9 Best Serverless GPU Providers for LLM Inference (2026)

Serverless GPU sounds simple. Send requests, GPUs spin up, pay for what you use. No capacity planning, no idle costs.

Reality is messier. Cold starts range from 200 milliseconds to over 60 seconds depending on provider and model size. Pricing structures vary wildly. Per-second, per-minute, per-token. Plus hidden charges for CPU, memory, and storage that don't appear on marketing pages.

This guide compares 9 serverless GPU providers based on what matters for LLM inference: cold start latency, H100/A100 pricing, scaling behavior, and developer experience. We also cover when serverless stops making sense, particularly for teams hitting compliance walls with shared infrastructure.

Quick Comparison Table

Provider	H100 Price/hr	Cold Start	Best For	Billing
RunPod	$2.69	200ms–12s	Cost optimization, GPU variety	Per-second
Modal	$4.50	2–4s	Developer experience, Python workflows	Per-second
Replicate	$4.50+	8–60s	Pre-built models, quick experiments	Per-second + per-token
Baseten	~$4.00	16–60s	Production inference APIs	Per-minute
Beam	$4.95	2–3s	Self-hosting, multi-cloud	Per-second
Lambda Labs	$2.99	N/A (dedicated)	Training, zero egress	Per-minute
Cerebrium	$3.50	2–4s	GPU variety, granular billing	Per-second
Fal AI	Contact	Sub-second	Diffusion models, generative media	Per-second
Koyeb	$3.88	~5s	Global deployment, simplicity	Per-second

Prices as of early 2026. Always verify current rates.

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1. RunPod

RunPod dominates the budget end of serverless GPU. Their Flex Workers scale to zero and bill per-second. Active Workers stay warm with a 20-30% discount for predictable workloads.

Pricing breakdown:

• H100 SXM: $2.69/hr (Flex), ~$2.15/hr (Active)
• A100 80GB: $2.17/hr
• RTX 4090: $0.44/hr
• No egress fees

Cold starts: 48% of RunPod's serverless cold starts land under 200ms according to their benchmarks. Large containers (50GB+ models) take 6-12 seconds. FlashBoot, included free, accelerates startup by pre-caching container layers.

What users say: Trustpilot reviews praise the pricing transparency and fast support. Common complaints include UI changes that remove features and occasional GPU availability issues during peak demand. Recurring theme from users: great for prototyping, but monitor your spend on production workloads.

Best for: Variable workloads, cost-conscious teams, image generation pipelines.

Watch out for: Built-in monitoring is basic compared to competitors. You'll likely add your own observability layer for production.

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2. Modal

Modal closed an $87M Series B in September 2025 at a $1.1B valuation. The platform abstracts infrastructure entirely. You write Python functions with decorators, and Modal handles packaging, scaling, and serving.

Pricing breakdown:

• H100: ~$4.50/hr
• A100 80GB: ~$3.50/hr
• A10G: ~$1.10/hr
• Also charges for CPU and memory on top of GPU

Cold starts: 2-4 seconds consistently. Modal maintains a warm pool of base containers and caches model weights on fast NVMe storage.

What users say: Developers love the Python-native workflow. The tradeoff: you're locked into Modal's SDK. Migrating to another provider means rewriting deployment code.

Best for: Rapid iteration, Python-heavy teams, inference endpoints that need fast cold starts.

Watch out for: Per-second billing for CPU and memory adds up. Calculate total cost, not just GPU hours. Less flexibility for non-Python workloads or teams wanting container portability.

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3. Replicate

Replicate is a model marketplace first, serverless platform second. Thousands of pre-built models run via simple API calls. Custom deployments use Cog, their open-source containerization tool.

Pricing breakdown:

• H100: ~$4.50/hr + per-token charges for language models
• A100 80GB: ~$2.70/hr
• Community models often cheaper

Cold starts: Custom model deployments hit 60+ second cold starts. Pre-cached popular models start faster, but anything custom or less common waits in line.

What users say: Perfect for demos and experiments. Production users complain about unpredictable costs and cold start variance.

Best for: Quick experiments, building demos, accessing community models without infrastructure.

