What infrastructure do you need to run AI agents in production?

How We Ranked These

We evaluated infrastructure options for running AI agents in production against five criteria: inference latency (sub-500ms for real-time agents), scalability (auto-scaling to 10,000+ concurrent agents), orchestration support (native agent workflow tools, not just raw compute), cost efficiency (per-agent-per-month pricing under $0.05 for high-volume use), and ecosystem integration (APIs for memory, tool use, and observability). We prioritized solutions that have been proven in production environments as of early 2027, with verifiable benchmarks from providers like MLCommons and CloudHarmony. Each option was stress-tested against a standard agent benchmark: a multi-step retrieval-augmented generation (RAG) agent handling 1,000 concurrent sessions. Only platforms with published SLAs for uptime and latency made the cut.

1. NVIDIA DGX Cloud 🏆 BEST OVERALL

NVIDIA DGX Cloud is a fully managed AI supercomputing service that provides dedicated DGX H100 or DGX B200 pods (each with 8 GPUs, 80GB HBM3 per GPU) in tier-1 data centers. For production AI agents, this means sub-10ms inference for LLMs like Llama 3 70B and Mistral Large, with native support for NVIDIA AI Enterprise (including NVIDIA NIM for model serving and NeMo for agent memory). Pricing starts at approximately $37,000/month per DGX pod (256 GPUs), but includes NVIDIA's Triton Inference Server for batching agent requests and Kubernetes-based orchestration via NVIDIA GPU Operator. This is best for teams running complex, multi-agent systems (e.g., autonomous customer support agents with 50+ tools) where latency and throughput are non-negotiable.

Concrete specs: Each DGX pod delivers 32 petaFLOPS of FP8 performance, enough to run 1,000 concurrent Llama 3 70B agents at 200ms response time. The service integrates with AWS, Azure, and Google Cloud for storage and networking, but the compute is physically hosted in Equinix IBX data centers with direct fiber to major clouds. For agent orchestration, you'll need to bring your own Kubernetes cluster (managed via NVIDIA AI Enterprise), but the platform includes NVIDIA Base Command for job scheduling and monitoring. The main drawback is cost—this is not for startups—but for enterprise-scale agent deployments (e.g., JPMorgan's trading agents or Siemens' industrial control agents), it's the only option that guarantees 99.99% uptime and sub-50ms tail latency.

2. Google Cloud Vertex AI Agent Builder

Google Cloud Vertex AI Agent Builder is a managed platform for building, deploying, and monitoring AI agents, built on Google Kubernetes Engine (GKE) with TPU v5p and A100/H100 accelerators. It offers a low-code agent designer for defining tools, memory, and conversation flows, with native integration to Google Search Grounding (for real-time data) and BigQuery (for agent analytics). Pricing is consumption-based: $0.10 per agent query for standard models (e.g., Gemini 1.5 Pro) plus compute costs for custom models. This is best for teams already on Google Cloud or those needing rapid prototyping—you can deploy a RAG agent with Vertex AI Search in under an hour.

Key specs: Vertex AI Agent Builder supports up to 10,000 concurrent agents with auto-scaling via GKE, and sub-300ms latency for Gemini models. It includes Vertex AI Model Garden for fine-tuning open-source models like Llama 3 and Mixtral 8x22B on your data, and Vertex AI Agent Monitoring for tracing agent decisions and cost per step. The platform also provides built-in guardrails via Vertex AI Safety (filtering toxic outputs) and Vertex AI RAG Engine for grounding agent responses in your documents. The trade-off: you're locked into Google's ecosystem, and custom model hosting on H100s can get expensive at scale (approximately $2.50 per GPU-hour for A100s).

3. AWS Bedrock + SageMaker

AWS Bedrock provides managed access to foundation models (including Claude 3.5 Sonnet, Llama 3 70B, and Amazon Titan) with Bedrock Agents for tool use and memory. Combined with Amazon SageMaker for custom model hosting, this stack offers serverless inference via AWS Lambda integrations and Amazon DynamoDB for agent state. Pricing: $0.013 per 1,000 tokens for Claude 3.5 Sonnet via Bedrock, plus SageMaker compute at $3.00 per A100-hour. Best for enterprises already on AWS needing FedRAMP High compliance or HIPAA eligibility.

Concrete specs: Bedrock Agents support multi-step reasoning with up to 20 tools per agent, and integrate with Amazon Kendra for RAG (indexing up to 10 million documents). For custom models, SageMaker HyperPod clusters with H100 GPUs can scale to 1,024 GPUs for training, and SageMaker Inference supports auto-scaling from 0 to 1,000 instances with sub-100ms cold start via Amazon Elastic Inference. The downside: agent orchestration is less mature than Vertex AI—you'll need to manage Amazon Step Functions for complex workflows and Amazon CloudWatch for monitoring, which adds operational overhead.

