LiteLLMFreemium
An open-source LLM gateway that gives you one consistent API and proxy across 100+ model providers, with key management and spend tracking.
AI Infrastructure comparison
LiteLLM vs Modal
Pricing, pros, cons, and ideal use cases — side by side.
ModalFreemium
A serverless cloud for running AI and data workloads — define infrastructure in Python and get on-demand GPUs without managing servers.
At a glance
| LiteLLM | Modal | |
|---|---|---|
| Pricing | FreemiumOpen-source and free to self-host. A paid enterprise edition adds SSO, audit logs, and support. | FreemiumUsage-based compute pricing with a recurring free credit allowance for getting started. |
| Category | AI Infrastructure | AI Infrastructure |
| Ideal for | Teams using multiple model providersPlatform teams centralizing LLM access and spendEnterprises wanting provider portability | Engineering teams running GPU and batch AI jobsTeams doing fine-tuning and large-scale inferenceOrgs wanting infrastructure defined in code |
Pros & cons
LiteLLM
Pros
- One consistent API across 100+ providers
- Proxy adds key management, budgets, and spend logging
- Avoids provider lock-in
- Lightweight to adopt
Cons
- Self-hosting the proxy is your operational burden
- A gateway is one more hop to monitor
- Advanced governance features are in the paid edition
Modal
Pros
- Define and scale infrastructure directly in Python
- On-demand GPUs with no cluster management
- Scales to zero — low idle cost
- Fast iteration for AI engineering teams
Cons
- Developer-centric — needs engineering capacity
- Usage costs need monitoring on heavy workloads
- Not a turnkey product for non-technical teams
Which should you choose?
LiteLLM is built around teams using multiple model providers; Modal leans more toward engineering teams running gpu and batch ai jobs. Shortlist the one whose strengths line up with your biggest constraint.