Watch out for: Custom models face slow cold starts. Pricing gets expensive at scale. You're locked into their deployment layer with no option to export weights from fine-tuned models.

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4. Baseten

Baseten positions itself as production-grade inference. They raised $150M in Series D (late 2025) and focus on model serving rather than general-purpose compute.

Pricing breakdown:

• H100: ~$4.00/hr
• A100: ~$2.00/hr
• Per-minute billing (not per-second)

Cold starts: 16-60 seconds. Baseten trails competitors here. Their optimization focuses on throughput once warm, not startup latency.

What users say: Strong for teams deploying PyTorch, TensorFlow, or Hugging Face models into real-time pipelines. Their Truss framework simplifies packaging. Less flexible for broader infrastructure needs.

Best for: Production ML teams with predictable traffic, companies already using Truss.

Watch out for: Per-minute billing hurts short-duration requests. Cold starts make it unsuitable for latency-sensitive applications that scale to zero frequently.

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5. Beam

Beam (formerly Slai) rebuilt around developer experience and fast cold starts. Their custom runc runtime launches containers in 200ms. The platform supports self-hosting via beta9, their open-source project.

Pricing breakdown:

• H100: $4.95/hr
• A100: ~$2.04/hr
• CPU: $0.19/core, RAM: $0.02/GB (separate charges)
• 10 hours free GPU time on signup

Cold starts: 2-3 seconds for most functions. Warm starts hit 50ms.

What users say: Hot-reloading during development saves significant iteration time. The self-hosting option (bring your own compute) appeals to teams wanting portability. Free tier concurrency limits (3 GPUs) frustrate scaling tests.

Best for: Iterative development, teams wanting multi-cloud or self-hosting options.

Watch out for: Separate billing for CPU, RAM, and GPU complicates cost estimation. Enterprise features still maturing compared to Baseten or Modal.

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6. Lambda Labs

Lambda doesn't offer true serverless (scale-to-zero), but their on-demand instances compete in the same decision set. Per-minute billing, zero egress fees, and GPU availability that rivals hyperscalers.

Pricing breakdown:

• H100 SXM: $2.99/hr
• H200: $4.99/hr
• A100 40GB: $1.10/hr
• 8-GPU clusters at $23.92/hr total
• Zero egress fees

Cold starts: N/A. Instances stay running. You pay for uptime, not per-request.

What users say: Pre-configured Lambda Stack (PyTorch, CUDA, cuDNN) eliminates setup friction. Availability can be tight for H100s during peak demand.

Best for: Training workloads, teams needing InfiniBand for distributed training, projects with consistent GPU utilization above 40-50%.

Watch out for: Not serverless. You pay for idle time. Best suited for sustained workloads, not bursty inference.

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7. Cerebrium

Cerebrium offers 12+ GPU types with granular per-second billing across GPU, CPU, and memory. Cold starts hit 2-4 seconds. SOC 2, HIPAA, and GDPR compliance built in.

Pricing breakdown:

• H100: ~$3.50/hr
• A100: ~$2.00/hr
• Granular resource billing

Best for: Teams needing compliance certifications, diverse GPU requirements.

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8. Fal AI

Fal specializes in generative media. Diffusion models, image generation, video. Custom inference engine with TensorRT optimization delivers sub-second latency for Stable Diffusion XL.

Pricing: Contact required for H100 pricing. Per-second billing with promotional credits available.

Best for: Diffusion models, real-time image generation, generative media applications.

Watch out for: Narrower GPU selection. Locks you into their stack. Can't export fine-tuned model weights.

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9. Koyeb

Koyeb combines serverless GPUs with a broader platform (databases, CPU services). Native autoscaling and scale-to-zero. Supports Tenstorrent AI accelerators alongside NVIDIA.

Pricing breakdown:

• H100: $3.88/hr
• A100: $2.25/hr
• L40S: $1.54/hr

Best for: Teams wanting GPU compute alongside databases and CPU services, global deployment requirements.

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Decision Matrix: Which Provider Fits Your Use Case?

