LLM Infrastructure
Model selection, hosting, fine-tuning, cost optimization, and scaling LLM-powered systems in production.
Running large language models in production requires careful infrastructure planning—from model selection and hosting to fine-tuning, cost optimization, and GPU provisioning. Explore practical guides on building reliable, scalable LLM infrastructure that balances performance, cost, and latency for real-world applications.
595 articles in this category
Glean Alternative Self-Hosted: Enterprise AI Without the Managed-Cloud Tax
Glean runs in Glean's cloud and charges ~$40 per user per month. ibl.ai is the self-hosted alternative: runtime inside your VPC, model-agnostic, source-code ownership, no per-seat pricing. Same enterprise-search + agent + knowledge-work surface — different shape.
COPPA Compliant AI for Schools: Student Data Inside the District, Not in a Vendor's Cloud
COPPA-compliant AI for schools isn't about a vendor checkbox — it's about where student data lives during the inference call. ibl.ai's runtime executes inside the district's VPC, alongside the SIS and LMS, so under-13 student data never reaches a third-party AI vendor.
ChatGPT Gov Alternative: Self-Hosted Government AI Inside the ATO Boundary
ChatGPT Gov runs OpenAI's stack in a government cloud variant. ibl.ai is the alternative for agencies that need the runtime inside their own ATO boundary, with any LLM the agency authorizes (including locally-hosted open-weight) and audit logs in their own SIEM.
MagicSchool Alternative: District-Owned K-12 AI on Your Infrastructure
MagicSchool runs in MagicSchool's cloud and prices per teacher. ibl.ai is the district-controlled alternative: runtime executes inside the district's VPC, FERPA-protected student data stays inside the district, no per-teacher or per-student tax, multilingual via Qwen 3.
FERPA-Compliant AI Platform for Higher Education: By Deployment, Not by Promise
FERPA-compliant AI isn't about a vendor's BAA-equivalent — it's about where student records live during the inference call. ibl.ai's runtime executes inside the campus VPC alongside the SIS and LMS, so FERPA-protected records never leave the institution's perimeter.
Self-Hosted AI Agent Platform You Own: All the Code, All the Data
A self-hosted AI agent platform you own = the source code, the runtime, the model, and the data inside your infrastructure. ibl.ai is the platform: open-source runtime, perpetual license, any LLM, deploy anywhere, no per-seat pricing.
On-Premise Legal AI Platform: Privileged Work Product Inside the Firm's Network
An on-premise legal AI platform keeps privileged work product inside the firm's network — no third-party cloud custody, no DPA renewals, no ABA Rule 1.6 chain-of-custody questions. The deployment model, the workloads, and the cost math vs Harvey / Co:Counsel.
Air-Gapped AI for Federal Agencies: FedRAMP-High, IL4/IL5, and the Boundary That Doesn't Move
Air-gapped AI is often the only architecture that works for federal agencies handling CUI, CJIS, or IL4/IL5 workloads. Why managed gov-cloud variants fall short, what air-gapped actually means at agency scale, and how ibl.ai ships the deployment.
Self-Hosted Enterprise AI Platform: The Stack Your IT Owns End-to-End
Self-hosted enterprise AI platform = the runtime, the model, and the data inside your infrastructure. ibl.ai handles orchestration; your IT owns the stack. No per-seat tax, model-agnostic, source-code ownership.
Self-Hosted AI for Hospitals and Health Systems: The Deployment That Survives Audit
Self-hosted AI for hospitals and health systems means the runtime executes inside your existing HIPAA-covered environment — PHI never traverses a third-party cloud. The deployment options, the workloads, the cost math, and why this becomes the default endpoint for any serious clinical AI program.
HIPAA-Compliant AI Alternative: Self-Hosted Inside Your Covered Boundary
Managed HIPAA-aligned AI vendors put PHI in their cloud under a BAA you have to re-paper every quarter. ibl.ai is the alternative: self-hosted inside your HIPAA-covered environment, PHI never leaves your perimeter, any LLM, no per-clinician seat tax.
Harvey AI Alternative: Self-Hosted Legal AI Without Per-Lawyer Pricing
Harvey AI charges $300–500 per lawyer per month and keeps privileged documents in its cloud. ibl.ai is the self-hosted, model-agnostic alternative: same workloads (contract review, due diligence, brief-writing, deposition prep), 10–100× cheaper at scale, privileged data stays inside the firm's network.
Air-Gapped Clinical AI Platform: Inside the HIPAA Boundary, Not Beside It
Why an air-gapped clinical AI platform is the only architecture that survives a HIPAA-covered boundary review. The clinical workloads, the deployment model, the compliance math, and the difference between 'managed-cloud with a BAA' and 'inside the boundary.'
Enterprise AI with No Per-Seat Pricing: The Math at Scale
Per-seat AI pricing scales linearly with headcount regardless of actual use. For any enterprise above ~100 users it costs 10–100× more than usage-based or self-hosted for the same workload. The math, the shape problem, and what to deploy instead.
On-Device AI Agents Are Enterprise's Next Moat
NVIDIA's new on-device AI chip signals a fundamental shift in enterprise AI architecture — from cloud-dependent to edge-first.
Air-Gapped AI for Banks: Why FINRA + SR 11-7 Make It the Default
Why air-gapped deployment is the default — not the upgrade — for AI inside a bank. The FINRA, SR 11-7, GLBA, and examiner-subpoena math that pushes the AML, KYC, advisor, and trading workloads inside the bank's own perimeter.
What AI Customer Support Actually Costs in 2026
Per-ticket token math across the latest models, monthly bills at small / mid-market / enterprise scale, and why the per-conversation customer-support AI vendors (Intercom Fin at $0.99/conversation) are the wrong shape — especially at scale.
What AI Academic Advising Actually Costs in 2026
Per-conversation token math across the latest models, monthly bills at community college / regional / R1 scale, and why the per-student and per-advisor AI vendors are the wrong shape — even when 'student success' is the headline pitch.
What AI Tutoring Actually Costs in 2026 (K-12 + Higher Ed)
Per-session token math across the latest models, monthly bills at school / district / campus scale, and why the per-student edtech AI vendors are the wrong shape — even at $4/student/month.
What AI FOIA Drafting Actually Costs in 2026
Per-request token math for FOIA drafting across the latest models, monthly bills at municipal / county / state agency scale, and why the per-request and per-seat AI vendors are the wrong shape — including in the GovCloud variants.
What AI AML Alert Triage Actually Costs in 2026
Per-alert token math across the latest models, monthly bills at community / regional / global bank scale, and why the per-alert and per-analyst AI vendors are the wrong shape — even with SR 11-7 governance as the headline justification.
What AI Contract Review Actually Costs in 2026
Per-contract token math across the latest models, monthly bills at solo / mid-market / AmLaw scale, and why the per-document and per-lawyer AI vendors are the wrong shape — even when the math feels value-aligned.
What AI Prior Authorization Actually Costs in 2026
Per-letter token math for prior authorization across the latest models, monthly bills at community / regional / IDN scale, and why the per-transaction and per-clinician AI vendors are the wrong shape — even for the workload that started the AI-in-healthcare conversation.
AI Cost Math for Higher Education: Per-Seat vs Usage-Based in 2026
What AI actually costs a university in 2026 — token pricing for the latest models against per-seat ChatGPT Edu / Copilot bills for 30K students and 3K faculty, with academic advising and tutoring workload math and a campus-controlled deployment.