How the inevitable evolution from monolithic models to distributed AI architecture will reshape the industry

By Eric MacDougall
VP Engineering at Raiinmaker 
LinkedIn | X/Twitter

Most AI startups are building on quicksand and don't even know it.

After leading engineering teams through the monolith → microservices → SOA evolution while working at companies like MindGeek (handling 45M+ monthly visitors) and architecting Realm's real-time esports servers for 200K+ concurrent players with EA, I'm now building actual DePIN infrastructure at Raiinmaker. From this vantage point, I'm watching AI make the exact same architectural mistakes (or I should say going through the same inevitable growing pains) we made 15 years ago.

The pattern is so familiar it's almost painful to watch.

The Economics Don't Lie

Current AI inference costs are fundamentally unsustainable, and the math is brutal:

Current API Pricing Reality:

• GPT-4.1: $3 per 1M input tokens, $10 per 1M output tokens (83% price drop from original GPT-4)
• GPT-4o: $5 per 1M input tokens, $15 per 1M output tokens
• Enterprise application processing 50M tokens monthly = $325-$430 in API bills (assuming balanced input/output ratios)
• H100 inference: $2.40-$6.98 per hour per GPU depending on provider and configuration
• Simple arithmetic: while costs have dropped significantly, inference expenses remain a substantial operational concern for AI startups scaling beyond prototype stage

But here's the deeper issue that most founders miss: you're paying premium prices for massive computational overkill on every single query. It's like hiring a team of PhD mathematicians to calculate your restaurant tip. While recent price cuts from OpenAI (83% reduction with GPT-4.1) have improved the economics, the fundamental inefficiency remains.

I've Seen This Movie Before

In 2015, everyone was building monolithic applications. Single codebases handling everything from user authentication to payment processing to content delivery. The architecture was simple, but scaling was a nightmare.

By 2018, the industry swung hard toward microservices madness. Teams were splitting every function into tiny, independent services. The result? Orchestration chaos, network latency nightmares, and debugging distributed systems that were more complex than the monoliths they replaced.

By 2020, experienced engineering teams have learned the lesson: start with Service-Oriented Architecture (SOA), and only split services when you have proven need and clear boundaries.

**AI is following this identical evolutionary pattern:

Phase 1 (Now): The Monolithic Model Era

We're currently living in the monolithic AI era, though it's more nuanced than pure monoliths:

• Large 405B parameter models (like Llama 3.1) handling diverse tasks, though Mixture of Experts (MoE) models are emerging
• One API call typically hits the same massive GPU cluster regardless of query complexity
• Every request incurs similar computational overhead whether you're asking "What's 2+2?" or "Write a comprehensive business strategy"
• Computational resources are allocated uniformly across wildly different problem domains, despite some early specialization

This is like using a supercomputer to run a calculator app. It works, but the economics are fundamentally broken.

Phase 2 (Likely Next 12-18 months): Micro-Model Chaos

The industry will likely overreact with tiny models everywhere:

• 50+ specialized 1B parameter models per application
• Nightmare orchestration between dozens of models
• Worse overall performance than monolithic systems
• Debugging distributed model failures across multiple inference endpoints

If you've lived through the microservices transition, this sounds familiar because it's the exact same pattern. The pendulum swings too far in the opposite direction before finding equilibrium.

Phase 3 (Estimated 3-5 years): Model SOA Reality

The smart money is building toward this architecture now:

• 3-5 core domain models (7-13B parameters each) handling distinct problem classes
• Intelligent router model (likely 3B parameters) classifying queries and directing them to optimal specialists
• Essential micro-models (1B parameters) only for proven edge cases with clear performance benefits
• Result: 75-90% cost reduction with better performance than monolithic systems (based on early quantization and edge deployment results)

The Technical Forces Driving This Evolution

Physics Favors Distribution

The hardware economics are shifting in favor of distributed inference:

Current Centralized Reality:

• H100 GPUs: 80GB memory, $25,000+ each, requiring 5-6 units for large model inference
• Cloud rental costs: $2.40-$6.98 per GPU hour depending on provider and configuration
• Low FLOPS utilization (<5%) when running large models, driving up per-query costs
• Massive data center cooling and power requirements
• Network latency penalties for every API call

