Every AI product starts at $0. Free API credits, Google Colab notebooks, and trial GPU instances get you from idea to prototype without a credit card. But the jump from free tier to production is where most teams miscalculate — suddenly facing $200-$2,000+ per month in infrastructure costs they did not plan for. This roadmap maps the exact cost at each stage so there are no surprises.
Stage 1: Prototype ($0/month)
At the prototype stage, you are testing feasibility. Free resources are sufficient and appropriate. OpenAI gives $5-$18 in free API credits. Google Colab offers free T4 GPU access with session limits. HuggingFace Inference API has a free tier for low-volume testing. Ollama runs small models on your laptop CPU. The goal here is not cost optimisation — it is learning what works. Spend $0, validate your idea, then plan the next stage.
Typical duration: 2-6 weeks. Monthly cost: $0.
Stage 2: Development ($15-$100/month)
You have validated the concept and need reliable GPU access for development. On-demand cloud GPUs (RunPod, Lambda Labs, Vast.ai) cost $0.30-$1.50 per hour. At 2-4 hours of daily development, that is $18-$180 per month. API providers with pay-as-you-go pricing (OpenAI, Anthropic) cost $15-$80 per month at development-level usage — a few hundred queries per day for testing.
| Resource | Cost | Use Case |
|---|---|---|
| OpenAI API (dev usage) | $15-$50/mo | Testing prompts, building pipelines |
| Cloud GPU (sporadic) | $30-$100/mo | Fine-tuning experiments, benchmarks |
| HuggingFace Pro | $9/mo | Inference API, model hosting |
| Total | $15-$100/mo |
Typical duration: 1-3 months. Use the cheapest GPU guide to select development hardware.
Stage 3: Beta/Staging ($100-$400/month)
Your product has beta users. You need a persistent inference endpoint that does not shut down between sessions. This is the stage where the self-hosting break-even question becomes relevant. At 1,000-10,000 queries per day, a dedicated GPU is often cheaper than API pricing.
| Approach | Monthly Cost | Best For |
|---|---|---|
| API-only (GPT-4o-mini) | $50-$200 | Quick launch, variable traffic |
| Shared GPU (RTX 5090) | $90-$130 | Consistent low-volume inference |
| Dedicated GPU (RTX 5090) | $180 | Predictable cost, full control |
Typical duration: 1-3 months. Start comparing with the GPU vs API comparison tool.
Stage 4: Production ($180-$800/month)
You have paying customers. Reliability matters. Latency matters. Cost predictability matters. This is where dedicated GPU hosting becomes the clear choice. A single RTX 5090 handles most 7B-13B model workloads. An RTX 6000 Pro 96 GB handles 70B models or high-concurrency 7B workloads. Deploy with vLLM for production-grade serving.
| Workload | Recommended GPU | Monthly Cost | Queries/Day Capacity |
|---|---|---|---|
| 7B model, moderate traffic | RTX 5090 | $180 | 50,000+ |
| 7B model, high traffic | RTX 6000 Pro 96 GB | $420 | 150,000+ |
| 70B model, moderate traffic | RTX 6000 Pro 96 GB | $420 | 20,000+ |
| 70B model, high traffic | 2x RTX 6000 Pro 96 GB | $840 | 60,000+ |
Stage 5: Scale ($800-$5,000+/month)
Traffic has grown beyond a single GPU. You need load balancing, redundancy, and possibly separate GPUs for different pipeline stages (embedding, reranking, generation). The TCO analysis at this stage shows dedicated hosting saves 50-80% versus cloud GPU instances. Multi-model serving, auto-scaling, and geographic distribution become relevant.
At this stage, the API equivalent spend exceeds $10,000-$50,000 per month. Every month you delay self-hosting at scale costs thousands in unnecessary API fees. Use the LLM cost calculator to model your specific growth trajectory.
Start Your Journey on GigaGPU
GigaGPU meets you at every stage of the roadmap. Start with open-source LLM hosting for your beta deployment, scale to dedicated GPU hosting for production, and grow into private AI hosting when compliance demands it.
Our UK-based infrastructure provides consistent low-latency performance with predictable monthly billing — no hourly surprises as you scale. Estimate your production costs with the LLM cost calculator and browse more scaling cost analyses on the cost blog.
