Llama
MetaThe default choice when you need to self-host and fully own the weights.
Score breakdown
Our verdict
Llama is the tool we reach for when a client wants full ownership of the model and the data pipeline — no vendor API in the loop, no per-token bill that scales with usage.
That control comes with real operational weight: someone has to host, scale, and maintain it. We only recommend it to clients who either have in-house ML infra or are budgeting for us to run it for them.
Pros & cons
Pros
- ✓Fully open weights — no per-token API fees once deployed
- ✓Largest ecosystem of fine-tuning tools, guides, and community support among open models
- ✓Full control over data — nothing leaves your infrastructure
- ✓Flexible licensing for most commercial use cases
Cons
- –You own the hosting, scaling, and ops burden
- –Requires real ML/infra expertise to run and fine-tune well
- –Raw capability trails closed frontier models at the largest scale
Ideal for
- Clients requiring full data control and on-prem deployment
- Cost control at very high inference volume
- Fine-tuning on proprietary data
- Teams with existing ML infrastructure and expertise
Pricing
Free, open-weight — cost is hosting infrastructure or per-token pricing via cloud/inference providers
Questions, answered.
For meaningful throughput, yes, or you can use a managed inference provider that hosts Llama models on your behalf.
Yes — this is one of its core strengths, with a large ecosystem of fine-tuning tools and guides.
More in AI Models & Assistants.
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