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Enterprise Reality

The AI Build vs Buy Lie: Why You Need Both

The false dichotomy killing enterprise AI projects and our hybrid approach that actually works.

11 min readSOO Group Engineering

"Should we build or buy?" - Every enterprise AI committee, wasting 6 months

Meanwhile: Your competitors shipped 3 months ago using both.

The question isn't build OR buy. It's build AND buy WHAT.

The False Choice Killing Enterprise AI

Every enterprise AI journey starts with the same meeting. Six months later, they're still debating. We've watched this movie 100 times. Here's why both extremes fail.

The Reality Nobody Admits:

  • Pure "build": 2 years and $10M to match vendor basics
  • Pure "buy": Vendor lock-in with 30% of needed features
  • Successful deployments: 95% hybrid approaches
  • Time wasted debating: 6-12 months average

Why "Just Build It" Fails

The NIH Syndrome

"We're special. Our needs are unique. We'll build from scratch."

12 months later:

  • Reinvented vector storage (poorly)
  • Built 10% of OpenAI's features
  • Spent $5M on infrastructure
  • Zero production deployments
  • Team burned out and leaving

The Talent Reality

You think you'll hire the team. Here's what happens:

ML Engineers needed: 10
ML Engineers hired: 3
ML Engineers who stay >1 year: 1
Time to productivity: 6 months
Cost per engineer: $400K+

Meanwhile: Vendors have 100+ engineers
Already built what you need

The Maintenance Trap

Building is 20% of the work. Maintaining is 80%.

  • Security patches every week
  • Model updates every month
  • Scaling issues every growth spurt
  • Compliance updates every quarter
  • Your team: Already on the next project

Why "Just Buy It" Fails

The 70% Problem

Best-case: Vendor solution meets 70% of your needs. The other 30%? That's where the value is.

What vendors won't customize:

  • Your specific business logic
  • Integration with legacy systems
  • Compliance requirements
  • Domain-specific features
  • The exact workflow your users need

Result: Square peg, round hole, angry users

The Lock-in Nightmare

Year 1: Great pricing. Year 3: Hostage situation.

The Vendor Lock-in Playbook:

  1. Proprietary data formats
  2. Custom APIs with no standards
  3. Migration costs that grow over time
  4. Price increases once you're stuck
  5. "Sunset" features you depend on

The Innovation Ceiling

You move at vendor speed, not market speed.

Need a critical feature? Get in line behind 500 other customers. Your competitor who built it? They shipped last week.

The Hybrid Approach That Actually Works

Stop debating. Start shipping. Here's the framework we use:

The 80/20 Architecture

Buy (80%):

  • Commodity infrastructure (vector DBs, LLM APIs)
  • Standard ML operations (monitoring, deployment)
  • Common capabilities (embeddings, basic RAG)
  • Security and compliance tools

Build (20%):

  • Business logic and workflows
  • Domain-specific models and prompts
  • Integration layers
  • Competitive differentiators

The Decision Framework

Buy When:

  • It's infrastructure (databases, queues, monitoring)
  • Standards exist (OAuth, embeddings, APIs)
  • Maintenance is complex (security, scaling)
  • Multiple good options exist
  • It's not your differentiator

Build When:

  • It's your secret sauce
  • Integration is the value
  • Domain expertise matters
  • Vendors can't understand your business
  • Speed of iteration is critical

Partner When:

  • You need expertise, not software
  • Time to market is critical
  • Risk needs to be shared
  • Knowledge transfer is valuable
  • You want to eventually insource

The Hybrid Approach in Action

Common Pattern: Claims Processing AI

Typical Buy Decisions:

  • Cloud LLM infrastructure (Azure OpenAI, Bedrock)
  • Vector storage (Pinecone, Qdrant)
  • Monitoring tools (DataDog, New Relic)
  • API management (Kong, Apigee)

Typical Build Decisions:

  • Domain-specific prompt engineering
  • Legacy system integration layers
  • Business logic and validation rules
  • Compliance and audit systems

Common Partnership Areas:

  • Architecture design and validation
  • Initial implementation and setup
  • Knowledge transfer to internal teams
  • Optimization and troubleshooting

Typical Outcomes:

  • 4-6 month deployment (vs 18+ for pure build)
  • Significant processing time reductions
  • Millions saved vs building everything
  • Flexibility to change vendors
  • Internal team ownership

The Anti-Patterns to Avoid

Building Commodity Infrastructure

"We'll build our own vector database!" No. Just no. Use Pinecone, Weaviate, or pgvector.

Buying Your Differentiator

If it's your competitive advantage, don't let a vendor own it. They'll sell it to your competitor next.

Analysis Paralysis

Perfect build vs buy decision after 12 months < Good enough decision after 1 month + 11 months of iteration.

The Practical Playbook

Week 1: Stop Debating, Start Mapping

  1. List all capabilities needed
  2. Mark which are differentiators
  3. Research available solutions
  4. Identify integration points

Week 2: Prototype the Hybrid

  1. Stand up bought infrastructure
  2. Build one differentiating feature
  3. Integrate with one legacy system
  4. Measure and learn

Week 3-4: Decide Based on Data

  1. What worked easily? (Keep buying)
  2. What was painful? (Consider building)
  3. What's missing? (Partner or build)
  4. Ship v1 and iterate

The Bottom Line

Build vs Buy is a false choice designed to waste time. The answer is always both. Build what makes you unique. Buy what makes you fast. Partner when you need to learn.

Your competitors aren't debating. They're shipping. Make the 80% decision and move.

Stuck in build vs buy analysis?

Let's design your hybrid architecture and ship something real.

Design Your Hybrid Approach
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