When developing an AI solution at enterprise scale—especially in Commerce—organizations often face a critical decision: build the technology stack and orchestration in-house, or partner with a specialized service provider. With the growth of large language models (LLMs) and generative AI, the complexity and cost of rolling out advanced AI (covering everything from data ingestion to inference) can be immense. This is particularly true when your customer base reaches tens of millions of monthly active users.

High Level Architecture of an Agentic AI Project                                

Data Layer	Structured eCommerce Data (transactions, user profiles, product catalogs)Unstructured Data (customer service logs, product reviews)Vector Stores / Embedding Databases (for retrieval-augmented generation, context injection)Storage & Data Lakes (for raw and processed data)
Agent Layer	Agent A (Qwin): Specializes in short, FAQ-style responses and product-related queries.Agent B (Llama): Handles creative or generative tasks (content creation, campaign text, chat flow).Agent C (DeepSeek): Handles deeper reasoning or multi-hop question answering tasks, possibly fine-tuned for domain-specific knowledge.(Potential Additional Agents): Recommendation agent, personalization agent, analytics agent, etc.
Coordinator / Orchestrator	A “controller” that receives requests from end-users, then dynamically decides which agent(s) to invoke.Facilitates agent-to-agent communication.Maintains context and state across multi-step interactions.
Frontend & Integration Layer	Customer Touchpoints: Website, Mobile App, Chatbot, Voice Assistant.Internal Tools: Customer service console, analytics dashboards for data scientists, marketing automation tools.
MLOps and DevOps	Continuous Integration / Continuous Deployment (CI/CD) pipelines.Infrastructure management (Kubernetes clusters, GPU availability, etc.).Monitoring & Observability (latency, cost, errors, drift).

Phases of a Agentic AI project

Below are the key phases, each culminating in deliverables and requiring iteration. These phases often happen in parallel or with overlaps, but it’s helpful to structure them linearly for budgeting and planning.

A. Data Preparation

1. Data Ingestion and Consolidation
   • ETL pipelines for eCommerce data (transactions, inventory, user behaviors).
   • Aggregation of user interaction logs, reviews, chat transcripts.
   • Tools involved: Spark, Kafka, Flume, or cloud-based data pipeline services.
2. Data Cleaning and Transformation
   • Handling duplicates, missing values, outliers.
   • Normalizing data formats (JSON, CSV, Parquet, etc.).
   • Ensuring consistent data across all markets and platforms.
3. Data Labeling and Annotation
   • For supervised fine-tuning or for RAG (Retrieval-Augmented Generation) test sets.
   • Tools: Labeling platforms (Labelbox, internal custom labeling system, etc.).
4. Data Governance & Security
   • Access control, GDPR/CCPA compliance, anonymization.
   • Setting up data retention policies.
5. Embedding Generation (optional within Data Prep or early LLM Training)
   • Generating embeddings for each chunk of text if you plan to do RAG.
   • Tools: Sentence-transformers, OpenAI embeddings, or a self-hosted embeddings model.

Deliverables:

B. LLM Training (RAG / Fine-Tuning)   

1. Foundational Model Selection & Architecture
   • You have multiple models (Qwin, Llama, DeepSeek). Decide which tasks each model is responsible for.
   • Some might remain few-shot; others might be fully fine-tuned.
2. Data Preparation for Training
   • Splitting data into training/validation/test sets.
   • Creating domain-specific corpora (product descriptions, chat logs, etc.).
   • Potentially transforming data into a knowledge base for RAG.
3. Fine-Tuning / Training Strategy
   • RAG Approach:
     • Use a pre-trained model + a retrieval mechanism (vector store).
     • Add domain knowledge by chunking relevant documents and injecting them into prompts.
   • Full Fine-Tuning:
     • Train the model on eCommerce domain data.
     • Integrate RLHF (Reinforcement Learning from Human Feedback) if you want higher alignment.
4. Training Infrastructure Setup
   • GPU/TPU clusters, HPC environment or a cloud-based training pipeline.
   • Possibly parallel training or multi-node training for large data sets.
5. Model Validation & Evaluation
   • Evaluate with domain-specific metrics: factual accuracy, brand tone compliance, etc.
   • Run stress tests for scaling with large concurrency.

