LLM Classification vs Fine-Tuning vs Distillation: Which to Use?
A technical comparison of direct LLM classification, fine-tuning, and distillation/compilation — with a decision tree for choosing the right approach.
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Deep dives on compiled models, latency evaluation, and the economics of replacing LLM API calls.
A technical comparison of direct LLM classification, fine-tuning, and distillation/compilation — with a decision tree for choosing the right approach.
Compiled Decision Intelligence turns labelled decisions into fast, deployable classifiers. A guide to the three-stage pipeline, training process, and when to use it.
Step-by-step setup for Sparkient's cloud and local MCP servers in Claude Desktop, Cursor, and VS Code.
Deploy ONNX classifiers to air-gapped environments, IoT devices, and edge locations, then benchmark local inference without network calls.
Use an LLM as a teacher to generate candidate training data, then evaluate a moderation classifier without requiring a historical customer dataset.
A technical deep-dive into the teacher-student pattern: use an LLM to generate candidate labelled data, then train and evaluate a task-specific classifier.
Compare human review, free APIs, LLM calls, commercial services, and compiled models using your real traffic, quality requirements, and operating costs.
Step-by-step guide to evaluating content moderation in a Next.js app with Sparkient through a cloud API route or local sidecar.
Build and evaluate a pre-action decision gate for LangChain agents using rules and a compiled classifier.
A practical checklist for deciding when to evaluate a compiled classifier—and when to keep the current LLM path.
A practical framework for comparing Perspective, OpenAI, Azure, Hive, and Sparkient on latency, accuracy, cost, policy control, and media support.
A practical framework for comparing rules, local models, compiled APIs, and live LLMs without treating provider latency as a constant.
A step-by-step guide to finding LLM calls, measuring what each one costs, and identifying bounded decisions worth testing with a compiled classifier.
Putting an LLM call in your synchronous request handler creates unpredictable latency, cascade failures, and cost scaling problems. Here are the alternatives — and when each one applies.
Traditional ML classifiers need labelled data and a maintained training workflow. Here are four approaches for a project that does not have either yet.
If a prompt makes a bounded decision, test whether labelled examples can train a sub-100ms classifier for the normal runtime path.
Trace an agent loop, identify bounded decisions, and test whether a compiled classifier improves its measured quality, latency, and cost profile.
Compare every content moderation approach by latency and accuracy: keyword filters, Perspective API, OpenAI Moderation, GPT-4o, and compiled classifiers.
Stuck between rules that are fast but dumb and LLMs that are smart but slow? Here's how a three-stage pipeline gives you both speed and accuracy.
A cost breakdown of every alternative to GPT-4o for classification workloads: cheaper models, fine-tuning, open-source, and compiled classifiers.
You've diagnosed the problem: the LLM call is the bottleneck. Here are four options to fix it, with real latency numbers and code for each.
An illustrative scaling scenario for finding latency, concurrency, reliability, and cost limits before traffic exposes them.
Five architecture patterns that can create latency, concurrency, reliability, or cost problems as traffic grows—and how to test and replace them.
Your API response time jumped from 50ms to 900ms. Here's how to find and fix the LLM call hiding in your request handler.