What Does a Private LLM Actually Cost in Australia?

Our minimum engagement for a production custom LLM deployment, including RAG architecture, knowledge ingestion for one document corpus, and integration with one existing system, starts at around $25,000 for implementation plus ongoing infrastructure costs. For organisations with a single, well-defined use case and an existing cloud environment on an Australian region, the total first-year cost including implementation is typically in the $40,000 to $70,000 range. This is not cheap, but it produces a durable organisational asset, not a monthly API subscription that can be repriced or deprecated.

For smaller organisations, the economics depend heavily on the specific use case. If the use case involves high query volumes, sensitive data that cannot go to public AI providers, or a task where a fine-tuned model dramatically outperforms a generic one, private deployment can be worth it even at 20 to 30 employees. For smaller organisations where the use case is occasional assistance and data sensitivity is low, a ChatGPT Teams or Claude for Work subscription may be a more appropriate starting point. We are honest about this in scoping conversations and will tell you when private deployment is not the right fit.

For a typical medium enterprise deployment running a 34B to 70B parameter model, the crossover point between cloud-hosted and on-premises is approximately 24 months: before 24 months, cloud-hosted is cheaper in total; after 24 months, on-premises becomes cheaper as the hardware is amortised. The breakeven is earlier for high query volumes and later for low volumes. On-premises also provides the highest level of sovereignty and eliminates ongoing compute cost uncertainty, which has planning value beyond the raw economics. The right answer depends on your query volume projections, capital availability, and sovereignty requirements.

The primary GPU hardware for on-premises LLM inference in Australia is NVIDIA A100 (80GB) and H100 (80GB) cards, available through Ingram Micro, Dicker Data, and NVIDIA enterprise resellers. A100 hardware is currently more accessible and adequate for models up to 70B parameters with appropriate quantisation. H100 hardware provides significantly better performance for large models and is worth the premium for high-concurrency or low-latency requirements. Alternative hardware including AMD MI300X and Intel Gaudi 2 is available but has narrower software ecosystem support. We provide hardware specifications and vendor sourcing support as part of on-premises deployments.

Several Australian government programs are relevant for organisations investing in AI infrastructure. The R&D Tax Incentive provides a 43.5 percent refundable tax offset for eligible small companies and 38.5 percent for larger companies on eligible R&D expenditure, which can include AI model development and evaluation activities. The Modern Manufacturing Initiative and associated programs have funded AI adoption for manufacturers. The Digital Solutions Program provides subsidised advisory support for smaller businesses. State government programs in NSW, Victoria, Queensland, and WA also have technology investment incentives. We can assist with identifying applicable programs during the scoping engagement.

Labour savings depend heavily on the use case, but some benchmarks from enterprise deployments: research and documentation tasks see 30 to 50 percent time reduction in fields like legal, accounting, and policy work. Customer service deployments reduce average handling time by 25 to 45 percent for complex queries. Technical support and knowledge management deployments reduce search and retrieval time by 50 to 70 percent. At $80 per hour and 5 hours per week saved per professional user, a deployment serving 20 users saves approximately $420,000 in labour over three years, well in excess of typical deployment costs.