Llama 3.1 70B

Cold start latency depends entirely on your hardware — 15-30 seconds on typical enterprise GPU clusters, 3-5 seconds with optimized serving (vLLM, TensorRT). P95 inference ranges 200ms-2s based on batch size and hardware. No managed caching layer means you build this yourself. Score reduced from 5 due to cold start reality.

Standard transformer API with good tokenizer support, but requires significant prompt engineering and fine-tuning for domain-specific tasks. Documentation is community-driven, not enterprise-support backed. Instruction following is strong but not as refined as GPT-4 or Claude for complex reasoning tasks.

No built-in access controls — you implement RBAC/ABAC in your serving layer. Model weights are open but your deployment security is entirely self-managed. No native audit logging, token-level attribution, or request governance. Score significantly reduced from 5 due to complete lack of native permission controls.

Ultimate adaptability — full model weights allow fine-tuning, quantization, architectural modifications, multi-cloud deployment. Can run on-premises, any cloud, or hybrid. No vendor lock-in whatsoever. Community ecosystem enables extensive customization.

Model supports long context (128K tokens) and function calling, but integration orchestration is self-built. No native tool calling framework or multi-modal capabilities in 70B variant. Cross-system integration depends entirely on your serving infrastructure design.

No built-in explainability, request tracing, or cost attribution — all must be implemented in your serving layer. Model outputs have no native citation mechanisms or confidence scoring. Transparency depends entirely on your observability infrastructure. Score significantly reduced from 6.

Complete data sovereignty — no data leaves your infrastructure. Enables air-gapped deployments and full compliance control. You implement all governance policies, but model never sees third-party servers. Highest possible governance score for data-sensitive environments.

No built-in observability — you instrument everything yourself using tools like Weights & Biases, MLflow, or custom metrics. No LLM-specific monitoring, token cost tracking, or model drift detection out of the box. Score reduced from 3 due to complete self-service requirement.

Availability depends entirely on your infrastructure team's capabilities. No vendor SLA, but you control redundancy and failover. Typical enterprise deployments achieve 99.5-99.9% uptime with proper architecture, but RTO depends on your incident response maturity.

Model supports standard prompt formats and can be fine-tuned for domain terminology, but semantic consistency depends on your prompt engineering and fine-tuning quality. No built-in business glossary integration — you implement semantic layer connections.

Meta's LLaMA family has 2+ years in market with extensive community validation. 70B variant widely deployed in production. Open weights provide ultimate stability — no risk of API deprecation or vendor changes affecting your deployment.