Emerging Edge Reality:

• M4 MacBooks: Running 7B models at 55-72 tokens per second locally with MLX optimization
• Consumer devices hitting 64GB+ memory as standard configuration
• 5G networks delivering 10-30ms latency vs 100-200ms typical cloud round-trips
• Distributed compute becoming economically viable at scale

Mixture of Experts Proves the Concept Works

The technical foundation for distributed AI already exists:

• MoE (Mixture of Experts) models demonstrate that activating only relevant parameters works
• Query routing based on domain classification is proven technology
• Specialized models consistently outperform generalists in narrow problem domains

The leap from MoE within a single model to MoE across distributed models is evolutionary, not revolutionary.

Building the Infrastructure for Phase 3

At Raiinmaker, we're working on distributed infrastructure that Phase 3 will require. The DePIN (Decentralized Physical Infrastructure Networks) approach represents exactly the kind of breakthrough the industry needs, though we're still in early development stages:

Distributed Model SOA Architecture

Domain-Specific Routing (The Vision):

• Mathematical queries → quantized 7B math specialists on edge nodes
• Code generation → specialized programming models with relevant context libraries
• Content safety → distributed validation with human-in-the-loop verification
• Complex reasoning → orchestrated workflows across reasoning specialists

Economic Architecture Potential:

• Node operators could specialize in hosting specific model families, driving efficiency
• Intelligent request routing to optimal compute for each query type
• 4-bit quantization + edge deployment = 75-80% cost reduction vs current API pricing (based on quantization reducing memory/compute needs by ~75%)
• Network effects could reward specialization over generalization, creating sustainable economics

Protocol Innovation

Technical Infrastructure:

• JSON-RPC 2.0 over WebSocket/HTTP + MCP (Model Context Protocol) for secure inter-model communication
• Fault tolerance through model redundancy across geographic and domain boundaries
• Sub-100ms multi-model orchestration for complex queries
• Graceful degradation when domain specialists are unavailable

Real-World Performance:

• Sub-$0.003 per complex request vs today's $0.010 API costs (based on current GPT-4.1 pricing)
• Better accuracy through domain specialization
• Reduced latency through edge deployment
• Improved reliability through distributed redundancy

My Prediction: The Architecture That Will Win

Within 3-5 years, I believe successful AI companies will likely standardize on this architecture:

Core Infrastructure:

• Router model (~3B parameters): Query classification and orchestration intelligence
• Core domain models (~7-13B each): Specialized in Code, Mathematics, Reasoning, Language
• Essential micro-models (~1B): Specialized validators for critical decision paths
• Edge deployment: Models running where data lives, minimizing latency and maximizing privacy

Economic Impact (Projected):

• Target: Sub-$0.003 per complex request vs today's $0.010-$0.015 (GPT-4.1/4o pricing)
• Projected 3-5x cost reduction while improving accuracy and speed through specialization
• Sustainable unit economics for AI-first businesses, though infrastructure maturity remains a key dependency

The companies still betting everything on monolithic mega-models may face the same fate as enterprises that tried to scale mainframes instead of adopting distributed systems in the 1990s.

The Real Opportunity: Infrastructure

Just like the microservices transition, the winners won't be the first movers who rush toward distributed models. They'll be the companies that learn from the inevitable chaos of Phase 2 and build sustainable, well-architected distributed systems.

The infrastructure play is where the generational wealth will likely be created. Just as AWS emerged during the microservices transition to become a $80B+ annual revenue business, DePIN infrastructure for distributed AI could create the next generation of $100B+ companies.

The question every AI company should ask: Are you building for Phase 1 monoliths, Phase 2 chaos, or Phase 3 reality?

The companies making the right architectural decisions today will likely own the AI landscape of tomorrow.

Eric MacDougall is VP of Engineering at Raiinmaker, building decentralized AI infrastructure and AI data/training systems. Previously, he led engineering at MindGeek (Pornhub) handling 45M+ monthly visitors and architected real-time systems for EA partnerships.

Connect with him on LinkedIn or X/Twitter.