Self-hosting Llama for security workloads looks attractive from a pure per-token pricing perspective: after the infrastructure is paid for, the marginal cost per inference approaches zero. The list-price comparison favours self-hosting for any meaningful volume. The actual cost comparison, including infrastructure, operations, and engineering, tells a different story — and it favours managed services for most organisations.
What self-hosting actually costs
Five cost categories:
- GPU infrastructure. A100/H100-class GPUs for inference at meaningful throughput. Capital cost or ongoing cloud spend.
- Operations. Serving infrastructure (vLLM, TGI, custom), monitoring, scaling, rolling updates.
- Engineering capacity. 1-2 FTE for model operation, plus part-time AI-engineering for tuning.
- Quality delta. If the in-house model trails frontier quality, the cost of the quality gap shows up in user experience and downstream engineering hours.
- Maintenance of the grounding layer. Reachability, SBOM integration, policy — have to be built.
A realistic total for a 100k-scan/month workload: $200k-500k annualised, dominated by engineering and infrastructure, not token cost.
What Griffin AI costs at the same volume
A comparable volume on the Safeguard platform: license + bounded token spend, typically in the $100k-250k annualised range for the same workload, depending on enterprise tier.
The cost delta is less than the list-price comparison suggests because the list-price comparison ignores engineering and ops.
When self-hosting is cost-effective
Three specific conditions:
- Very large volume. At 10M+ scans/month, amortising the infrastructure becomes favourable.
- Existing AI engineering capacity. The FTE cost is already budgeted.
- Compliance constraints that make managed services prohibitive regardless of cost.
For most enterprise deployments, none of these conditions hold.
The hidden tax of self-hosting
Two that consistently surprise customers:
- Quality regression on model upgrades. Swapping in a newer Llama version produces behaviour changes that require eval re-baselining.
- Incident debugging. When something goes wrong with the model, the on-prem team is the debugging team. Frontier vendors have large teams for this; in-house teams have 1-2 people.
These are not budget line items but they consume engineering time.
What to evaluate
Three concrete checks:
- Total cost of ownership over 3 years including engineering and infrastructure.
- Quality delta under the specific workload.
- Operational risk comparison — what happens at 3am when the inference cluster is unhealthy?
How Safeguard Helps
Safeguard's Griffin AI delivers frontier-model quality, managed operations, and pre-built security grounding at a total cost that is frequently lower than self-hosting Llama equivalents at enterprise scale. For organisations whose self-hosting business case was built on list-price comparison, the full TCO often shifts the answer.