Modal alternatives for vLLM inference
If Modal is your reference point, the next question is usually where you can run vLLM with more direct control over GPU choice and hourly cost. This page keeps that research on getflops with live pricing across the clouds most likely to replace a Modal-style workflow.
Vast.ai currently has the lowest tracked vLLM-ready row: RTX 4090 at $0.31/hr.
That is about $227/mo for a continuously running 730-hour month. The figure is a latest on-demand median, not an availability guarantee; verify inventory, region, and deployment overhead before committing.
- Continuous-run estimate
- $227/mo 730 GPU-hours
- Cheapest 80GB+
- $1.00/hr $732/mo continuous
- Evidence set
- 7 providers 16 qualifying GPUs
Providers most likely to replace Modal in a vLLM stack
These providers show up most often once teams start asking whether they should keep using a managed platform or move closer to raw GPU economics.
Teams that want a documented path from prototype to OpenAI-compatible vLLM APIs.
$0.39/hr
L4 · 24GB
$1.39/hr
A100 PCIE
- Strong fit for managed vLLM APIs and bursty traffic patterns.
- Often carries practical A100, H100, and L40-class options.
- Easy handoff from experimentation into production-style endpoints.
- Cold starts and model pull time still matter for latency.
- The cheapest inventory can change quickly across GPU families.
Builders who want straightforward dedicated GPU instances for steadier inference loads.
$0.69/hr
RTX 6000Ada · 48GB
$1.99/hr
A100 SXM4
- Simple dedicated GPU positioning for longer-running inference services.
- Good fit when you want less marketplace churn than spot-style capacity.
- Frequently competitive on 80GB-class training and inference GPUs.
- Less optimized for pure scale-to-zero workflows than serverless-first platforms.
- Inventory breadth can be narrower than broader marketplaces.
Cost-sensitive teams that can trade operational smoothness for lower entry pricing.
$0.31/hr
RTX 4090 · 24GB
$1.00/hr
A100 PCIE
- Often exposes the lowest tracked entry price for vLLM-friendly GPUs.
- Great for experiments, internal tools, and flexible batch inference.
- Marketplace depth makes it useful for bargain hunting.
- Marketplace variability means quality and persistence are less uniform.
- You need to be comfortable evaluating individual offers and host quality.
Enterprise workloads that care about procurement, networking, and surrounding cloud primitives.
$1.50/hr
L4 · 24GB
$3.09/hr
A100 SXM4
- Strong ecosystem fit when inference has to live near other AWS services.
- Useful baseline when you need a managed-cloud price anchor.
- Usually not the cheapest place to start open-weight inference.
- Operational flexibility comes with more cloud complexity.
Teams optimizing for adjacent GCP services or multi-service ML stacks.
$0.66/hr
L4 · 24GB
$4.72/hr
A100 SXM4
- Good fit when your data, networking, or ML tooling already lives on GCP.
- Helpful enterprise benchmark against specialist GPU clouds.
- Typically competes on integration, not absolute hourly price.
- Can be overkill for simple single-model APIs.
Organizations that need Azure-native controls, billing, and procurement paths.
$3.67/hr
A100 PCIE · 80GB
$3.67/hr
A100 PCIE
- Useful when compliance and Microsoft stack integration matter.
- Acts as a reality check against specialist GPU providers.
- Often trails specialist clouds on price and deployment simplicity for open-weight inference.
- Best suited to teams already committed to Azure workflows.
Teams already on Oracle Cloud Infrastructure or that need enterprise procurement and bare-metal GPU shapes.
$2.00/hr
A10G · 24GB
$4.00/hr
A100 SXM4
- Transparent public list pricing for H100, H200, B200, A100, and L40S.
- Bare-metal GPU instances suit steady, reserved training and inference loads.
- Fewer fractional or marketplace options than specialist GPU clouds.
- Best value when you're already invested in the OCI ecosystem.
