Deploying Enterprise AI Copilot: On-Premise vs Cloud

In a cloud deployment, your AI copilot relies on hosted LLM APIs (OpenAI, Anthropic, Google) and managed vector databases (Pinecone, Weaviate Cloud). Your data is sent over the internet to these providers for processing and the responses are returned to your application.

When cloud deployment makes sense

Cloud is typically the best starting point when:

• You handle fewer than 1000 queries per day
• You operate in a non-regulated industry without strict data residency requirements
• Your team wants to launch within 2–3 weeks
• You expect unpredictable or spiky query volumes and want automatic scaling
• You prefer ongoing API-based OPEX over upfront infrastructure CAPEX

Reference cloud architecture

• LLM API: Claude 4 Sonnet or GPT-4o via API
• Vector database: Pinecone Serverless or Weaviate Cloud
• Application server: AWS Lambda or ECS in the Mumbai region
• Document storage: Amazon S3 with encryption at rest

Cost breakdown (500 daily active users ≈ 2000 queries/day)

• LLM API costs: Rs 1,20,000–1,80,000/month

(Assuming ~800 tokens per query across prompts and responses)

• Pinecone Serverless: Rs 8,000–15,000/month
• AWS infrastructure (Lambda/ECS, networking, S3, monitoring): Rs 12,000–20,000/month

Total estimated monthly cost: Rs 1,40,000–2,15,000

Advantages of cloud deployment

• No upfront infrastructure cost: Start quickly without buying GPUs or servers.
• Automatic scaling: Handles spikes in usage without capacity planning.
• Latest models by default: Immediate access to new LLM versions and features.
• Minimal DevOps overhead: Provider manages most of the infrastructure complexity.

Risks and trade-offs

• Data leaves your network: Even with TLS and encryption, data traverses the public internet and resides (temporarily or longer) with third-party providers.
• Costs scale linearly with usage: As query volume grows, monthly bills can rise sharply.
• Latency overhead: Network round-trips to cloud APIs add ~500–2000 ms per query.
• Vendor lock-in: Architectures often become tightly coupled to specific LLM APIs and vector DBs.

In an on-premise deployment, the entire AI stack runs within your own controlled environment—your data center, a private cloud (e.g., AWS VPC with strict controls), or co-located servers. The LLM runs on your own GPU hardware, and no data needs to leave your network.

When on-premise deployment makes sense

On-prem is typically the right choice when:

• You operate in a regulated industry: banking, healthcare, insurance, government
• You must comply with DPDP Act 2023 data localization and similar mandates
• You handle more than 5000 queries per day (the cost crossover point vs cloud)
• Your knowledge base includes highly sensitive documents (financials, patient data, legal contracts)
• There is a board-level requirement for zero data exfiltration or external processing

Reference on-premise architecture

• LLM: Llama 3.1 70B or Mistral Large, served via vLLM or TGI
• GPU infrastructure: 2–4× NVIDIA A100 or H100 GPUs
• Vector database: Qdrant or Milvus (self-hosted)
• Application layer: Kubernetes on bare metal or private cloud

Cost breakdown (500 daily active users)

• GPU servers (2× A100 80GB): Rs 8,00,000–12,00,000 one-time

(or ~Rs 1,80,000/month using AWS p4d instances)

• Infrastructure and networking (racks, switches, storage): Rs 3,00,000–5,00,000 one-time
• Ongoing maintenance: Rs 50,000–80,000/month

(power, cooling, DevOps/ML ops, hardware support)

Total setup cost: Rs 11,00,000–17,00,000 (one-time)

Ongoing monthly cost: Rs 50,000–80,000

Advantages of on-premise deployment

• Complete data control: Data stays within your network boundary.
• Predictable costs at scale: No per-query API charges; marginal cost per query drops as volume grows.
• Lower latency: Inference happens locally without external network hops.
• Regulatory alignment: Easier to satisfy data localization and sectoral regulations.

Risks and trade-offs

• High upfront CAPEX: GPUs, servers, networking, and storage require significant initial investment.
• Operational complexity: You own GPU maintenance, capacity planning, and model lifecycle management.
• Manual model updates: New model versions require testing and redeployment.
• Specialized skills required: Need in-house or partner ML/DevOps expertise.

Break-even analysis

For most Indian enterprises, on-premise becomes cheaper than cloud when you cross roughly 3000–5000 queries per day, depending on:

• Average tokens per query
• Required model size and latency
• Power and cooling costs

Below this threshold, cloud is usually more cost-effective and operationally simpler.

A hybrid deployment combines the strengths of cloud and on-premise. Most Indian enterprises find this to be the most practical long-term approach: use cloud APIs for general, low-risk queries while keeping sensitive data and workloads on-premise.

This allows you to:

• Leverage cloud model quality and agility
• Maintain on-premise security and compliance for critical data
• Optimize costs by routing only the right workloads to each environment

Hybrid architecture patterns

Pattern 1 — Classification-based routing

A lightweight classifier (running locally) inspects each query and routes it appropriately:

• General queries (e.g., company policies, public documentation) → Cloud LLM (Claude/GPT-4)
• Sensitive queries (financials, HR data, regulated content) → On-premise LLM

This ensures sensitive content never leaves your network while still benefiting from cloud for low-risk use cases.

Pattern 2 — Data-tier separation

All document processing and embedding generation happens on-premise. Only the final prompt, with anonymized or redacted context, is sent to a cloud LLM.

