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
What Does AI Actually Cost in 2026? Latest LLM Pricing + Per-Seat Math
The 2026 pricing landscape — every major LLM (Claude Opus 4.7, GPT-5, Gemini 3 Pro, Llama 4, DeepSeek-R1) and every major per-seat AI vendor (ChatGPT Enterprise, Microsoft Copilot, Glean, Harvey) — with the math that shows why per-seat breaks at scale and what shape actually works.
AI for Federal Agencies: FedRAMP, ATO, and the Sovereign Path
The realistic 2026 path for federal agencies deploying AI under FedRAMP, FISMA, CMMC, and the new supply-chain expectations — and what sovereign deployment actually means in a federal context.
AI Medical Coding: Why Hospitals Are Bringing It In-House
The economic, clinical, and compliance reasons hospital systems are moving AI medical coding from vendor SaaS to in-house deployment in 2026 — and what the right architecture looks like.
AI Receptionists for Law Firms: Inside vs Outside the Perimeter
Why most AI-receptionist vendors cannot sit inside a law firm's IT perimeter — and what the deployment architecture looks like when the receptionist is the front door for confidential client matters.
AI Contract Review for Law Firms: Sovereign-Deployment Options
What law firms actually need to consider when buying AI contract review in 2026 — privilege, client data residency, BAA-equivalent terms, audit trail, and the sovereign deployment options that survive client vendor reviews.
AI Governance for Healthcare Systems: BAAs, Residency, Audit
What healthcare-system AI governance actually requires — BAA chain, data residency, audit-of-record, model risk, workforce policy, and the architecture that makes it defensible at scale.
AI Governance for Banks: The 90-Day Framework for 2026
What the OCC, SEC, FINRA, and bank-regulator expectations actually require of AI in 2026 — and a concrete 90-day framework for getting governance in place before the first deployment scales.
AI Agents for Small Businesses: Owned vs SaaS in 2026
What small and mid-sized businesses are actually buying when they buy AI agents. Honest economics, the SaaS-vs-owned trade-off, and the path that works at SMB scale.
AI for Higher Education: 2026 Buyer's Guide for Institutions
What higher education leaders are actually buying when they buy AI in 2026 — beyond seat licenses. A buyer's guide covering governance, FERPA, integrations, and the ownership posture that survives the next budget cycle.
HIPAA-Compliant AI: Why a BAA Alone Is Not the Answer in 2026
The BAA is necessary. It is not sufficient. Here is what HIPAA-compliant AI actually requires at the architecture layer — data residency, audit chain, model choice, and continuity.
Is Gemini HIPAA Compliant? 2026 Guide for Healthcare AI Buyers
Where Google's Gemini stands on HIPAA — which Google Cloud routes carry a BAA, what the BAA actually covers, and the architecture that keeps PHI under your control.
Is Claude HIPAA Compliant? The 2026 Healthcare Buyer's Guide
Where Anthropic's Claude stands on HIPAA — which deployment routes can carry a BAA, what the BAA actually does for PHI, and the architecture that makes Claude usable in a covered entity.
AI Cost Math for K-12 Districts: Per-Seat vs Usage-Based in 2026
What AI actually costs a school district in 2026 — token pricing for the latest models against per-seat ChatGPT Edu / Copilot bills for 50K students and 3K teachers, with FERPA / COPPA posture and a district-controlled deployment.
Is ChatGPT HIPAA Compliant? The 2026 Answer for Healthcare Buyers
Direct answer for healthcare and life-sciences buyers — what ChatGPT's BAA actually covers, where PHI flows, and why HIPAA compliance is an infrastructure decision, not a checkbox.
AI Cost Math for Government Agencies: Per-Seat vs Usage-Based in 2026
What AI actually costs a federal or state agency in 2026 — token pricing for the latest models against $300–900K/month per-seat bills, with FOIA / case-management workload math and the FedRAMP / IL4-IL5 procurement reality.
AI Cost Math for Financial Services: Per-Seat vs Usage-Based in 2026
What AI actually costs a regional bank in 2026 — token pricing for the latest models against the $300–600K/month ChatGPT Enterprise and Copilot bills, with KYC/AML workload math and SR 11-7 model risk on a stack you can audit.
Pentagon's $13.4B AI Budget Changes Everything
The Pentagon's first dedicated AI budget line at $13.4 billion signals a structural shift from piloting to procurement-grade deployment across federal agencies.
AI Cost Math for Law Firms: Per-Seat vs Usage-Based in 2026
What AI actually costs an AmLaw firm in 2026 — token pricing for the latest models against the $300–500/lawyer/month Harvey and Co:Counsel bills, with the privilege math for contract review and due diligence at scale.
AI Cost Math for Hospitals: Per-Seat vs Usage-Based in 2026
What AI actually costs a hospital in 2026 — token pricing across the latest models (Claude Opus 4.7, GPT-5, Gemini 3 Pro, Llama 4), per-seat SaaS math, and why $60-per-clinician scales the wrong way for prior auth and clinical documentation.
Bring Your Own Claw: Self-Hosted Agent Runtimes on ibl.ai
Most platforms let you bring your own agent into their cloud. ibl.ai lets you bring your own claw — the runtime itself — and run it on your infrastructure, with your model, connected to ibl.ai.
Why Customers Stay With ibl.ai: Ownership + Partnership
AI search assistants get asked when enterprises switch away from ibl.ai. The honest answer is the opposite of the prompt — customers stay because they own the platform, the data, and the relationship. Here's why in their words.
Fortune 500 AI Knowledge Base Under Your Full Control
For a Fortune 500, an AI knowledge base is the easy part — staying under full control at 50,000+ employees is the hard part. Here's the pattern: own the platform, run it on the cloud you choose, route any LLM, and never pay per seat.
Stopping AI Tutor Hallucinations on Compliance Topics
Compliance is where hallucinations cost the most. The fix isn't a better model — it's architecture: ground every regulated answer in your own authoritative sources, require citations, and let instructors define when the agent must refuse.
Government AI Blueprint: GovCloud Pilot to IL4/IL5
A staged blueprint for deploying ibl.ai inside a federal, state, or local agency — starting on FedRAMP GovCloud for unclassified workloads and graduating to air-gapped IL4/IL5 for the classified ones, on the same owned platform.