Choosing between serverless and dedicated GPU infrastructure is one of the most impactful cost decisions in AI deployment. Get it wrong and you waste money on idle hardware or frustrate users with cold start delays. The right answer depends on how many hours per day you run inference, how latency-sensitive your application is, and whether traffic is steady or bursty.
How serverless GPU works
Providers like Replicate, Modal, and RunPod Serverless follow a simple model. You send a request, the provider allocates a GPU, loads your model, runs inference, and releases the GPU. You pay only for seconds of compute used.
This eliminates idle costs. Ten requests per day means you pay for ten requests of compute — not 24 hours of a rented GPU. The tradeoff is cold starts. When no warm GPU is available, the provider must allocate hardware and load your model into VRAM, taking 5–30 seconds depending on model size.
Providers mitigate this with warm pools for popular models. For custom or fine-tuned models, cold starts are a reality you must design around.
How dedicated GPU works
Dedicated means renting a GPU around the clock from RunPod, Lambda Labs, or Vast.ai. You install your serving stack — vLLM, Ollama, or custom code — and manage it yourself.
No cold starts, no queuing, full control over the software stack. You can optimize batch sizes, configure caching, and tune every parameter. The downside is paying whether the GPU is working or idle at 3 AM.
Cost comparison
The economics cross over at a predictable point:
| Daily usage | Serverless cost | Dedicated A100 (~$1/hr) |
|---|---|---|
| 1 hour/day | ~$3/day | ~$24/day |
| 4 hours/day | ~$12/day | ~$24/day |
| 8 hours/day | ~$24/day | ~$24/day |
| 16 hours/day | ~$48/day | ~$24/day |
| 24/7 | ~$72/day | ~$24/day |
Break-even sits at roughly 8 hours of active inference per day. Below that, serverless wins. Above it, dedicated wins. Use our inference cost calculator to model your specific workload.
Latency
Dedicated GPUs deliver consistent, predictable latency. Every request hits a warm model in VRAM. First-token latency is under 100 milliseconds.
Serverless varies dramatically. Warm requests match dedicated performance. Cold requests add 5–30 seconds. For interactive apps like chatbots, a 20-second cold start is unacceptable. For batch processing, it is irrelevant.
Some providers offer reserved capacity to guarantee warm starts, but this partially defeats the cost advantage.
Scaling
Serverless excels at traffic spikes. If your app receives 100x normal traffic, the provider scales automatically. No capacity planning or autoscaling policies needed. Ideal for unpredictable traffic.
Dedicated requires manual scaling — monitoring utilization, provisioning machines, setting up load balancing. More work but precise control. For provider options, see our cloud GPU comparison.
When to use serverless
Serverless fits prototyping and development, low-volume production under a few hundred requests per day, bursty traffic that spikes unpredictably, and batch processing that tolerates cold start latency.
When to use dedicated
Dedicated fits production workloads running 8+ hours per day, interactive applications that cannot tolerate cold starts, custom serving configurations with specific vLLM parameters or LoRA adapters, and multi-model serving on one GPU.
The third option: own hardware
For sustained high-volume inference, owning hardware is cheapest. An RTX 4090 ($1,600) pays for itself in two to three months versus cloud rental. A Mac Mini M4 with 32 GB ($1,150) runs 7B models with zero ongoing costs beyond electricity.
You handle hardware failures, cooling, and networking. For teams with the expertise, it is the most economical path. See our guide on how much VRAM you need to size hardware correctly.
Hybrid architectures
Many production deployments combine both. A dedicated GPU handles baseline traffic while serverless absorbs spikes. This gives cost efficiency for steady-state load and elastic scaling for peaks.
Implementing this requires a routing layer that directs to dedicated infrastructure first and overflows to serverless when saturated. Tools like LiteLLM make this straightforward.
Decision framework
| Question | Serverless | Dedicated |
|---|---|---|
| Under 4 hours/day? | ✅ Cheaper | ❌ Wasteful |
| Over 8 hours/day? | ❌ Expensive | ✅ Cheaper |
| Tolerates cold starts? | ✅ Fine | N/A |
| Needs consistent latency? | ❌ Variable | ✅ Predictable |
| Unpredictable traffic? | ✅ Auto-scales | ❌ Manual |
| Custom stack needed? | ❌ Limited | ✅ Full control |
FAQ
Is serverless GPU cheaper?
Serverless is cheaper when active inference is below about 8 hours per day. Below that, you save by not paying for idle time. Above 8 hours, dedicated rental is more economical because the flat rate beats per-second billing at high utilization. The exact break-even depends on provider and GPU type.
When should I use dedicated GPUs?
Use dedicated GPUs for consistent workloads running 8+ hours per day, applications requiring sub-100ms first-token latency without cold starts, or when you need full control over the serving stack. Production vLLM serving, multi-model deployments, and latency-sensitive interactive apps all benefit from dedicated hardware.
What is cold start?
Cold start is the delay when a serverless provider must allocate hardware and load your model into VRAM before processing a request. It happens when no warm instance is available — typically after inactivity. Cold starts range from 5 to 30 seconds depending on model size and provider. They are the primary drawback of serverless GPU.
Which providers offer serverless GPU?
Major serverless GPU providers in 2026 include Replicate, Modal, RunPod Serverless, Banana, and Fireworks AI. Each has different pricing, GPU types, and cold start characteristics. Replicate and Modal are popular for developer experience. RunPod offers both serverless and dedicated. Fireworks specializes in optimized inference with low cold starts.