• Raw documents and embeddings stay within your infrastructure
• Cloud sees only abstracted or masked context
• You retain strong control over data exposure while using best-in-class cloud models

Pattern 3 — Cloud primary with on-premise fallback

Cloud LLMs handle all queries under normal conditions. On-premise LLMs are used when:

• Cloud providers experience outages or latency issues
• Queries involve compliance-tagged documents
• API costs exceed predefined monthly budgets and traffic needs to be throttled or re-routed

Cost breakdown (500 daily active users)

Assuming ~2000 queries/day:

• Cloud API (70% of queries): Rs 85,000–1,30,000/month
• On-premise GPU (30% sensitive queries):

– ~Rs 1,20,000/month (CAPEX amortized over 24 months), or

– ~Rs 90,000/month using cloud GPU instances

• Infrastructure (networking, storage, monitoring): Rs 15,000–25,000/month

Total estimated monthly cost: Rs 1,90,000–2,45,000

with full compliance coverage for sensitive workloads.

Why hybrid works well in India

• Compliance where it matters: Sensitive data stays local; general workloads use cloud.
• Cost optimization: High-volume, low-risk queries can be offloaded to the most economical environment.
• Gradual migration path: Start cloud-first, then introduce on-premise for specific workloads as usage and compliance needs grow.

Security posture differs significantly across cloud, on-premise, and hybrid deployments. Indian enterprises must align their choice with both DPDP Act 2023 and sector-specific regulations.

Data in transit

• Cloud:
• Encrypted using TLS 1.3 or equivalent.
• Data traverses the public internet to provider endpoints.
• On-premise:
• Traffic remains within your private network.
• No external transmission is required for inference.
• Hybrid:
• Classified/sensitive data stays local.
• General queries use encrypted channels to cloud providers.

Data at rest

• Cloud:
• Provider-managed encryption at rest.
• You may rely on provider keys or, with some vendors, bring your own keys (BYOK).
• On-premise:
• Full control over encryption keys, storage, and access policies.
• Easier to align with internal security standards and audits.
• Hybrid:
• Sensitive data is encrypted and stored locally.
• Non-sensitive data follows cloud provider encryption practices.

Compliance mapping for Indian enterprises

• DPDP Act 2023:
• On-premise or hybrid with data-tier separation best satisfies data localization requirements.
• RBI guidelines (banking):
• On-premise strongly recommended for core banking and customer data.
• Cloud may be acceptable with strict contractual safeguards and approved architectures.
• SEBI (financial services):
• On-premise preferred for trading and market-sensitive data.
• Cloud acceptable for general operations and non-critical workloads.
• Healthcare (HIPAA-equivalent expectations):
• On-premise for patient-identifiable data.
• Cloud with appropriate agreements (e.g., BAA-like constructs) for general operations.

Audit and monitoring

Regardless of deployment model, you should implement:

• Query logging (with appropriate masking/anonymization)
• Access audit trails for users and admins
• Response monitoring for quality, bias, and policy violations

On-premise gives you full control over audit data and retention policies. In cloud, you must rely partly on provider audit capabilities and APIs. A best practice is to maintain local copies of all critical audit logs even when using cloud services.

Choosing the right deployment model is ultimately a business decision informed by query volume, regulatory exposure, internal capabilities, and time-to-value.

Choose Cloud if:

• Query volume is under 1000/day
• Your knowledge base contains no regulated or highly sensitive data
• Your team lacks GPU/ML infrastructure expertise
• Speed to deployment (2–3 weeks) is the top priority

Choose On-Premise if:

• You operate in a regulated industry with strict data localization or confidentiality requirements
• Query volume exceeds 5000/day
• You already have or plan to build GPU infrastructure or a managed data center
• You have zero tolerance for data leaving your network

Choose Hybrid if:

• You manage a mix of sensitive and general content
• You want to start with cloud and gradually migrate sensitive workloads on-premise
• You need compliance coverage but prefer cloud model quality where possible
• Your query volume is 1000–5000/day, where cost and compliance both matter

Most Boolean & Beyond clients follow a phased approach:

1. Start with cloud to validate the use case, measure adoption, and refine workflows.
2. Introduce hybrid by adding on-premise components for sensitive data and high-volume workloads.
3. Optimize long-term architecture based on real usage, compliance findings, and cost curves.

Boolean & Beyond has hands-on experience deploying AI copilots across cloud, on-premise, and hybrid models for Indian enterprises.

End-to-end deployment expertise

• Cloud deployments: Launch production copilots in as little as 2 weeks, including integration with your existing tools and identity systems.
• On-premise installations: Deploy in bank and enterprise data centers, including hardened security configurations and compliance documentation.

What we handle for you

• GPU procurement and setup: Sizing, vendor coordination, installation, and benchmarking.
• LLM fine-tuning and alignment: Tailoring models (Llama, Mistral, etc.) to your domain and policies.
• Vector database optimization: Designing schemas, indexing strategies, and retrieval pipelines for performance and relevance.
• Monitoring and observability: End-to-end monitoring for latency, cost, quality, and security.

For hybrid deployments, we also build the query classification and routing layer that:

• Automatically identifies sensitive vs general queries
• Routes each request to the appropriate backend (cloud or on-premise)
• Enforces policy-based controls and budget thresholds

Seamless migration path

When your initial cloud deployment outgrows its cost-effectiveness or compliance requirements tighten, Boolean & Beyond manages the transition to hybrid or fully on-premise without disrupting end users:

Explore more from our AI solutions library:

• RBI & DPDP Act Compliance for AI Systems in India — Ensure your enterprise AI deployment meets India's data protection and financial compliance requirements.
• Smart Factory Implementation: MES to AI Integration Roadmap — See how manufacturing enterprises deploy on-premise AI for quality control and predictive maintenance.