Deliverables:

• Domain-tuned LLM(s) or RAG pipeline with tested retrieval.
• Clear evaluation metrics and performance thresholds.

C. Agent Building

1. Agent Construction
   • Agent A (Qwin): Specialized for Q&A. Possibly lighter model with narrower scope.
   • Agent B (Llama): Larger generative tasks; may integrate marketing or campaign text generation.
   • Agent C (DeepSeek): More advanced reasoning tasks or multi-hop queries.
2. Agent “Personality” & Protocols
   • Defining how each agent “speaks” or responds (tone, style).
   • Standardizing prompt format and context injection.
   • Setting up query limits, timeouts, fallback behaviors.
3. Agent Integration
   • RESTful APIs or gRPC endpoints for each model.
   • Mechanisms to hand off partial context or partial solutions from one agent to another.
4. Security & Compliance
   • Ensuring PII does not leak during agent interactions.
   • Role-based access control (some agents might handle sensitive data, others not).
5. Testing & Validation
   • End-to-end tests covering multi-agent orchestration.
   • Non-deterministic scenario tests (agent autonomy).

Deliverables:

• Agent-based microservices or orchestrated system.
• Fully tested multi-agent flow.

D. Inferencing

1. Deployment Environment
   • Real-time inference on GPUs or CPU-based servers (depending on scale and model size).
   • Low-latency architecture for user-facing queries.
   • Potential offline batch processing for marketing or recommendation tasks.
2. Autoscaling & Load Management
   • Horizontal scaling for peak traffic.
   • Caching partial answers or intermediate results.
   • Consider multi-region deployment to reduce latency globally.
3. Monitoring & Logging
   • Observability into latencies, token usage, concurrency.
   • Logging for error analysis, user behavior analysis.
4. Performance Optimization
   • Prompt engineering to reduce token usage or improve speed.
   • Quantization or distillation of models if cost or latency is too high.
5. A/B Testing & Continuous Improvement
   • Test different agent strategies and model versions.
   • Gradual rollout of new model versions to subsets of traffic.

Deliverables:

• Production-grade inferencing stack.
• Monitoring dashboards, scaling policies, performance metrics.

E. Orchestration Between Multiple Agents

1. Central Orchestrator / Controller
   • Receives requests, decides which agent(s) to call.
   • Potentially uses a chain-of-thought or blackboard approach to pass partial results.
   • Manages concurrency, agent selection logic, and fallback.
2. Context Management
   • Shared memory or ephemeral context store (like a conversation memory).
   • Ensuring each agent sees the relevant portion of the conversation or data only.
3. Agent Autonomy / Non-Determinism
   • Agents can call each other: e.g., Llama calls DeepSeek if it needs extra knowledge.
   • Must track calls to prevent infinite loops or repeated queries.
4. Exception Handling
   • If an agent is at capacity or fails, fallback to a second-best agent.
   • If an agent’s output is ambiguous, controller might re-ask or clarify.
5. Cost & Latency Governance
   • Each agent call has a cost in tokens and compute.
   • The orchestrator can set a maximum budget (time or cost) per request.

Deliverables:

• Fully functional multi-agent orchestration platform.
• SLA definitions for each agent (e.g., must respond within X ms).

Cost Considerations for Enterprise

Below is a structured list of cost drivers and variables to consider for each phase. Each line item has volume, unit cost, and duration.

A. Data Preparation Costs

Cost Driver	Variables / Units	Notes
Data Pipeline Setup	– Engineer hours (FTE cost)\n- Cloud infrastructure (VMs, storage, etc.)	Initial build + ongoing maintenance
ETL Tools & Licensing	– License fees (if any)\n- Number of seats / usage levels	Could be replaced by open-source or fully in-house solutions
Storage Costs	– Volume of data (TB)\n- Storage type (hot/cold)	For raw data, processed data, and backups
Data Labeling & Annotation	– Amount of data (tokens, documents)\n- Cost per label per item or hourly labeling staff rate	Scalability depends on brand coverage (products, categories, languages)
Data Cleaning & QA	– Engineering hours for cleaning scripts\n- Potential 3rd-party tools	Possibly iterative, especially for large eCommerce catalogs
Data Governance & Compliance	– Governance tool licensing\n- Compliance officer / data steward time	Must consider legal overhead and tooling (e.g., for anonymization)

