Question 1

What is the cheapest way to run AI workloads if I'm leaving Modal?

Accepted Answer

Runpod and Cerebrium both offer per-second billing for GPU compute, which can be significantly cheaper than fixed monthly fees if your workloads are bursty or experimental. LM Studio eliminates cloud costs entirely by running open-source models locally on your own hardware, though it requires upfront investment in GPUs. The best choice depends on whether you want to minimize per-job costs, eliminate cloud fees, or balance flexibility with predictability.

Question 2

Can I run open-source models without relying on a cloud platform?

Accepted Answer

Yes. LM Studio is specifically designed to run open-source LLMs locally with a desktop UI, giving you full control over your models and data without cloud dependencies. This approach works well for development, testing, and production workloads where data residency or cost control is critical, though you'll need sufficient local GPU or CPU resources.

Question 3

Is Runpod a good replacement for Modal?

Accepted Answer

Runpod is a strong alternative if your primary concern is GPU cost and flexibility. Both platforms offer on-demand compute for AI workloads, but Runpod emphasizes transparent per-second billing and a broader marketplace of pre-configured environments, while Modal historically focused on developer experience and built-in observability. Runpod may require more manual configuration for observability and autoscaling, but it often costs less for variable workloads.

Question 4

Which alternative is best for teams that need governance and orchestration?

Accepted Answer

Xpress AI is purpose-built for running and governing AI agents at enterprise scale, making it ideal if your team needs centralized control, audit trails, and multi-user collaboration around agent workflows. Lightning AI also supports team collaboration through browser-based development, though it emphasizes ease of use over governance features. For pure infrastructure governance without agent-specific tooling, you may need to layer additional tools on top of Runpod or Cerebrium.

Question 5

Do these alternatives support the same hardware and regions as Modal?

Accepted Answer

Support varies by platform. Runpod and Cerebrium offer broad GPU availability across multiple regions and hardware types, though not necessarily identical to Modal's catalog. LM Studio runs only on your local hardware. Lightning AI abstracts hardware selection for simplicity. If you depend on specific GPU types, regions, or compliance zones, verify availability with your target platform before migrating.

Question 6

How do I migrate my workloads from Modal to an alternative?

Accepted Answer

Start by exporting your Modal code, environment variables, and any custom Docker images or dependencies. Most alternatives accept containerized workloads or Python scripts, so your core logic often transfers with minimal changes. Test your workload on the target platform in a staging environment, paying special attention to cold start times, memory limits, and any platform-specific APIs. Update your deployment scripts and monitoring to match the new platform's conventions.

Question 7

Which alternative offers the fastest setup for someone new to AI infrastructure?

Accepted Answer

Lightning AI is designed for zero-setup deployment from a browser, making it the fastest path if you want to train and deploy without infrastructure knowledge. LM Studio is also quick to set up locally if you just want to run open-source models. For more control with reasonable onboarding, Runpod and Cerebrium both provide clear documentation and pre-built templates, though they require more hands-on configuration than Lightning AI.

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