Best GPU cloud for inference
4 models · updated 2026-08-14
The verdict
Modal leads — 1 of 4 models rank Modal the top pick.
Not unanimous: ChatGPT picks RunPod; Claude picks Baseten; Grok picks RunPod.
As of 2026-08-14, ChatGPT, Claude, Gemini and Grok collectively rank Modal #1 for gpu cloud for inference on ModelsAgree by aggregate score. The models' case: Industry-leading cold-start latency through container memory snapshotting, pure Python-defined infrastructure, seamless scale-to-zero economics, and versatile GPU. The models' main caveat: Not cost-effective for multi-month continuous flat-line inference workloads where long-term committed bare-metal clusters offer lower raw hourly. The strongest alternative is RunPod — Best overall balance of low GPU pricing, broad hardware and regional choice, dedicated Pods plus pay-per-second Serverless, persistent storage, and a. Not unanimous: ChatGPT picks RunPod; Claude picks Baseten; Grok picks RunPod. Source: https://modelsagree.com/best/best-gpu-cloud-for-inference (modelsagree.com, CC BY 4.0).
Combined ranking
- 1GPT #2Claude #4Gemini #1Grok #2
Industry-leading cold-start latency through container memory snapshotting, pure Python-defined infrastructure, seamless scale-to-zero economics, and versatile GPU selection making it the gold standard for bursty and custom inference architectures.
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Gemini Industry-leading cold-start latency through container memory snapshotting, pure Python-defined infrastructure, seamless scale-to-zero economics, and versatile GPU selection making it the gold standard for bursty and custom inference architectures.
GPT Near-tie for first when developer time matters most; exceptionally clean code-defined deployments, fast autoscaling, scale-to-zero economics, strong observability, and excellent support for custom inference containers.
Grok Strongest pure serverless DX for inference (Python decorators, autoscaling, snapshots, scale-to-zero) with reliable performance and low idle costs for bursty or variable traffic, making it near-tied with RunPod for teams whose entire workflow is function-style endpoints
Claude Best developer experience for custom inference — define GPU functions in Python, fast cold starts, scale-to-zero serverless billing, and full flexibility to run any model, container, or preprocessing pipeline; excellent for teams whose workloads don't fit a hosted-endpoint mold.
Where it falls shortper GPT Its convenience premium makes sustained, highly utilized inference materially costlier than well-managed dedicated instances.
per Claude You build and own the serving stack (batching, optimization, routing); it gives infrastructure, not turnkey optimized LLM endpoints, so you do more engineering than with Baseten/Together.
per Gemini Not cost-effective for multi-month continuous flat-line inference workloads where long-term committed bare-metal clusters offer lower raw hourly rates.
per Grok Higher effective cost under sustained high utilization and less flexible for non-Python or low-level custom environments
- 2GPT #1Claude #5Gemini #2Grok #1
Best overall balance of low GPU pricing, broad hardware and regional choice, dedicated Pods plus pay-per-second Serverless, persistent storage, and a straightforward container workflow; strongest default for independent developers and small AI teams.
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GPT Best overall balance of low GPU pricing, broad hardware and regional choice, dedicated Pods plus pay-per-second Serverless, persistent storage, and a straightforward container workflow; strongest default for independent developers and small AI teams.
Grok Best practical value for typical practitioners via Secure/Community Pods plus first-class serverless endpoints (FlashBoot cold starts, scale-to-zero, per-second billing), broad GPU catalog from 4090/L40S to H100/H200/B200, custom containers/templates, and competitive on-demand rates that undercut most managed alternatives without forcing hyperscaler complexity
Gemini Unmatched price-to-performance ratio across a massive inventory ranging from budget GPUs to multi-H100 nodes, pairing rapid serverless endpoints with flexible dedicated pods (near-tie with Modal for overall developer value).
Claude Best value for cost-sensitive practitioners — cheap on-demand and serverless GPUs, wide accelerator selection, and pay-per-second serverless workers; strong for indie developers, startups, and variable workloads where hyperscaler pricing is prohibitive.
