Technical guide to deploying Generative AI in production environments. RAG architectures, LLM selection, cost optimization, governance, and expert consulting recommendations.
90% of GenAI POCs fail to reach production. This guide covers the technical decisions that separate successful deployments from failed experiments: architecture patterns, model selection, cost management, and operational readiness.
Vector DB: $500-$5,000/month | Embedding: $0.0001/1K tokens | LLM: $0.01-0.03/1K tokens
Total for 1M queries/month: $8,000-$25,000
Training: $100-$5,000 per fine-tune (GPT-3.5: ~$100, GPT-4: ~$2,000-5,000)
Inference: 2-3x cheaper than base model with shorter prompts
Total for 1M queries/month: $3,000-$10,000 (after initial training)
Agentic systems have higher latency (10-60s), higher cost (multiple LLM calls), and increased risk of hallucination or unintended actions. Start with constrained use cases and extensive testing.
| Model | Best For | Cost/1K tokens | Context Window | Considerations |
|---|---|---|---|---|
| GPT-4 Turbo | Complex reasoning, code generation | $0.01-0.03 | 128K tokens | Industry standard, best ecosystem support |
| Claude 3.5 Sonnet | Analysis, safety-critical, long documents | $0.003-0.015 | 200K tokens | Strong safety, excellent for enterprises |
| Llama 3.1 (70B/405B) | On-premise, data sovereignty | Infra costs only | 128K tokens | Open source, requires GPU infrastructure |
| GPT-3.5 Turbo | High-volume, cost-sensitive tasks | $0.0005-0.0015 | 16K tokens | 10x cheaper than GPT-4, good for simple tasks |
| Mistral Large | European data residency | $0.004-0.012 | 32K tokens | EU-compliant, competitive performance |
Dublin, Ireland
Global leader in enterprise data transformation with comprehensive capabilities from strategy through managed services. Platform Factory reduces GenAI deployment time by 30%.
New York, USA
Premium strategy house with specialized AI practice. Delivered 40% warehouse efficiency improvement through supply chain optimization. C-suite engagement focus.
Marlborough, USA
AI-first consultancy with strong cloud and MLOps focus. Google Cloud Premier Partner with advanced AI capabilities.
Boston, USA
Strategic consulting with deep AI capabilities. Focus on connecting business strategy with advanced analytics and ML model deployment.
Mumbai, India / New York, USA
Specialized analytics boutique with deep AI and decision science expertise. Proprietary frameworks and industry accelerators.
San Francisco, USA
Official Databricks consulting services. Deep platform expertise for Lakehouse architecture and MLOps implementations.
New York, USA
Big Four leader with 800+ clients on Deloitte Fabric platform. 92% renewal rate. Strong governance frameworks and compliance focus for regulated industries.
Armonk, USA
Enterprise consulting with proprietary Watson AI platform and hybrid cloud expertise. Strong in healthcare and financial services.
Paris, France
European systems integrator with strong industry focus. Comprehensive cloud and analytics capabilities.
RAG systems are only as good as the documents they retrieve. Outdated, inconsistent, or poorly structured content leads to poor responses. Budget 40-60% of project time for data preparation.
POC costs of $100/month can explode to $50K+/month at production scale. Implement cost monitoring, caching, and model routing (expensive model only when needed) from day one.
“It looks good” is not a metric. Define specific evaluation criteria: relevance, faithfulness, answer correctness, response time. Automate evaluation with LLM-as-judge and human spot checks.
Production issues are invisible without proper logging. Track: latency per step, token usage, retrieval quality scores, user feedback, error rates. Tools: LangSmith, Arize, Weights & Biases.
Same prompt can produce different outputs. Account for variability in downstream systems. Use temperature=0 for consistency, implement output parsing with error handling, validate structured outputs.
| Project Type | Implementation Cost | Timeline | Ongoing Monthly |
|---|---|---|---|
| RAG Chatbot (Internal Knowledge) | $100K - $400K | 3-6 months | $5K-$20K (API + infra) |
| Customer-Facing GenAI Product | $300K - $1M+ | 6-12 months | $20K-$100K+ (scale dependent) |
| Document Processing Pipeline | $150K - $500K | 4-8 months | $8K-$30K (volume dependent) |
| Agentic Workflow System | $400K - $1.5M+ | 9-18 months | $30K-$150K+ (complexity dependent) |
| Fine-Tuned Domain Model | $200K - $800K | 4-10 months | $10K-$50K (retraining cycles) |