Live vLLM-friendly pricing rows for alternative buyers
The table below filters to GPUs that commonly show up in open-weight inference plans, starting with the cheapest tracked on-demand entry points.
| Provider | GPU | VRAM | On-demand median | 730-hour estimate | Why it matters | Internal next step |
|---|---|---|---|---|---|---|
| Vast.ai | RTX 4090 | 24GB | $0.31/hr | $227/mo | Cheapest entry point for smaller chat, coding, and internal APIs. | |
| RunPod | L4 | 24GB | $0.39/hr | $285/mo | Cheapest entry point for smaller chat, coding, and internal APIs. | |
| RunPod | A40 | 48GB | $0.44/hr | $321/mo | Balanced single-GPU serving for mid-sized open-weight models. | |
| Vast.ai | RTX 5090 | 32GB | $0.48/hr | $348/mo | Cheapest entry point for smaller chat, coding, and internal APIs. | |
| RunPod | A6000 | 48GB | $0.53/hr | $387/mo | Balanced single-GPU serving for mid-sized open-weight models. | |
| Vast.ai | L40 | 48GB | $0.58/hr | $420/mo | Balanced single-GPU serving for mid-sized open-weight models. | |
| GCP | L4 | 24GB | $0.66/hr | $482/mo | Cheapest entry point for smaller chat, coding, and internal APIs. | |
| Vast.ai | RTX 6000Ada | 48GB | $0.68/hr | $498/mo | Balanced single-GPU serving for mid-sized open-weight models. | |
| Lambda | RTX 6000Ada | 48GB | $0.69/hr | $504/mo | Balanced single-GPU serving for mid-sized open-weight models. | |
| RunPod | RTX 4090 | 24GB | $0.69/hr | $504/mo | Cheapest entry point for smaller chat, coding, and internal APIs. | |
| RunPod | RTX 6000Ada | 48GB | $0.84/hr | $613/mo | Balanced single-GPU serving for mid-sized open-weight models. | |
| RunPod | L40 | 48GB | $0.91/hr | $661/mo | Balanced single-GPU serving for mid-sized open-weight models. |
Tracked outbound links for this search intent
These links stay visible for buyers who still want the source docs, and each outbound click is tracked so you can measure whether this page reduces immediate leakage.
More vLLM and competitor-intent landing pages
These related pages keep comparison-intent visitors inside the site as they move from one query to the next.
Modal alternatives for vLLM inference: how to use this page
These landing pages are built for searchers comparing platforms, not just looking for a deployment tutorial. Start with the live pricing table, then use the provider cards to separate the cheapest GPU row from the platform that best matches your operational needs.
The internal links on this page intentionally point back into the main LLM guide, provider detail pages, and direct comparison pages so you can keep researching on getflops instead of immediately jumping to external documentation.
Cheapest tracked Modal-style starting point
RTX 4090 on Vast.ai is the current cheapest tracked starting point at $0.31/hr. Cheapest 80GB-plus option: A100 PCIE on Vast.ai at $1.00/hr
How these vLLM price pages are assembled
We filter the live compare payload to GPUs that commonly fit vLLM deployments, keep the latest on-demand median row per provider and GPU, and highlight both the cheapest entry price and the cheapest higher-memory option so buyers can compare cost and headroom together.
Modal alternatives for vLLM inference FAQ
What is the cheapest tracked option on the modal alternatives for vllm inference page?
RTX 4090 on Vast.ai is the current cheapest tracked starting point at $0.31/hr.
Why are these pages focused on RunPod, Lambda, Vast.ai, AWS, and more?
These providers are the most common next stop when buyers move from tutorial intent to where-should-I-host intent for vLLM: they expose live GPU inventory, direct hourly pricing, and clearer tradeoffs between convenience, capacity stability, and raw cost.
Which GPU tiers matter most for vLLM hosting decisions?
24GB to 48GB GPUs are the cheapest way into smaller instruct and coding models, while 80GB and 141GB-class GPUs matter once you want larger models, more headroom, or better multi-tenant throughput. This page surfaces both the cheapest overall row and the cheapest 80GB-plus option.
How fresh are the price callouts on this page?
Every callout uses the latest stored on-demand median snapshot for the providers and GPUs shown here. The freshest visible row is from Jul 31, 2026, and collectors run on a daily cadence.
Keep researching after modal alternatives for vllm inference
Use these internal follow-up pages to move from high-intent search landing pages into the model, provider, and GPU comparisons most likely to matter next.