Low cost, faster
AI classification
<200 ms latency · 10x cheaper than GPT-5-mini
Built for our own apps. Now open to everyone.
The Problem
You're overpaying for simple decisions.
You need to sort a support ticket. Detect spam. Route a phone call. Tag a product image. So you:
Write "Hope-driven" prompts
Spend hours tweaking adjectives to prevent hallucinations.
Parse brittle JSON
Write regex to catch the LLM when it forgets a closing bracket.
Wait & Pay
Pay $1.00+ per 1k requests for a 5-second response.
It works. But it's slow, expensive, and embarrassingly over-engineered for a task that should take milliseconds.
The Solution
One line. Any input. Native strings.
Classer handles the heavy lifting of OCR, vision-encoding, and text-embedding. You get a deterministic label and a calibrated confidence score.
import classer
# No prompt engineering. No JSON parsing.
result = classer.classify(
source="I can't log in and need a password reset.",
labels=["billing", "technical_support", "sales", "spam"]
)
print(result.label) # "technical_support"The Journey
Start in 60 seconds. Improve without ML engineers.
Zero-shot
Just pass your labels. It works out of the box.
Monitor
See every prediction in your console. Inspect confidence scores. Spot edge cases.
Correct
Label a few examples. Add class descriptions. Optionally write custom prompts.
Auto-improve
Turn on auto-calibration. Our system identifies low-confidence predictions, labels them with heavy LLMs, and fine-tunes a model unique to your account—automatically.
You stay focused on your product. The model gets smarter in the background.
Comparison
How we compare to General-Purpose LLMs
| Feature | Frontier LLMs (GPT/Gemini) | Classer.ai |
|---|---|---|
| Latency | 2–60 seconds | <200ms (P95) |
| Schema Drift | High (JSON can break) | Zero (Native string/enum) |
| Cold Start | 2s - 5s (TTFT) | <50ms |
| Reliability | Hallucinates / Refusals | Deterministic Labels |
| Maintenance | Brittle prompt versioning | Auto-calibrating weights |
| Cost | ~$10.00+ per 1M tokens | $0.10 per 1M tokens |
Pricing
Simple, transparent, cheap.
Base rate: $0.10 per 1M tokens
| Tier | Latency SLA | Best For | Multiplier |
|---|---|---|---|
| Real-time | P95 <200ms | UX-blocking tasks (Chatbots) | 10x |
| Standard | P95 <1s | Backend routing/Triage | 1x |
| High-Throughput | P95 <10s | Batch processing / Indexing | 0.1x |
| Batch | 24h | Historical data re-tagging | 0.01x |
Free tier: 10M tokens/month on High-Throughput. No credit card required.
Enterprise: On-prem deployment, SOC2/HIPAA compliance, and version pinning.
The Engine
From Zero-Shot to SOTA: The Auto-Calibration Pipeline
We bridge the gap between expensive frontier LLMs and high-performance production inference using a Student-Teacher architecture.
Just pass your labels. Our base models (optimized Vision-Transformers) classify your data out of the box.
You set an Ambiguity Threshold (e.g., conf < 0.85). Predictions below this are flagged for the calibration queue.
Flagged samples are labeled asynchronously by a high-reasoning "Teacher" model to find the ground truth.
Classer uses parameter-efficient fine-tuning to create a private model version unique to your account, trained specifically on your edge cases.
New weights are backtested against your "Gold Set" to ensure no regressions before being promoted to production.
FAQ
Frequently Asked Questions
Stop babysitting prompts.
Get your API key in 30 seconds. First 10M tokens free.