How do we deploy AI on-prem for enterprise use?
Start with private inference, install verification, admin onboarding, identity-aware policy gates, secure retrieval, and full audit logging. Srasta is designed for this model from day one.
Private AI Deployment
Srasta runs inside your infrastructure with private inference, installation plans, host inventory, admin onboarding, runtime verification, model routing, memory boundaries, audit evidence, rollback, and sizing guidance for production workloads.
If you are evaluating quickly, follow this sequence to move from governance requirements to a practical deployment recommendation.
Map private inference, install control, admin onboarding, governance, audit, and recovery expectations.
Jump to section → Step 2Compare on-prem, private cloud, and hybrid options.
Jump to section → Step 3Use tier guidance and GPU classes for your expected scale.
Jump to section → Step 4Answer 3 questions for an indicative configuration.
Jump to section →The first deployment question is not only GPU size. It is whether private inference, install state, admin access, governance evidence, and recovery workflows can be verified by enterprise operators.
Run open-weight models on customer-controlled GPUs, route through an OpenAI-compatible gateway, and capacity-plan usage instead of accepting external token-meter surprise.
Plan/run state, host inventory, topology, placement, preflight checks, smoke verification, reset, rollback, and backup make deployment state explicit.
Onboard users and teams, connect SSO, grant roles, manage model access, view license posture, and track runtime health.
Audit auth, inference, memory, tools, and admin actions; evaluate policy and compliance-rule outcomes; export evidence when needed.
Srasta does not require public SaaS hosting. Private inference, admin, governance, and core data services operate inside your controlled infrastructure.
GPU servers in your data centre. Full control, zero external dependencies. Recommended for regulated industries and air-gapped requirements.
AWS, Azure, or GCP — deployed inside your VPC. No data leaves your cloud account. Supports GPU instance types across all major providers.
VMware, OpenStack, or Proxmox on your existing data centre infrastructure. Srasta deploys via Docker Compose or Kubernetes.
On-prem inference combined with cloud integrations. Run your models on owned hardware while connecting to cloud-based storage or services.
A practical lab configuration for demonstrating private inference, admin onboarding, governed retrieval, and operator workflows. Final production architecture depends on workload, concurrency, compliance scope, and availability targets.
Use this as a reference point for evaluation and demo planning, not a universal production recommendation. Srasta deployment scoping confirms the right hardware, topology, and controls for the customer environment.
Sizing scales with subscription tier and workload concurrency.
Lower VRAM requirements. Suitable for knowledge retrieval, document Q&A, and policy lookup. Runs on entry-level GPU hardware.
Recommended for agentic workflows, code intelligence, and multi-step reasoning. Requires A100 / H100 class GPU. Our reference deployment runs 30B FP8.
Mixture-of-Experts architectures require higher VRAM and benefit from multi-GPU routing. Srasta supports model pooling and hybrid routing strategies.
For production deployments, reliability and governance need to be designed together.
Prevents ingestion workloads from impacting active inference latency.
Isolate the vector store to its own node for reliable search performance at scale.
Track prompt quality, retrieval behavior, memory drift, policy decisions, compliance rules, latency, audit events, and cost before scaling.
Distribute across availability zones for resilience in AWS, Azure, and GCP environments.
Snapshot vector collections, knowledge ingestion pipelines, and governance configuration on a scheduled basis.
Optional but recommended for Enterprise Plus deployments with unpredictable concurrency.
Answer three questions to get an indicative configuration. For accurate sizing, schedule a deployment session with our team.
Select one option per step. Your estimated infrastructure appears after the third answer.
High-intent questions from infrastructure, security, and engineering leaders.
Start with private inference, install verification, admin onboarding, identity-aware policy gates, secure retrieval, and full audit logging. Srasta is designed for this model from day one.
On-prem or private cloud with customer-controlled inference, strict access controls, audit trails, admin visibility, and data residency constraints is typically the best fit for regulated organisations. This is why teams evaluate Srasta as a private AI governance platform for regulated companies.
Yes. Srasta supports private vector search, local embedding pipelines, and governed retrieval so enterprise knowledge never leaves your environment.
It depends on concurrency, model size, and use case. Use the sizing estimator for baseline planning, then confirm with readiness and pilot scoping.
We scope every deployment around the controls that matter: model selection, private inference economics, admin onboarding, memory boundaries, evaluation needs, compliance rules, GPU sizing, cloud vs on-prem trade-offs, and recovery expectations.