B. LLM Training (RAG / Finetune) Costs

Cost Driver	Variables / Units	Notes
Model Licensing	– If you use a commercial foundation model\n- Per-model or per-seat licensing	Some open-source models (like Llama variants) may have usage restrictions depending on the license
Compute (GPU/TPU) for Training	– GPU hours or TPU hours\n- Cloud provider or on-prem HPC\n- Instance type (cost/hr)	Potentially the biggest single line item for training large LLMs
Data Preparation for Training	– Overlap with Data Prep above, but specifically for curated training sets\n- Engineering hours	Often repeated or incremental for each training cycle
Fine-Tuning / RAG Integration	– Engineer hours for pipeline setup\n- Additional compute for test runs	May also require specialized vector DB licensing or usage costs
Hyperparameter Tuning & Experimentation	– Additional GPU hours for iterative experiments	Could be 20–40% of the total training compute cost
Validation & Evaluation	– Cost for evaluation datasets\n- Data scientist hours for building eval metrics	Might require external human evaluation for brand compliance
Model Storage & Versioning	– Space for model checkpoints (GB/TB)\n- Model artifact management system	MLOps overhead

C. Agent Building (Agent Construction)

Cost Driver	Variables / Units	Notes
Development Resources	– AI engineers, MLOps engineers, software architects (FTE cost)\n- Duration of project	Overheads for multi-agent design and integration
Agent Framework / Tools	– License or usage cost for agent orchestration frameworks (if commercial)	Open-source frameworks exist, but may require in-house customization
API Integration	– Number of internal/external APIs to integrate\n- Dev hours for each	E.g., hooking up product database, user info, marketing platforms
Security & Compliance	– Dev hours for security audits\n- Additional tools or modules for securing prompts	Possibly mandatory due to scale and data sensitivity
Testing & QA	– Dev/QA hours for agent-based integration tests\n- Test environment costs	Non-deterministic flows may require more complex test scenarios
Documentation & Training	– Technical writers for internal training guides\n- Possibly external contractor time	Important for large teams or multi-lingual support

D. Inferencing Costs

Cost Driver	Variables / Units	Notes
Compute for Inference (GPU/CPU)	– Queries per second (QPS)\n- concurrency\n- cost per GPU hour or CPU hour	Possibly the largest ongoing cost at scale (50–100M MAUs)
Autoscaling & Load Balancers	– Additional overhead for scale (Kubernetes cluster, etc.)	Multiplied by geographic distribution
Model Serving Infrastructure	– Cloud or on-prem costs\n- Possibly specialized hardware for large LLMs	Node count, memory, networking, etc.
Maintenance & Upgrades	– Ongoing engineering to patch, update, retrain, or optimize inference	24/7 operational overhead
Observability & Monitoring	– Logging costs at scale (e.g., ELK stack, Datadog, Splunk)\n- Metrics storage (Prometheus)	High traffic means large logging volumes
A/B Testing & Model Iterations	– Running multiple versions in parallel\n- Additional compute for canary releases	Standard ML practice, but cost can be non-trivial

E. Orchestration Between Multiple Agents

Cost Driver	Variables / Units	Notes
Orchestrator Development	– Engineering hours for building & maintaining the orchestration logic	Potential complexity for concurrency and fallback logic
Context Storage / Shared Memory	– Database or in-memory store (Redis, etc.) costs\n- Scalability for high concurrency	Non-deterministic calls can explode the concurrency factor
Agent-to-Agent Communication	– Network overhead\n- Additional microservice calls	Each agent call might count as an inference call, so costs multiply with the number of calls per user request
Monitoring & Logging	– More granular logs (who called who, reason, partial results)\n- Data storage and analysis costs	Required to debug multi-agent loops or errors
Security & Access Control	– Additional overhead to ensure agent calls are authorized	Each agent might handle different data sensitivity levels

