs are $0.18 per GB-hour and $0.0000065 per edge function invocation. Additional bandwidth and storage add-ons are priced separately.
Vercel is ideal for AI assistants embedded in websites or apps and on-demand agents handling user queries. It works well for lightweight intelligent agents that can be split into short function calls, such as documentation chatbots or e-commerce assistants that improve customer satisfaction. It’s less suited for goal-based agents that perform complex tasks requiring continuous runtime without human supervision.
Cloudflare Workers is a serverless platform that can be used for agentic AI applications through its edge network capabilities and Durable Objects for state management. Workers runs JavaScript/TypeScript functions globally and offers Workers AI for accessing generative AI models. With execution times up to 15 minutes per invocation, it can support agent workflows that require multiple steps or external integrations. However, building full agent systems requires custom development work to handle data processing and orchestration.
Cloudflare Workers has a generous free tier (~100,000 requests per day). The paid Bundled plan costs $5/month for 10 million requests, with additional requests at $0.50 per million. Unbound mode pricing is based on CPU time at roughly $0.125 per million CPU-milliseconds. Workers AI usage follows a pay-as-you-go model based on model complexity and latency requirements. Notably, Cloudflare doesn’t charge for outbound data transfer.
Cloudflare Workers can support edge-deployed AI assistants that need fast responses, agents maintaining conversational state through Durable Objects, and event-driven automations that react to triggers. It works for concurrent workloads like agents handling customer interactions to identify patterns and deliver responses, though with some limitations around runtime and memory.
AWS provides a comprehensive set of building blocks for deploying agentic AI at any scale. The cornerstone for serverless agents is AWS Lambda with up to 15 minutes per execution, orchestrated by Step Functions for multi-step workflows. This approach enables each reasoning step or tool invocation to run as a separate Lambda function, with Step Functions handling the decision making in a durable way. AWS also offers Bedrock for managed large language models access and a rich selection of databases and event services for agent memory and communication between other agents. Its advanced infrastructure is particularly well-suited for enterprise-grade autonomous AI agents that require human oversight and robust security measures. The primary challenge with AWS is the significant integration work required across multiple services, with developers responsible for creating data pipelines, managing state, and handling versioning.
AWS pricing is pay-per-use with extensive free tiers. Key services include: Lambda (first 1M invocations free, then $0.20 per million requests and $0.00001667 per GB-second), Step Functions (first 4,000 state transitions free, then $0.025 per 1,000 transitions), and various data storage services. Bedrock model inferencing is charged per model usage, similar to direct API pricing from model providers. Overall, costs scale with usage from mere dollars to potentially thousands per month for complex AI systems with many intelligent agents.
AWS is ideal for large-scale enterprise AI agent deployments, agents integrated with complex backend workflows, and use cases requiring high reliability and security. It is good at multi-agent orchestration where different services play specialized roles coordinated by Step Functions. It’s a good option for mission-critical agentic AI systems that handle business data and require both machine learning capabilities and human supervision to address real-world challenges.
Google Cloud leverages its artificial intelligence expertise to provide strong hosting options for agentic systems. Cloud Run enables deploying containerized applications that can run up to 60 minutes per request, while Cloud Workflows orchestrates multi-step processes. GCP also offers Vertex AI Reasoning Engine, a managed runtime for agent frameworks like LangChain, and excellent data cloud services for analysis and storage. The platform balances flexibility with managed AI services, making it attractive for data-centric autonomous agents that need to analyze customer data, apply natural language processing, and generate valuable insights without constant human intervention. Managing the complete agent lifecycle typically requires orchestrating multiple separate systems for execution, storage, and AI processing, adding complexity to sophisticated agent architectures.
Google Cloud offers usage-based pricing with generous free tiers. Cloud Run provides 2 million requests, 360,000 GB-seconds, and 180,000 vCPU-seconds free monthly, with pay-per-use beyond that. Cloud Workflows is very affordable: first 5,000 steps per month free, then $0.01 per 1,000 steps. Vertex AI’s generative model APIs are charged per text token. Google provides $300 in initial credits, making experimentation accessible for developing autonomous AI agents.
GCP works well for orchestrated AI workflows and data-centric agents, agents leveraging Google’s AI models, and scenarios requiring containerized code execution. It’s particularly well-suited for model-based agents that interact with Google ecosystem services, background processors that run on schedules, and organizations looking to benefit from Google’s advancing AI capabilities to address customer needs in dynamic environments.
Azure offers a powerful cloud platform with distinctive advantages for enterprises with Microsoft-centric IT and solutions requiring strong security compliance. For agentic AI, Azure Durable Functions enables writing stateful workflows that can yield and resume, effectively maintaining long-running processes across checkpoints. Combined with Azure OpenAI Service for enterprise-grade access to OpenAI models, it creates a compelling platform for production-grade autonomous AI agents that can be seamlessly integrated with business data systems. This infrastructure supports both virtual assistants and complex goal-based agents while maintaining human oversight through robust security measures. The primary challenges with Azure include the steep learning curve for Durable Functions and the need for significant custom development to create complete agent ecosystems.
Azure Functions pricing follows a similar model to AWS Lambda: 1 million free executions monthly, then ~$0.20 per million plus ~$0.000016/GB-second. Durable Functions add minimal overhead for storage transactions. Azure OpenAI is billed per 1,000 tokens at rates comparable to OpenAI’s direct pricing. Enterprise discounts and dev/test rates are available, and the primary cost driver for most deployments will be language model inference rather than the orchestration layer.
Azure is good for enterprise and business process agents, agents requiring deep Microsoft service integration, and long-running autonomous processes needing fault tolerance. It’s the natural choice for organizations with Microsoft-centric environments, agents utilizing advanced language models in secure environments, and AI copilots for internal business tools that benefit from Azure’s identity management and compliance features. Its utility function design makes it particularly well-suited for learning agents that enhance financial transactions with optimal performance.
