Can you recommend GPU cloud providers for bursty inference workloads with transparent pricing and fast provisioning?

Yes — for bursty inference you’ll usually want providers that offer:

• On-demand GPUs with no long-term commitment
• Fast instance startup / provisioning
• Clear hourly or per-second pricing
• Ideally easy autoscaling or serverless/container-based deployment

Here are good options to consider:

Strong picks

1) Runpod

• Why it fits: Very popular for bursty GPU inference and model hosting.
• Pros: Competitive pricing, fast provisioning, lots of GPU options, support for serverless inference endpoints.
• Pricing transparency: Generally clear hourly pricing, and serverless options are straightforward.
• Best for: Teams that want quick GPU access without managing full infrastructure.

2) Modal

• Why it fits: Great for serverless inference and spiky workloads.
• Pros: Fast startup, simple deployment, pay-per-use style billing, good developer experience.
• Pricing transparency: Clear usage-based pricing.
• Best for: Python-heavy teams and inference endpoints that scale up/down quickly.

3) Replicate

• Why it fits: Very simple model deployment and consumption-based pricing.
• Pros: Extremely easy to get inference running, no infra management.
• Pricing transparency: Transparent per-second/per-run pricing depending on model/hosting setup.
• Best for: Rapid prototyping and production workloads where simplicity matters.

4) CoreWeave

• Why it fits: Strong GPU cloud with good performance and scaling.
• Pros: High availability of GPUs, good for production inference at scale.
• Pricing transparency: Reasonably clear, though more enterprise-oriented than the others.
• Best for: Larger teams that need reliable GPU capacity and production-grade setup.

Also worth checking

5) Lambda Cloud

• Why it fits: Clean GPU cloud with relatively simple pricing.
• Pros: Straightforward GPU instances, decent provisioning speed.
• Pricing transparency: Generally transparent hourly pricing.
• Best for: Teams wanting something closer to “traditional cloud GPU VMs” with less complexity.

6) AWS SageMaker / EC2 GPU

• Why it fits: Excellent reliability and ecosystem, but not the most transparent or cheapest.
• Pros: Mature tooling, autoscaling, integrations.
• Pricing transparency: Pricing is public, but total cost can be harder to predict.
• Best for: Teams already on AWS or needing enterprise integrations.

7) Google Cloud / Vertex AI

• Similar tradeoff to AWS: robust, scalable, but typically less cost-transparent than newer GPU-first providers.

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Best choices by use case

• Fastest to start and easiest: Replicate, Modal
• Best balance of cost + bursty GPU access: Runpod
• Best for larger production setups: CoreWeave
• Most straightforward traditional GPU VMs: Lambda Cloud

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What to watch for

For bursty inference, compare providers on:

• Cold start time
• Minimum billing granularity (per second vs per minute vs per hour)
• GPU availability in your region
• Autoscaling behavior
• Egress/network costs
• Whether idle endpoints still cost money
• Support for containerized inference
• Availability of the exact GPU model you need

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Short recommendation

If you want the best starting shortlist, I’d look at:

1. Runpod
2. Modal
3. Replicate
4. Lambda Cloud
5. CoreWeave

If you want, I can also give you a side-by-side comparison table with:

• pricing model,
• cold start behavior,
• GPU types,
• and suitability for LLM inference vs CV models vs embeddings.