Quick answer
What should you choose?
Start with RunPod when you want a documented choice between controllable Pods and vLLM-based Serverless endpoints. Start with Vast.ai when marketplace selection and serverless options fit a technical team that can evaluate offer-level reliability. Consider Massed Compute or Cudo Compute when sustained, dedicated or clustered infrastructure is the real requirement. Choose only after the model, quantization, context length, concurrency and latency target are defined.
An LLM that loads successfully is not automatically production-ready. Model weights compete with KV cache and framework overhead, while concurrency, context length and batching change both memory demand and response time.
Compare providers with the same model build and traffic shape. For an API, measure cost per accepted request at the required latency—not theoretical tokens per second on an empty server.
Current facts that change the decision
RunPod documents controllable Pods and OpenAI-compatible vLLM workers for serverless inference.
Vast.ai documents marketplace instances, reusable templates and autoscaling serverless workloads.
Evaluate for sustained workloads where tenancy and dedicated capacity matter.
A sales-led candidate for organizations evaluating dedicated or clustered deployments.
Time-sensitive facts verified August 15, 2026. Always recheck the live product page before paying.
The shortlist at a glance
Start with buyer fit, then validate the exact plan. Candidate order follows this guide's decision path; it is not a synthetic score.
RunPod
A US-operated AI cloud combining GPU Pods, serverless inference and clusters.
Developers moving between GPU development and production inference
You have not separated storage and idle-resource cost from compute
Vast.ai
A distributed GPU marketplace with variable host, price and reliability characteristics.
Price-sensitive experiments that can compare individual marketplace offers
You need a uniform provider-wide hardware and support promise
Massed Compute
US-operated GPU infrastructure spanning hourly instances, bare metal and clusters.
Teams that may grow from one GPU into dedicated or clustered capacity
You only need a managed pay-per-token model API
Compare every candidate
| Provider | Best fit | Key limitation | Company region |
|---|---|---|---|
| RunPodgpu-cloud | Developers moving between GPU development and production inference | You have not separated storage and idle-resource cost from compute | United States |
| Vast.aigpu-cloud | Price-sensitive experiments that can compare individual marketplace offers | You need a uniform provider-wide hardware and support promise | United States |
| Massed Computegpu-cloud | Teams that may grow from one GPU into dedicated or clustered capacity | You only need a managed pay-per-token model API | United States |
| Cudo Computegpu-cloud | Enterprise teams evaluating dedicated GPU clusters | You need a new self-service GPU account | United Kingdom |
How to choose without buying the wrong plan
- Fix the model, quantization and maximum context before sizing
- Model KV-cache memory at the intended concurrency
- Choose between persistent instance, serverless endpoint and dedicated capacity
- Benchmark time-to-first-token and throughput under representative load
- Include cold starts, storage, idle time, retries and engineering labor
A current offer is not automatically the lowest total cost. Compare the initial charge, billing period, renewal amount, required add-ons, backups, migration effort and your administration time.
Frequently asked questions
What GPU is best for LLM inference?
The useful answer starts with required memory and traffic shape. Model size, quantization, context length, concurrency and latency determine whether one GPU, multiple GPUs or a managed endpoint is appropriate.
Is serverless GPU always cheaper for an LLM API?
No. It can fit bursty traffic with long idle periods, while sustained utilization may favor a persistent or dedicated deployment. Cold starts, minimum billing units and request latency must be measured.
Can a normal VPS run an LLM?
A CPU VPS can run some small or heavily quantized models, but response speed and concurrency may be inadequate. Use the VPS-versus-GPU guide to decide from the workload rather than the label.
Primary sources
- RunPod official vLLM Serverless documentation ↗
- RunPod official Pods overview ↗
- Vast.ai official getting-started documentation ↗
- Massed Compute official products ↗
- Cudo Compute official GPU cloud ↗
Recheck the exact plan, company terms and checkout total before buying. Product pages and availability can change after the verification date.