Using Claude Haiku as a Gatekeeper to Reduce Sonnet API Costs by 80%

A developer shared a cost-saving pattern for processing large volumes of unstructured text through Claude AI models. The approach uses Claude Haiku as a gatekeeper to filter out irrelevant content before sending only valuable data to the more expensive Claude Sonnet model.

The Problem and Solution

The developer built a platform called PainSignal (painsignal.net) that pulls thousands of real comments from workers and business owners across different industries, then classifies them into structured app ideas. Most input was garbage — comments like "great video" or "first" or random noise. Sending all of that to Sonnet would be insanely expensive.

The Two-Stage Pipeline

Stage 1 — Haiku as a gate: Every comment hits Haiku first with a simple prompt: "Does this comment contain a real frustration, complaint, or unmet need related to someone's work?" It returns a yes/no and a confidence score. This takes fractions of a cent per call and filters out about 85% of the input.

Stage 2 — Sonnet for the real work: Only the comments that pass the gate go to Sonnet. This is where the expensive processing happens — it extracts the core pain point, classifies it into an industry and category (no predefined list, it builds the taxonomy dynamically), assigns a severity score, and generates app concepts with features and revenue models.

Results and Implementation Details

The result is running Sonnet on approximately 15% of total input instead of 100%, creating massive cost savings when processing thousands of comments.

Key learnings from the implementation:

• Haiku is surprisingly good at the gate job — it catches real complaints consistently with few false negatives
• The dynamic taxonomy approach (letting Sonnet decide categories rather than defining them upfront) found categories the developer never would have thought of
• Batching helps on the Sonnet side — everything is queued through BullMQ and processed in controlled batches to avoid slamming the API

The entire system was built with Claude Code using Next.js, Postgres with pgvector, and related technologies.