LLMs that stay inside your walls
Run large language models safely within your enterprise ecosystem.
Why on-prem
Your data, your rules
Why enterprises choose on-prem GenAI:
What we deploy
From model to stack
Calsoft builds complete on-prem LLM stacks:
LLaMA 2, Mistral, Mixtral, Falcon, MPT, Orca, Gemma
GPU/TPU inference optimization (vLLM, TGI, Ollama, Triton)
Embedding + vector search using FAISS, Qdrant, Weaviate
Chat orchestration with LangChain, RAG layers, API wrappers
Role-based agent deployment with retrieval boundaries
Integration with enterprise identity (LDAP, SSO, OAuth2)
Infrastructure planning
We size it right
We analyze:
Model type (7B, 13B, 70B), tokens/sec, concurrency
Hardware footprint (NVIDIA A100, H100, L40S, etc.)
Workload balancing (streaming vs batch)
Memory and storage planning for context caching
Disaster recovery and HA (High Availability) plans
Security & governance
Compliant by design
We enforce:
- RBAC and ABAC policy hooks
- Token-level and context window audit logging
- Prompt injection shielding and response filtering
- Red-teaming for adversarial prompt resilience
- Encryption at rest + runtime + in vector search
- Full isolation via VPC or on-prem cluster
Launch vertical chatbots 2x faster.
Outcomes delivered
Build once. Scale internally
Here’s how we get started
Metric
Cloud
On-Prem
Compliance readiness
Limited
Full (HIPAA, ISO, SOC2)
Cost per 1M tokens
$0.10–$0.25
~$0.005
Network dependency
High
None
Downtime risk
Vendor-linked
Controlled
Customization depth
Moderate
Deep (LoRA, Fine-tuning)
How to start
Deploy in 4 steps
Define the Use Case
Identify high-sensitivity GenAI needs (e.g., healthcare, banking, defense).
Select the Stack
Choose open-source model, embedding method, vector DB, and serving layer.
Plan the Infra
Estimate GPU, memory, storage needs; configure HA and failover.
Deploy + Monitor
Launch in your secure environment, integrate with tools, and govern usage.