4. Microsoft Azure AI Studio + OpenAI

Microsoft Azure AI Studio offers a unified platform for building agents with OpenAI models (including GPT-4o and o3-mini) and Azure OpenAI Service for production deployment. It includes Azure AI Search for RAG, Azure Functions for tool execution, and Azure Kubernetes Service (AKS) for scaling. Pricing: $0.03 per 1,000 tokens for GPT-4o via Azure OpenAI, plus AKS compute at $0.50 per vCPU-hour. Best for organizations using Microsoft 365 or Copilot integrations, as agents can natively call Microsoft Graph APIs.

Key specs: Azure AI Studio supports up to 50,000 tokens per agent call with GPT-4o, and Azure OpenAI Service provides 99.95% uptime SLA with provisioned throughput units (PTUs) for guaranteed capacity (e.g., 100 PTUs for 1,000 concurrent agents). The platform includes Azure AI Content Safety for guardrails and Azure Monitor for observability. For custom models, Azure ND H100 v5 VMs (8x H100 GPUs, 80GB each) cost approximately $30 per hour per VM. The main limitation: agent orchestration is still evolving—you'll need Semantic Kernel or AutoGen for multi-agent systems, which adds complexity.

5. Modal (Serverless GPU)

Modal is a serverless GPU platform that runs Python-based AI agents with sub-second cold starts via container snapshots. It supports H100, A100, and L40S GPUs with auto-scaling to 1,000 concurrent runs and pay-per-second pricing (e.g., $0.002 per second for an A100). Best for teams running lightweight agents (e.g., LangChain or LlamaIndex workflows) that need burst capacity without managing Kubernetes.

Concrete specs: Modal provides 3TB of ephemeral storage per volume, 100 Gbps networking between containers, and built-in secrets management for API keys. It integrates with Hugging Face for model loading and Weights & Biases for monitoring. For agent orchestration, you can use Modal's function decorators to define steps (e.g., @app.function(gpu="H100")), and Modal's scheduler handles retries and timeouts. The trade-off: no native agent memory—you'll need Redis or MongoDB Atlas externally—and maximum 24-hour runtime per function, which may not suit long-lived agents.

6. Together AI (Inference API)

Together AI provides a serverless inference API optimized for open-source models (e.g., Llama 3 70B, Mixtral 8x22B, DBRX) with sub-100ms latency via FlashAttention-3 and 4-bit quantization. Pricing: $0.50 per 1M tokens for Llama 3 70B, with no GPU management required. Best for teams that want to offload inference entirely and focus on agent logic, especially for RAG agents that need high throughput.

Key specs: Together AI supports up to 128K context windows and batch inference for up to 64 requests at once. It provides tool-use endpoints (e.g., function_call parameter) for agent workflows, and Together AI's Embeddings API for vector storage (e.g., with Pinecone or Weaviate). The platform offers 99.9% uptime SLA and rate limits of 1,000 requests per minute for paid tiers. The downside: no custom model hosting—you're limited to their supported models—and no dedicated capacity for latency-sensitive agents (shared infrastructure can cause variability).

7. RunPod (Serverless GPU with Spot Pricing)

RunPod offers serverless GPU endpoints with spot pricing (up to 70% cheaper than on-demand) for running AI agents. It supports H100, A100, and RTX 6000 Ada GPUs with auto-scaling to 500 concurrent workers. Pricing: $0.79 per hour for a RTX 6000 Ada on spot, or $2.49 per hour for an A100 80GB. Best for cost-sensitive teams running agents with intermittent workloads (e.g., batch processing agents) that can tolerate occasional preemption.

Concrete specs: RunPod provides 1TB of persistent storage per pod, 10 Gbps networking, and template-based deployment for popular frameworks (e.g., vLLM, TGI, Ollama). For agent orchestration, you can use RunPod's REST API to spin up pods per request, or RunPod's Serverless Endpoints for auto-scaling. The platform includes Cloudflare integration for DDoS protection and Prometheus metrics for monitoring. The main risk: spot instances can be terminated with 2-minute notice, so you need checkpointing (e.g., save agent state to S3 every 5 seconds). Not suitable for real-time agents.

8. Replicate (Cloud API for Open Models)

Replicate provides a managed API for running open-source AI models (e.g., Llama 3, Stable Diffusion, Whisper) with pay-per-prediction pricing (e.g., $0.0001 per second for a 7B model). It supports webhooks for async agent workflows and custom model deployment via Cog (their container tool). Best for prototyping and low-volume agent deployments (under 10,000 calls per month).

Key specs: Replicate offers sub-500ms latency for small models (e.g., Mistral 7B) and 99.9% uptime. It integrates with LangChain and Vercel AI SDK for agent orchestration, and provides usage dashboards for cost tracking. The platform supports up to 8K context for most models and batch predictions for up to 100 inputs. The limitation: no dedicated GPUs—all inference is shared—so latency spikes are common during peak hours, and maximum 60-second timeout per prediction limits complex agent steps.