Where it falls shortper GPT Capacity and reliability can vary by GPU type and region, so it is not ideal as the sole provider for strict high-availability deployments.
per Claude Thinner reliability, support, and enterprise/compliance guarantees than the managed platforms; you assume more ops responsibility and occasional capacity/availability variability.
per Gemini Cold-start predictability and network latency consistency can fluctuate across community and secure clouds, requiring manual optimization for strict latency SLAs.
per Grok Community tier reliability varies and Secure adds cost; still requires more container/ops work than pure Python serverless platforms
- 3GPT —Claude #1Gemini #3Grok —
Purpose-built for production inference with dedicated autoscaling deployments, its Truss packaging and model-performance tuning (custom kernels, TensorRT-LLM integration), and strong reliability/observability; model-agnostic so you can serve open-weight or fine-tuned custom models rather than a fixed catalog. Assumes the practitioner is deploying their own or fine-tuned models, not just calling a hosted API.
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Claude Purpose-built for production inference with dedicated autoscaling deployments, its Truss packaging and model-performance tuning (custom kernels, TensorRT-LLM integration), and strong reliability/observability; model-agnostic so you can serve open-weight or fine-tuned custom models rather than a fixed catalog. Assumes the practitioner is deploying their own or fine-tuned models, not just calling a hosted API.
Gemini Purpose-built for high-throughput enterprise model serving with the open-source Truss framework, native vLLM and TensorRT-LLM acceleration, rock-solid autoscaling, and advanced traffic routing for high-concurrency production deployments.
Where it falls shortper Claude Priced and shaped for teams running dedicated serving at scale; overkill and less cost-efficient than serverless marketplaces for hobbyists or bursty, low-volume experiments.
per Gemini Carries a platform premium and steeper operational configuration curve, making it overkill for basic prototyping or low-volume hobby projects.
- 4GPT —Claude #2Gemini #4Grok —
Best breadth-plus-value for open-model inference — hundreds of models via serverless per-token endpoints and dedicated GPU instances on the same platform, with strong price/performance and low latency from its own inference-stack optimizations; easy migration path from serverless to dedicated as volume grows.
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Claude Best breadth-plus-value for open-model inference — hundreds of models via serverless per-token endpoints and dedicated GPU instances on the same platform, with strong price/performance and low latency from its own inference-stack optimizations; easy migration path from serverless to dedicated as volume grows.
Gemini Custom inference engine and kernel-level optimizations that deliver exceptionally high token throughput and low latency per dollar on open-weight models, supported by seamless transitions between serverless APIs and dedicated GPU instances.
Where it falls shortper Claude Centered on the open-model ecosystem and hosted-endpoint pattern; less suited if you need arbitrary custom container workloads or fully bespoke serving logic.
per Gemini Strongly optimized around mainstream LLMs and generative vision architectures, offering limited flexibility for bespoke Python pipelines or non-standard models.
- 5GPT #3Claude —Gemini —Grok —
Best for large production inference fleets needing current NVIDIA hardware, high-performance networking, Kubernetes-native infrastructure, reserved capacity, and strong multi-GPU scaling.
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GPT Best for large production inference fleets needing current NVIDIA hardware, high-performance networking, Kubernetes-native infrastructure, reserved capacity, and strong multi-GPU scaling.
Where it falls shortper GPT Enterprise-oriented complexity, commitments, and economics make it a poor fit for small or sporadic workloads.
- 6GPT —Claude #3Gemini —Grok —
Speed-optimized LLM/multimodal serving (FireAttention, aggressive quantization and batching) with very low latency and high throughput per dollar, plus solid support for LoRA fine-tunes and function calling in production.
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Claude Speed-optimized LLM/multimodal serving (FireAttention, aggressive quantization and batching) with very low latency and high throughput per dollar, plus solid support for LoRA fine-tunes and function calling in production.
Where it falls shortper Claude Optimized for text/LLM and adjacent modalities on a curated stack; not the tool for raw custom-model deployment or non-LLM/CV pipelines needing full container control.
- 7GPT —Claude —Gemini —Grok #3
Reliable dedicated instances with simple UX, solid multi-GPU options
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Grok Reliable dedicated instances with simple UX, solid multi-GPU options
- 8GPT #4Claude —Gemini —Grok —
Strong purpose-built AI infrastructure with modern NVIDIA systems, high-speed networking and storage, managed Kubernetes, and competitive performance for sustained inference at meaningful scale.
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GPT Strong purpose-built AI infrastructure with modern NVIDIA systems, high-speed networking and storage, managed Kubernetes, and competitive performance for sustained inference at meaningful scale.
Where it falls shortper GPT Its smaller regional footprint and less mature ecosystem make it less convenient than established global clouds.
- 9GPT —Claude —Gemini #5Grok —
The most transparent, cost-effective raw compute pricing on dedicated top-tier NVIDIA GPUs, making it the premier choice for steady-state, high-volume inference where teams run their own container orchestrators.
