Build vs. Buy: Should Your Firm Build Its Own AI Infrastructure?
The DIY Instinct
When a managing partner or COO first hears about private AI — running your own AI models on your own infrastructure — the reaction is often the same: "Can't we just do this ourselves?"
It is a reasonable question. Your firm already has IT staff. Cloud computing is widely available. Open-source AI models are free to download. In theory, the pieces are all there. But understanding what "doing it yourself" actually entails will clarify why most professional services firms should not.
What Building Private AI Actually Requires
Deploying a private AI environment is not a single project. It is a series of specialized engineering challenges, each requiring different expertise.
GPU Infrastructure
AI models run on GPUs, not the standard compute infrastructure most firms use. You need to provision GPU-accelerated servers, select the right GPU type for your model, configure CUDA drivers and libraries, optimize memory allocation, and set up networking for the data throughput AI inference requires. GPU instances are expensive, often in short supply, and require specific configuration.
Model Deployment
Downloading an open-source model is the easy part. Production deployment requires selecting the right model and version, quantizing and optimizing it for your hardware, setting up an inference server that handles concurrent requests, configuring model parameters for professional use cases, and building a user-facing interface.
RAG Pipeline
The real value for professional services firms comes from connecting AI to your firm's internal knowledge base through Retrieval-Augmented Generation (RAG). Building a production-quality RAG pipeline requires a document ingestion system for PDFs, Word documents, and spreadsheets; a vector database for document embeddings; a chunking strategy that preserves semantic meaning; and a retrieval system that handles the dozens of edge cases real-world documents create.
Security and Operations
For a professional services firm, security requirements are not optional: encryption at rest and in transit, access controls, network isolation, audit logging, and potentially HIPAA-compliant infrastructure. And once built, someone needs to monitor the system, apply patches, upgrade to newer models, scale infrastructure, and keep the RAG pipeline current.
The Real Cost
To build and maintain a private AI environment in-house, a firm typically needs:
Personnel (3-5 FTEs):
- 1-2 ML/AI engineers ($150,000-$250,000 each)
- 1 DevOps/infrastructure engineer ($140,000-$200,000)
- 1 full-stack developer ($130,000-$180,000)
- Partial allocation of existing IT staff
Fully loaded cost: $600,000-$900,000 per year in personnel alone.
Infrastructure costs for GPU-accelerated cloud computing: $3,000-$8,000 per month for a firm of 20-50 users.
Timeline: 6-12 months from hiring to a production-ready system.
Total first-year cost: $700,000-$1,000,000+
What You Get With a Managed Approach
The alternative is working with a managed provider that specializes in private AI for professional services.
Deployment timeline: Days to weeks, not months. The infrastructure patterns are established and the integration work is well-understood.
Cost: A fraction of the in-house build, because the provider's expertise is amortized across multiple clients.
Ongoing management: Model upgrades, security patches, performance monitoring, and support are included.
Expertise: The edge cases, performance tuning, and security configuration are known quantities, not learning exercises.
When Building In-House Makes Sense
There are scenarios where building in-house is the right call: very large firms (500+ employees) with existing technical teams, firms with highly unique requirements needing custom model training, or firms that view AI infrastructure as a core competency worth long-term investment.
For most professional services firms — those with 10 to 200 people, where AI is a productivity tool rather than a product — the managed approach is overwhelmingly more practical.
The Decision Framework
Ask yourself three questions:
Do we have the technical talent? Not IT generalists — AI-specific engineers with experience in model deployment, MLOps, and infrastructure security.
Is this our core competency? Your firm's expertise is legal work, financial advisory, healthcare, or whatever your practice area is. Building AI infrastructure is a fundamentally different discipline.
What is the cost of delay? Every month spent building is a month your competitors are using AI to work faster.
For most firms, the math points clearly toward working with a specialist. You would not build your own email server. AI infrastructure is the same category of decision.
To learn more about how we work and what a managed deployment looks like, or to book a conversation about your firm's specific needs, we are happy to walk you through it.