9. Lambda GPU Cloud (Bare Metal)

Lambda GPU Cloud provides bare-metal GPU servers (e.g., Lambda Blade with 8x H100 80GB) for running AI agents with full hardware control. Pricing: $2.99 per hour for an 8x H100 server (approximately $2,150 per month). Best for teams that need dedicated capacity for latency-sensitive agents and are comfortable managing Ubuntu + Docker environments.

Concrete specs: Lambda GPU Cloud offers 1.6 TB/s memory bandwidth per H100, NVLink for inter-GPU communication, and 100 Gbps Ethernet networking. Servers are hosted in US-West (California) and US-East (Virginia) data centers. For agent orchestration, you'll set up Kubernetes or Docker Compose manually, and use Lambda's API for provisioning. The platform provides root access and persistent storage (up to 10TB NVMe SSD). The trade-off: no managed services—you handle scaling, monitoring, and security—and minimum 24-hour rental period for most instances.

10. Banana (Serverless GPU for Python)

Banana is a serverless GPU platform for Python-based AI agents, supporting H100, A100, and T4 GPUs with sub-500ms cold starts. Pricing: $0.0005 per second for a T4, $0.002 per second for an A100. Best for small-scale agent prototypes (under 1,000 concurrent calls) that need simple deployment via GitHub integration.

Concrete specs: Banana provides 1GB of persistent storage per model, 50 concurrent requests per deployment, and built-in logging via Grafana. It supports custom Dockerfiles for dependencies and REST API endpoints for integration. For agent orchestration, you can use Banana's Python SDK (banana.dev) to call models from LangChain or AutoGen. The platform offers free tier (up to 10 hours per month) for testing. The limitations: no auto-scaling beyond 50 concurrent requests, no GPU memory guarantees (shared infrastructure), and maximum 15-minute timeout per request—not suitable for long-running agents.

Related on PULSE

• [How do you handle model rollbacks safely in production?](/knowledge/ai429)
• [How do you A/B test different LLMs in production?](/knowledge/ai391)
• [How do you choose a vector database for a production RAG system in 2027?](/knowledge/ai339)
• [How do you monitor LLMs in production for drift and hallucinations?](/knowledge/ai355)
• [How do you secure an LLM application’s infrastructure?](/knowledge/ai363)
• [What infrastructure do you need for fine-tuning versus RAG?](/knowledge/ai427)

FAQ

What is the minimum compute required to run a single AI agent in production? For a basic agent handling simple queries, a single GPU like an NVIDIA A100 or H100 with at least 40GB VRAM is typically sufficient. For agents needing real-time responses or multiple model calls, you’ll want at least 80GB VRAM and a CPU with 8+ cores.

Can I run AI agents on CPU-only servers? Yes, for very small or text-only agents with low throughput, CPU inference is possible but often 10–100x slower than GPU. For latency-sensitive production, GPUs are strongly recommended; expect 1–5 second delays per query on CPU versus under 500ms on GPU.

How much memory and storage do AI agents typically need? RAM requirements range from 16GB for simple agents to 64GB+ for those with large context windows or multiple models. Storage is usually 100GB–1TB for model weights and logs, but can grow quickly with agent memory or conversation history.

What networking setup is needed for multi-agent systems? Low-latency networking (e.g., InfiniBand or 100 Gbps Ethernet) is essential when agents communicate frequently or share model state. For cloud setups, ensure your instances are in the same region and availability zone to minimize latency under 1ms.

Is Kubernetes required for orchestrating AI agents? Not strictly, but it’s highly recommended for scaling, fault tolerance, and managing multiple agent instances. Alternatives include Docker Compose for small setups or managed services like Vertex AI Agent Builder that abstract orchestration away.

How do I handle cost unpredictability with GPU-based agents? Costs vary widely: spot instances can reduce GPU costs by 60–90% but risk interruptions, while reserved instances offer stable pricing. For a single agent, expect $0.50–$5 per hour for GPU compute, plus storage and networking fees.

Sources

• NVIDIA DGX Cloud official page
• Google Cloud Vertex AI Agent Builder docs
• AWS Bedrock Agents documentation
• Azure AI Studio overview
• Modal serverless GPU platform
• Together AI inference API
• RunPod serverless GPU pricing
• Lambda GPU Cloud bare metal
• Banana serverless GPU
• MLCommons AI benchmarks

Bottom Line

For production AI agents, NVIDIA DGX Cloud is the best overall choice for high-throughput, multi-agent systems requiring dedicated H100/B200 GPUs and sub-10ms inference, while Google Cloud Vertex AI Agent Builder offers the best managed experience for rapid deployment with built-in orchestration. For cost-sensitive teams, RunPod with spot instances or Together AI for inference API provide viable alternatives. Always test with your actual agent workload before committing—latency and cost can vary 10x based on model size, concurrency, and tool complexity.

*AI agents production infrastructure GPU cloud serverless inference agent orchestration*