Who is Meta Llama best for?

Best use cases for Meta Llama

• Teams with strict data privacy or data residency requirements where sending inference requests to a third-party API is a compliance or security blocker.
• High-volume inference workloads where per-token API costs at scale exceed the cost of running self-hosted GPU infrastructure — typically above 10-50M tokens per day depending on model size.
• Organizations that want full control over the model — fine-tuning on proprietary data, modifying the system prompt architecture, or deploying on air-gapped infrastructure — without API dependency.

Who should avoid Meta Llama?

• You want the fastest path to production without infra ownership
• You can’t invest in evaluation, monitoring, and safety guardrails
• Your workload needs maximum out-of-the-box capability with minimal tuning

Upgrade triggers for Meta Llama

• Need more operational maturity: monitoring, autoscaling, and regression evals
• Need stronger safety posture and policy enforcement at the application layer
• Need hybrid routing: open-weight for baseline, hosted for peak capability

Next steps