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Private AI

Plan, operate, and evaluate AI on your own infrastructure. Explore deployment costs, offline dependencies, performance, and recovery.

11 articles

Private AI
Which AI agent integrations work without internet access?
Assess offline AI agent integrations by checking endpoint location, authentication, dependencies, and data freshness rather than assuming local plugins are disconnected.
Sarath Chandran
Sarath Chandran
September 2, 20266 min read
Private AI
Updating self-hosted AI: what to test before changing versions
Plan version compatibility, representative task checks, migration rehearsals, and rollback decisions before updating a self-hosted AI deployment.
Sarath Chandran
Sarath Chandran
August 21, 20266 min read
Private AI
Backing up private AI: what needs to be recoverable?
Define a recoverable private AI backup across application data, files, configuration, indexes, and versioned artifacts, then prove it with an isolated restore.
Sarath Chandran
Sarath Chandran
August 7, 20266 min read
Private AI
Monitoring self-hosted AI: latency, throughput, and errors
Choose LLM monitoring metrics that separate queueing, first-token delay, generation, failures, and dependency health without collecting unnecessary task content.
Sarath Chandran
Sarath Chandran
July 28, 20269 min read
Private AI
High availability for private AI: finding single points of failure
Map the model service, databases, storage, queues, and network dependencies that determine whether a private AI application survives a component failure.
Sarath Chandran
Sarath Chandran
July 17, 20269 min read
Private AI
Why is a self-hosted LLM slow?
Troubleshoot a slow self-hosted LLM by isolating request length, waiting time, generation, concurrency, and serving configuration in controlled experiments.
Sarath Chandran
Sarath Chandran
July 7, 20265 min read
Private AI
Air-gapped AI deployment: requirements beyond installation
Plan the model artifacts, software dependencies, transfer process, updates, and recovery checks an air-gapped AI service needs after installation.
Sarath Chandran
Sarath Chandran
June 26, 20268 min read
Private AI
Sizing on-premises AI for your actual workloads
Size on-premises AI with representative requests, input lengths, peak concurrency, and service expectations instead of relying on employee counts.
Sarath Chandran
Sarath Chandran
June 16, 20266 min read
Private AI
Private AI costs: building a total-cost-of-ownership model
Build a private AI cost model that separates infrastructure, staffing, support, utilization, and successful workload completion using explicit assumptions.
Sarath Chandran
Sarath Chandran
June 5, 20265 min read
Private AI
On-premises AI privacy: which data still leaves your network?
Map model calls, telemetry, downloads, tools, identity, and support traffic to determine what an on-premises AI deployment actually sends outside its boundary.
Sarath Chandran
Sarath Chandran
May 26, 20266 min read
Private AI
Private AI deployment planning: on-premises, private cloud and air-gapped
Plan a private AI deployment around data boundaries, workloads, infrastructure ownership, integrations, updates, recovery, and evidence of production readiness.
Sarath Chandran
Sarath Chandran
May 15, 20268 min read

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