Should I build my own large language model (LLM)?

The question "Should we build our own LLM?" reveals a fundamental misunderstanding of what most organisations actually need. After architecting AI systems that exploit advanced capabilities like hierarchical context management and self-improving workflows, the real question isn't whether to build an LLM—it's whether you're maximising the technical potential of AI solutions that already exist.

Whilst organisations debate building custom LLMs, they're missing the bigger opportunity. The majority of AI implementations barely scratch the surface of available capabilities. Companies spend months building basic chatbots when they could be developing sophisticated systems with guided user journeys, contextual memory, and adaptive learning patterns.

Building your own LLM is like manufacturing your own microprocessors when you haven't yet mastered software architecture.

Custom LLM development becomes viable when you meet these criteria:

Technical Prerequisites:

• Sophisticated AI development team with deep ML expertise
• Substantial computational infrastructure (multi-million pound investment)
• Access to high-quality, domain-specific training datasets
• 18-24 month development timeline without revenue pressure

Business Justification:

• Unique domain requirements that no existing model addresses
• Data sovereignty regulations requiring complete control
• Long-term competitive advantage dependent on proprietary AI capabilities
• Volume economics where custom development becomes cost-effective

Most organisations claiming these prerequisites are overestimating their needs and underestimating the complexity.

Instead of building from scratch, focus on exploiting the full technical potential of available AI capabilities:

Advanced implementation approaches:

• Hierarchical context management for complex decision-making workflows
• Self-improving prompting systems that evolve with usage patterns
• Novel interaction patterns that transform how users engage with AI
• Multi-agent architectures that coordinate specialised AI capabilities

Strategic technical decisions:

• Fine-tuning open-source models for specific domains
• Implementing retrieval-augmented generation (RAG) with sophisticated knowledge graphs
• Building custom evaluation frameworks for domain-specific performance
• Developing hybrid approaches that combine multiple AI capabilities

Before making any LLM investment decision:

1. Technical assessment: What percentage of available AI capabilities does your current implementation exploit?
2. Competitive analysis: Could sophisticated implementation of existing models provide the same competitive advantage?
3. Resource evaluation: Do you have the technical expertise to distinguish between superficial and genuine AI innovation?
4. Strategic alignment: Does custom LLM development align with your core business capabilities and competitive positioning?

Building production-ready LLMs requires:

• £2-10M+ in computational resources
• 12-18 months with expert ML team
• Ongoing operational costs exceeding £100K monthly
• Risk of technological obsolescence as the field rapidly evolves

Most organisations would generate superior returns investing these resources in sophisticated implementation of existing capabilities.

For 95% of organisations, the answer is clear: Don't build your own LLM. Build AI products that fully exploit what's already possible.

Focus on:

• Product definition: Bridging ambitious vision with technical reality
• Sophisticated architecture: Implementing advanced capabilities most competitors ignore
• Strategic implementation: Creating competitive advantage through technical depth, not custom models

The organisations succeeding with AI aren't those building custom LLMs—they're those maximising the potential of existing capabilities whilst their competitors implement basic chatbots.

The LLM decision requires technical due diligence that distinguishes between solutions that exploit AI's full potential and those representing typical shallow implementations. This isn't a technology decision—it's a strategic business decision requiring expertise in both advanced AI capabilities and competitive positioning.

Before committing resources to custom LLM development, ensure you're not missing opportunities to create breakthrough products with existing technologies. The most sophisticated AI implementations often involve no custom model development whatsoever.

Need expert evaluation of your AI strategy and technical approaches? Schedule a call with us to discuss technical due diligence for AI investments and strategic guidance for building sophisticated AI products that maximise available capabilities.