Running LLM as a service, AI Infrastructure

LLM Agency Implementation, Multi-Model Automation & Data Extraction

Learn how to build client delivery workflows using LLM's unified CLI interface to run prompts across OpenAI, Anthropic, Gemini, and local models without switching platforms. This course teaches agencies how to set up SQLite logging for audit trails, extract structured data from documents using JSON schemas, and productize batch automation services that reduce manual data work for clients.

Open the decision record for LLM

What does running LLM for clients commit you to?

Published figures for this service. Blank fields are not published.

Monthly tool cost
Not published, LLM is open-source with no listed vendor pricing tiers; your investment is developer labor plus provider API usage costs, which the agency must supply from provider billing.
Time to first value
Not published
Payback
Not modeled, client price, labor cost, provider usage, overhead, and expected volume are not supplied in the source data.
Guided implementation
8 hours

Is LLM worth running as a client service?

LLM is a capable developer utility for scripting multi-provider model access with SQLite logging and schema-based extraction, and it is viable as a developer-led managed service. The vendor is open-source with no published pricing tiers and no white-label or multi-client dashboard, so client pricing, margins, and ROI cannot be modeled from the supplied evidence.

An agency-fit judgement for reselling this service. It is separate from the tool description on the decision record.

Before you start

What has to be in place before the first client engagement.

Tools and subscriptions

  • Python environment for the LLM CLI and library
  • API keys for at least one remote provider (OpenAI, Anthropic, or Gemini)
  • Ollama or LM Studio installed if local model support is in scope
  • SQLite client for inspecting logged prompt/response records
  • JSON schema files defining structured extraction targets for each client

People and inputs

  • Developer proficient in command-line tools and Python scripting
  • Process for managing per-client API keys securely via 'llm keys set'
  • Schema design capability to define JSON extraction contracts with clients
  • Internal review step for prompt templates and tool-calling permissions before production use

Included with the course

7 working documents for delivering this service.

  • Multi-Model Prompt Routing Checklistchecklist
  • SQLite Audit Log Setup for Client Projectssop
  • JSON Schema Extraction Template Librarytemplate
  • LLM Batch Workflow Pricing Calculatorworksheet
  • API Key Management and Security Guideguide
  • Client Handoff Documentation for LLM Outputstemplate
  • Local Model vs. API Cost Comparison Worksheetworksheet

Listed by name. These documents are not yet published as individual downloads.