Additional / Overarching Costs

Cost Driver	Variables / Units	Notes
Personnel & Overhead	– Salaries for data scientists, ML engineers, DevOps, project managers	Typically the largest expense in many projects, especially for multi-year timeframes
Project Management	– PMO or scrum master overhead	Coordination across large cross-functional teams
Legal & Compliance	– GDPR, CCPA, PCI compliance for eCommerce\n- Possible external consulting fees	Must be baked into initial design
Risk & Contingency Reserves	– Additional 10–20% buffer for unknowns	Especially important for new AI technologies
Maintenance & Continuous Improvement	– Yearly or quarterly cost for improvements, new feature requests	AI systems rarely remain static

Putting It All Together: Detailed TCO Example Structure

Below is an example line-by-line TCO breakdown. You would fill in the actual numbers based on quotes from your cloud provider(s), your internal salaries, expected training durations, etc.

Phase A: Data Preparation (Year 1)

1. Data Pipeline Engineering: 2 FTEs × 6 months = 12 FTE-months; loaded cost per FTE-month = $X → $X total
2. Data Storage: 100 TB raw data; $Y / TB / month → $Y × 100 TB × 12 = $Z
3. Labeling/Annotation: 1 million items × $0.02 / item → $20,000
4. Data Governance Tool: $A / year licensing → $A

Phase B: LLM Training (Year 1, repeated as needed)

1. Model Licensing (if needed): $X annual fee or usage-based
2. GPU Compute (Training): E.g. 500 GPU hours at $Y/hour = $Z
3. Hyperparameter Tuning: Additional 200 GPU hours at $Y/hour = $Z’
4. Evaluation & QA: 1 FTE × 2 months = 2 FTE-months → $W

Phase C: AI Building (Year 1–2)

1. Agent Development: 3 AI Engineers × 6 months each → 18 FTE-months → $X
2. Integration & Security: 2 Security Engineers × 2 months → $Y
3. Testing & Documentation: 1 QA Engineer × 3 months → $Z

Phase D: Inferencing (Ongoing, Year 2+)

1. Production GPU/CPU:
   • Peak load: 10,000 QPS → requires N GPU nodes
   • Cost per GPU node: $X / month × N nodes × 12 = $…
2. Autoscaling & Orchestration: Additional overhead (Kubernetes, etc.) → $…
3. Logging & Monitoring: $A / million logs → with 50–100M MAUs, potentially huge → $…

Phase E: Multi-Agent Orchestration (Ongoing)

1. Controller Development & Maintenance: 2 FTEs year-round → 24 FTE-months → $X
2. Context Storage: Redis cluster or equivalent → $Y / month → $Z / year
3. Intra-Agent Call Overhead: This can be measured as increased usage of each model. E.g., if each request triggers on average 1.5 calls per user request, your inference cost grows by 50%.

Finally, sum all these to get your total project TCO. You can bucket them into CapEx (initial engineering, training) vs. OpEx (ongoing inference, maintenance) if that aligns with your financial reporting.

Key Variables & Considerations

1. User Concurrency & QPS
   • High MAUs can translate to tens of thousands of queries per second during peak hours.
   • This concurrency drives your inference hardware and scaling strategies.
2. Depth of Orchestration
   • If each user request triggers multiple agent calls, your inference cost can double, triple, or more.
3. Model Size & Complexity
   • Larger models → more GPU memory needed, higher inference latency, and cost.
   • Potential to use smaller distilled or quantized models for cost savings.
4. Training Frequency
   • How often you plan to retrain or fine-tune (monthly, quarterly?).
   • The more frequent the training, the higher your GPU costs.
5. Data Growth
   • Ongoing accumulation of logs, new products, new languages.
   • Need to periodically update embeddings for RAG.
6. Licensing
   • Some foundation models have usage restrictions or fees for commercial use.
   • Some vector databases or specialized AI frameworks are not fully open-source.
7. Risk Factor
   • Innovative multi-agent systems can have unexpected overhead in debugging, concurrency challenges, cost overruns.
8. MLOps Maturity
   • A well-developed MLOps pipeline can reduce operational overhead and help orchestrate everything automatically (CI/CD for models, automated retraining, etc.).