Artificial Intelligence has dominated headlines, boardrooms, and product roadmaps over the last year. The promise is massive: streamlined operations, accelerated coding, automated content generation, and new efficiencies across every industry. And at the center of it all sits the Large Language Model (LLM).
But the real question for enterprises isn’t whether to use an LLM—it’s which kind. Do you rely on a public model-as-a-service from vendors like OpenAI or Anthropic? Or do you invest in building and deploying a private enterprise LLM, trained on your own data, optimized for your needs, and protected by your own security standards?
Based on our research, here are 29 considerations that should shape any enterprise LLM strategy.
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Impact: Where LLMs Drive the Most Value
- An LLM trained specifically on an enterprise’s use cases will always be more relevant and effective.
- Superfluous information in public LLMs reduces accuracy for business-specific needs.
- Private LLMs trained on proprietary data can themselves become valuable intellectual property.
Security: The First and Last Word
- Source data must be secure to avoid accidental exposure of customer, order, or financial data.
- Guardrails should be in place to prevent sensitive information from leaking during training or inference.
- External datasets introduce risks of inaccuracy, ownership disputes, or legal issues.
- Model security is just as critical as data security, and open-source options may offer better transparency.
- Think in terms of an AI platform, not just a single model, so you can switch between engines as needed.
- Proprietary model providers are subject to leadership changes and corporate politics that can disrupt services.
- Hosting matters: enterprise-controlled data centers or secure private clouds are the safest environments.
Performance: More Than Just Benchmarks
- LLMs should be consistently measured with benchmarks across accuracy, reasoning, and synthesis.
- Latency is crucial—especially in customer-facing or real-time use cases like speech recognition or logistics.
- Switching between models in a private LLM often improves performance compared to retraining a single public model.
- Larger context windows enable deeper, longer conversations and better continuity.
- Private LLMs allow for more advanced hallucination control techniques such as distillation, RAG, quantization, and intent analysis.
- Public LLMs are limited to fine-tuning as their main hallucination control mechanism.
Cost: More Than Pennies Per Token
- Token costs can escalate quickly at scale, turning “pennies per query” into six-figure annual bills.
- LLMs that run efficiently on CPUs can reduce infrastructure costs by more than half compared to GPU deployments.
Flexibility: The Only Constant is Change
- Vendor lock-in is a common risk with model-as-a-service providers tied to large cloud ecosystems.
- Mid-market providers may offer the best balance of agility and depth, compared to giant vendors or under-resourced startups.
- Open-source models evolve faster, driven by global developer communities.
- Different tasks require different model outputs, token lengths, and tuning approaches—flexibility is key.
- Fine-tuning should be monitored to prevent trade-offs between use cases.
- Artificial General Intelligence may grab headlines, but enterprises need LLMs focused on solving specific, immediate problems.
Sustainability: Data, People, and Policy
- Continuous data pipelines are essential for keeping private LLMs current and accurate.
- Synthetic data is useful for augmentation but cannot replace real, high-quality datasets.
- Legal risks around copyright, labor disputes, and regulation will continue to intensify.
- Employees should be trained on how to effectively use and prompt LLMs.
- Enterprises must evaluate their internal capacity—talent, infrastructure, and security—to sustain private LLMs long-term.
The Lifecycle of an Enterprise AI Player
Enterprises that fully adopt private LLMs typically progress through four phases:
- Initial Setup with pre-trained models for fast deployment
- Model Tailoring through fine-tuning, prompt engineering, and retrieval-augmented generation
- In-house Training using lightweight open-source models to reduce SaaS costs
- Independence, where the enterprise controls its own data, models, and infrastructure, potentially even commercializing its LLM as a service
Conclusion: Choosing the Enterprise Path Forward
Public LLMs offer speed and scale, but private enterprise LLMs offer something more valuable: control, security, flexibility, and long-term competitive advantage. Enterprises that treat LLMs not as off-the-shelf tools but as strategic assets—trained on their data, aligned with their goals, and secured by their infrastructure—will be the ones who win in the age of generative AI.
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