Understanding how autonomous AI agents function on each platform reveals important differences in their implementation:
These implementation differences affect how well each platform handles autonomous agents working on repetitive tasks versus complex decision making. While all platforms can support generative AI applications, their approaches to state management, execution duration, and tool integration vary significantly.
When deploying autonomous AI agents, the underlying physical infrastructure matters significantly. Here’s how different data center approaches affect your agents:
Choosing the right data center strategy is crucial for autonomous AI agents that require specific hardware for machine learning or need to maintain regional compliance when processing business data. The physical infrastructure directly impacts both the agent’s performance and its compliance with data privacy regulations.
Each platform offers different approaches to integrating advanced AI capabilities for autonomous agents:
The integration options directly impact how autonomous AI agents can utilize large language models, process customer interactions, and deliver AI solutions that truly meet customer needs. Advanced tools for model monitoring enable human supervision of agent behavior.
Securing autonomous agents requires addressing several unique challenges:
These security measures become particularly critical when autonomous AI agents handle financial transactions, access sensitive customer information, or make decisions that affect customer satisfaction. Robust security measures must be implemented to provide appropriate human oversight while still allowing agents to operate autonomously.
| Feature | Raindrop | Vercel | Cloudflare | AWS | GCP | Azure |
|---|---|---|---|---|---|---|
| Max Runtime | ★★★★☆ 15min | ★★☆☆☆ 5-15s | ★★★★☆ 15min | ★★★★☆ 15min | ★★★★★ 60min | ★★★★☆ Durable |
| State Management | ★★★★★ Versioned | ★★☆☆☆ External only | ★★★★☆ Durable Objects | ★★★★☆ Step Functions | ★★★★☆ Firestore | ★★★★★ Durable Entities |
| AI Model Integration | ★★★★★ Native + External | ★★☆☆☆ External APIs | ★★★★☆ Workers AI | ★★★★☆ Bedrock | ★★★★★ Vertex AI | ★★★★★ Azure OpenAI |
| Workflow Orchestration | ★★★★★ Declarative | ★★☆☆☆ Limited | ★★★★☆ Durable Objects | ★★★★★ Step Functions | ★★★★☆ Cloud Workflows | ★★★★★ Durable Functions |
| Developer Experience | ★★★★★ Declarative | ★★★★★ Excellent | ★★★★☆ Very Good | ★★★☆☆ Complex | ★★★★☆ Good | ★★★☆☆ MS-Focused |
| Enterprise Ready | ★★★★★ Full Governance | ★★★☆☆ Limited | ★★★☆☆ Emerging | ★★★★★ Comprehensive | ★★★★☆ Strong | ★★★★★ Excellent |
| Cost Efficiency | ★★★★☆ Inclusive | ★★★☆☆ Good for small | ★★★★★ Excellent | ★★★☆☆ Complex | ★★★★☆ Good value | ★★★☆☆ Enterprise |
| Platform | Killer Feature | Best Use Case | Pricing | Ease of Use |
|---|---|---|---|---|
| Raindrop | Declarative design with versioned data | Mission-critical agent ecosystems | Free tier and enterprise pricing | ★★★★☆ |
| Vercel | Frictionless deployment | Web-embedded assistants | $0-20/mo + usage | ★★★★★ |
| Cloudflare | Edge computing with stateful objects | Global low-latency agents | $0-5/mo + usage | ★★★★☆ |
| AWS | Comprehensive service ecosystem | Enterprise orchestration | Usage-based | ★★★☆☆ |
| GCP | 60-min container runtime | Data processing agents | Usage-based | ★★★★☆ |
| Azure | OpenAI + Microsoft ecosystem | Corporate agents with compliance needs | Usage-based | ★★★☆☆ |
Each platform has its strengths, and the right choice depends heavily on your specific requirements:
Stateless hosting (like basic serverless functions) requires external storage for agent memory between invocations, while stateful hosting maintains context natively. Stateful platforms significantly simplify building autonomous agents that need to maintain conversation history, reasoning chains, or tool outputs across multiple steps without disrupting the customer experience. The most advanced platforms provide versioning for both compute and data for comprehensive stateful agent development.
Traditional serverless platforms impose execution time limits (seconds to minutes), so truly continuous processing requires breaking work into chunks. Platforms with longer execution windows (60+ minutes) or persistent workflows are better suited for lengthy processes where autonomous agents work on complex tasks with minimal human intervention. The most sophisticated platforms offer observer-based architectures that enable effectively unlimited runtime by automatically managing checkpoint states and resuming operations in response to events.
For low to moderate volume simple agents, platforms with minimal overhead and no data transfer fees offer good value. For comprehensive agent systems with data intelligence needs, all-inclusive pricing models often result in lower total cost of ownership despite higher base prices, as they eliminate the need for multiple separate services and custom integration work. For very high volume predictable workloads, the major cloud providers can be optimized with reserved instances.
Multi-agent systems benefit from platforms with robust orchestration capabilities or actor models. Additionally, efficient message passing mechanisms become increasingly important as agent counts increase. These systems help manage multi-agent dependencies and prevent infinite feedback loops when autonomous AI agents interact with each other. Platforms with declarative approaches to defining agent relationships provide the most straightforward path to building cooperative multi-agent systems.
Edge deployment becomes critical when your agents need to minimize latency for user interactions or process data close to its source. For internal business process agents or batch processing, regional cloud deployment is often sufficient. The decision should be based on whether your autonomous agents require real-time customer interactions or can operate with some latency while performing complex tasks. Ideally, your chosen platform should support both centralized and edge deployments depending on your specific needs.