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Gemini The most transparent, cost-effective raw compute pricing on dedicated top-tier NVIDIA GPUs, making it the premier choice for steady-state, high-volume inference where teams run their own container orchestrators.
Where it falls shortper Gemini Lacks native serverless autoscaling, scale-to-zero capabilities, and managed inference middleware, placing the entire infrastructure and orchestration burden on the user.
- 10GPT #5Claude —Gemini —Grok —
Best raw compute value for fault-tolerant practitioners; its marketplace and serverless layer can deliver unusually inexpensive GPUs with wide hardware choice and per-second billing.
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GPT Best raw compute value for fault-tolerant practitioners; its marketplace and serverless layer can deliver unusually inexpensive GPUs with wide hardware choice and per-second billing.
Where it falls shortper GPT Host quality, availability, networking, and operational consistency vary, so it is not the default for latency-sensitive or tightly regulated production services.
By use case
How this board's leaders rank when the same four models are asked a more specific question.
| Product | This board | training | serverless platforms AI | serverless bursty |
|---|---|---|---|---|
| Modal | #1 | — | #1 | #1 |
| RunPod | #2 | #3 | #3 | #2 |
| Baseten | #3 | — | #2 | #3 |
| Together AI | #4 | #8 | — | — |
| CoreWeave | #5 | #2 | — | — |
| Lambda Labs | #7 | #1 | — | — |
Rank history
Just missed the top 5
GPT Lambda — simple, competitively priced dedicated GPU instances, but recurring capacity constraints and fewer inference-platform conveniences · Google Cloud Run with GPUs — excellent managed reliability and scale-to-zero integration, but limited GPU selection and weaker price-performance for sustained inference
Claude Groq — GroqCloud delivers class-leading token-generation latency via its LPU, but the narrower supported-model catalog and dependence on its own hardware keep it from a general top-5 slot — near-tie with Fireworks for pure speed · Fal — excellent for diffusion/image/video inference but too media-specialized to rank as a general-purpose inference cloud
Gemini CoreWeave — dominates hyperscale dedicated enterprise infrastructure, but lacks accessible on-demand scale-to-zero serverless primitives for typical practitioners
By model
ChatGPT
- 1.RunPod
- 2.Modal
- 3.CoreWeave
- 4.Nebius AI Cloud
- 5.Vast.ai
Claude
- 1.Baseten
- 2.Together AI
- 3.Fireworks AI
- 4.Modal
- 5.RunPod
Gemini
- 1.Modal
- 2.RunPod
- 3.Baseten
- 4.Together AI
- 5.Lambda
Grok
- 1.RunPod
- 2.Modal
- 3.Lambda Labs
Common questions
What is the best gpu cloud for inference according to AI models?
Modal leads. 1 of 4 models rank Modal the top pick. The current top 3: Modal, RunPod, Baseten. Ranked by asking ChatGPT, Claude, Gemini, Grok the same buying question and merging their top-5 picks, updated 2026-08-14. Source: modelsagree.com.
Which gpu cloud for inference did each AI model pick first?
ChatGPT: RunPod. Claude: Baseten. Gemini: Modal. Grok: RunPod.
Do the AI models agree on the best gpu cloud for inference?
Not unanimous. ChatGPT picks RunPod; Claude picks Baseten; Grok picks RunPod.
What changed in the latest gpu cloud for inference ranking?
In the latest poll (2026-08-14): Baseten climbed 1 spot, Together AI climbed 3 spots, Lambda Labs climbed 1 spot; CoreWeave dropped 2 spots, Nebius AI Cloud dropped 3 spots, Vast.ai dropped 4 spots; Fireworks AI and Lambda entered the ranking. The models are re-polled on demand, so this ranking moves.
How is this gpu cloud for inference ranking made?
ChatGPT, Claude, Gemini, Grok are each asked the same buying question in a fresh session with no system steering. Their top-5 answers are merged (rank 1 = 5 pts … rank 5 = 1 pt) into the consensus ranking, re-polled on demand and tracked over time.
More on how polling works: full methodology →
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Cite this ranking
ModelsAgree, “Best GPU cloud for inference” — merged ranking from ChatGPT, Claude, Gemini & Grok, polled 2026-08-14. https://modelsagree.com/best/best-gpu-cloud-for-inference (CC BY 4.0)
Tracked by ModelsAgree · rank 1 = 5 pts … rank 5 = 1 pt · re-polled on demand