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Deploy LLM to Production: Platform Comparison & Costs

Deploybase · April 1, 2025 · LLM Guides

Contents

Deploy LLM to Production: LLM Deployment market

Deploying a language model to production requires choosing between managed platforms, self-hosted infrastructure, and API-based solutions. Each approach trades cost, control, and operational complexity differently.

Deploy LLM to production decisions hinge on several factors: inference latency requirements, cost sensitivity, model size, and team infrastructure expertise. Teams range from serverless function deployments to multi-node GPU clusters.

As of March 2026, production LLM hosting spans these categories:

Managed inference platforms handle scaling, monitoring, and load balancing automatically.

Self-hosted options provide maximum control but demand DevOps expertise.

API-first approaches eliminate infrastructure concerns but incur per-query costs.

Deployment Platform Options

Replicate provides serverless LLM inference. Users pay per API call with automatic scaling. No infrastructure management required.

Pricing: $0.00035 per second for Llama 2 7B, $0.0014 per second for Llama 2 70B. A model running for 10 seconds costs $0.0035-$0.014 per request.

Best for: Early-stage applications, experimental models, unpredictable traffic patterns.

Together AI offers batch processing and streaming inference APIs. Emphasis on cost-effective large-batch inference.

Pricing: $0.00015 per 1K tokens for Llama 2 7B, $0.0009 per 1K tokens for Llama 2 70B. 100K token inference costs $0.015-$0.09.

Best for: Batch processing, document processing pipelines, predictable workloads.

Baseten provides containerized model deployment with auto-scaling. Hybrid approach combining serverless simplicity with custom code.

Pricing: $0.50 per hour compute base, plus usage charges. A continuous inference service costs $360+/month plus API calls.

Best for: Custom model serving, complex preprocessing, medium-scale applications.

Runhouse enables distributed model serving. Self-hosted infrastructure with distributed compute abstraction.

Pricing: Infrastructure costs only (GPU rental). Runhouse platform is open-source. Self-hosted H100 inference costs $1.99/hour via RunPod.

Best for: Cost-sensitive deployments, on-premise infrastructure, complex multi-model serving.

vLLM + cloud hosting combines open-source serving framework with cloud compute. Users rent GPU instances and run inference themselves.

Pricing: GPU rental only. RunPod H100 at $1.99/hour serves 50-100 requests/hour, costing $0.02-$0.04 per request.

Best for: Maximum cost efficiency, custom inference optimization, on-premise flexibility.

Pricing Comparison

Real-world costs for Llama 2 70B deployment at 1,000 daily requests (30K requests monthly):

Replicate:

  • 1,000 requests at 10 seconds average = 10,000 seconds = $14/month
  • Highly efficient for low-volume inference

Together AI:

  • 1,000 requests at 2,000 output tokens = 60M tokens/month = $540/month
  • Cost scales linearly with output tokens

Baseten:

  • Base compute: $360/month (always-on container)
  • Plus token overage: $5-$15/month
  • Total: $365-$375/month

Runhouse on RunPod H100:

  • H100 at $1.99/hour
  • Assuming 50 requests/hour utilization
  • Cost per request: $1.99 / 50 = $0.04
  • Monthly cost at 1,000 requests/day = $1,200-$1,600

Self-hosted vLLM on RunPod H100:

  • Same H100 rental: $1.99/hour
  • Better throughput optimization = 80-100 requests/hour
  • Cost per request: $0.02-$0.025
  • Monthly cost at 1,000 requests/day = $600-$750

At low volumes (100 requests/day), Replicate wins ($1.40/month). At high volumes (10K requests/day), self-hosted vLLM becomes cheapest ($3,000-$4,000/month for dedicated H100).

Technical Requirements

Replicate and Together AI require minimal technical setup: API credentials, model selection, parameter tuning. Integration takes hours.

Baseten requires Docker containerization knowledge and understanding of async request handling.

Runhouse demands familiarity with distributed Python, GPU memory management, and networking configuration.

vLLM self-hosted requires expertise in Kubernetes or cloud VM management, load balancing, monitoring infrastructure.

Model size dictates infrastructure requirements:

  • Llama 2 7B: Single A100 (40GB) or L40S sufficient
  • Llama 2 70B: Single H100 (80GB) or A100 SXM required
  • Llama 2 70B + LoRA: Multi-GPU setup recommended

Latency requirements drive architecture decisions. Sub-100ms latency requires containerized serving on GPUs; sub-1000ms tolerates batch processing or queue-based approaches.

Scaling to Production

Early-stage deployments often use Replicate for simplicity. Cost structure shifts as volume scales:

0-1M requests/month: Replicate cost-effective; consider Together AI for token-intensive workloads.

1-10M requests/month: Baseten or containerized solutions become competitive. Fixed monthly costs dominate variable per-request pricing.

10M+ requests/month: Self-hosted vLLM on reserved GPU capacity wins. Reserved H100s at $1,600+/month support millions of inference requests.

Multi-model serving adds complexity. Baseten and Runhouse handle multiple models better than Replicate. Load balancing, fallback logic, and model versioning require careful architecture.

Visit /gpus for GPU selection guidance and /articles/self-hosted-llm for self-hosting considerations.

FAQ

What's the cheapest way to deploy a Llama 2 model in production?

Self-hosted vLLM on an H100 from RunPod costs $0.02-$0.04 per inference request at scale. For low volumes (under 1,000 requests/day), Replicate is cheaper at $0.00035 per second.

Should I use API-based solutions or self-host?

API solutions (Replicate, Together AI) are simpler to start but become expensive at scale. Self-hosting requires infrastructure expertise but is cheaper beyond 10K monthly requests. Hybrid approaches use API for prototyping, self-hosting for production.

Can I deploy a fine-tuned model on these platforms?

Replicate and Together AI support custom model uploads. Baseten excels at custom models through containerization. Self-hosted approaches support any model; fine-tuning integration is straightforward with vLLM.

What about compliance and data privacy?

Managed platforms send inference data to their servers. Compliance-sensitive workloads require self-hosting or production solutions with data residency guarantees. Some platforms offer private clusters for additional cost.

How do I minimize latency in production?

Use GPU-optimized serving (vLLM, TensorRT-LLM). Deploy close to users geographically. Batch inference offline when possible. For interactive latency, reserve GPU capacity; spot instances introduce unpredictable delays.

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