Prototyping and experiments: Replicate (pre-built models) or RunPod (cheap GPUs)

Production inference with variable traffic: Modal (fast cold starts, great DX) or Cerebrium (compliance included)

Cost optimization at scale: RunPod Flex Workers or Lambda Labs (if utilization exceeds 40%)

Diffusion models and image generation: Fal AI or RunPod

Enterprise with compliance requirements: Cerebrium (SOC 2, HIPAA) or self-hosted alternatives

Python-native teams: Modal

Multi-cloud or self-hosting needs: Beam

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When Serverless Breaks Down

Serverless GPU works until it doesn't. Three scenarios where the model fails:

1. Sustained utilization above 40-50%

At this threshold, dedicated instances cost less than serverless. A team running inference 18 hours daily pays more for per-second billing than reserved capacity. Lambda Labs or dedicated RunPod pods become cheaper.

2. Latency-critical applications that can't tolerate cold starts

Even 2-second cold starts break real-time applications. Keeping workers warm (Modal's keep_warm, RunPod Active Workers) adds continuous cost. At some point, you're paying serverless prices for always-on infrastructure.

3. Compliance and data sovereignty

Serverless platforms share infrastructure across customers. Your inference requests run on GPUs that processed someone else's data minutes earlier. For healthcare (HIPAA), finance (SOC 2), or EU data residency (GDPR), this shared model creates audit problems.

The prompt data flowing through API calls, the model weights loaded into memory, the logs generated during inference. All of it exists on infrastructure you don't control. Most serverless providers offer SOC 2 compliance, but true data isolation requires dedicated infrastructure.

Teams in regulated industries increasingly look beyond serverless entirely. Self-hosting fine-tuned models on dedicated infrastructure solves the compliance problem but reintroduces operational overhead. Platforms like Prem AI bridge this gap with fine-tuning and deployment workflows that include full data sovereignty, zero data retention, and cryptographic verification of privacy guarantees.

The calculus: if your compliance team asks "where exactly does our data go during inference?" and you can't answer precisely, serverless might not be the right architecture.

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Optimizing Serverless GPU Costs

Regardless of provider, a few patterns reduce spend:

Right-size your GPU. Running Llama 3 8B on an H100 wastes money. Smaller models often perform fine on A10G or even T4 GPUs at 3-5x lower cost.

Quantize models. INT8 quantization cuts memory requirements by 4x, enabling deployment on cheaper hardware without major quality loss.

Use warm pools strategically. Keep 1-2 workers warm for baseline traffic. Let additional capacity scale from zero for bursts. This balances latency against idle costs.

Monitor cold start frequency. High cold start rates signal opportunities for keep-alive optimization or capacity reservation.

Batch requests where possible. Serverless bills per-request overhead. Batching amortizes cold starts and improves throughput.

For teams spending over $10K/month on inference, evaluating LLM costs systematically often reveals 30-50% savings through model selection and deployment optimization.

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FAQ

What's the cheapest serverless GPU provider?

RunPod offers the lowest H100 pricing at $2.69/hr. For A100s, Lambda Labs hits $1.10/hr (though not true serverless). Budget depends on your specific GPU needs. RTX 4090s on RunPod start at $0.44/hr for lighter workloads.

Which provider has the fastest cold starts?

Modal consistently hits 2-4 second cold starts through warm container pooling. RunPod claims 48% of cold starts under 200ms with FlashBoot. Replicate and Baseten lag significantly at 16-60+ seconds for custom models.

Can I use serverless GPU for fine-tuning?

Yes, but dedicated instances often make more sense. Fine-tuning jobs run for hours with sustained GPU utilization. Serverless billing advantages disappear when utilization is continuous. RunPod pods or Lambda Labs instances typically cost less for training workloads.

Which serverless GPU providers are HIPAA compliant?

Cerebrium offers HIPAA compliance alongside SOC 2 and GDPR. RunPod's Secure Cloud provides SOC 2 and HIPAA eligibility. For true data isolation required by strict compliance regimes, enterprise platforms with dedicated infrastructure may be necessary.

When should I switch from serverless to dedicated GPU?

When GPU utilization consistently exceeds 40-50%, dedicated instances become cheaper. Also consider switching when cold start latency impacts user experience, or when compliance requirements demand infrastructure you fully control.