Sub-60ms inference · 89% accuracy

Crypto news in. Model-ready sentiment out.

One API call turns any headline or article into a structured sentiment score your prediction models can use — in under 60ms.

100 free credits on signup · No credit card required

POST/v1/sentiment47ms

Request

"text": "Bitcoin ETF approval sparks record institutional inflows"

Response

{
"sentiment": "positive",
"score": 0.87,
"confidence": "high",
"cached": false
}

<60ms

Inference latency

89%

Model accuracy

99.9%

API uptime

How it works

Three steps to structured sentiment

No ML expertise required. Send text, get a score. It's that simple.

01

Send your text

POST any crypto headline, tweet, or article to a single endpoint. Raw text in — up to 140 characters.

POST /v1/sentiment
{ "text": "SEC hints at BTC ETF..." }
02

We extract the signal

Our crypto-tuned NLP model classifies sentiment and scores confidence in under 60ms.

Classifying: sentiment,
score, confidence
03

You get structured output

A sentiment label, a score from 0 to 1, and a confidence level — ready for your feature vector.

{ "sentiment": "positive",
  "score": 0.82,
  "confidence": "high" }

Inside the model

Meet bb-sentiment-1, our latest production release

bb-sentiment-1 is our current flagship release, tuned to turn headlines, articles, and fast-moving market language into dependable sentiment signals for research and trading systems.

Explore model details
Latestbb-sentiment-1

Tech specs

Accuracy

89%

Current validation benchmark

Latency

<60ms

Average inference response

Parameters

>400M

Number of parameters in the model

Dataset

Large curated corpus

Headlines, articles, and crypto-native market language.

Validation

Advanced release gates

Holdouts, filtering, and repeated regression checks.

Architecture

Modern BERT methods

Context-aware encoder training for nuanced sentiment.

Release notes

bb-sentiment-1

Latest

Large dataset

Trained on a large, curated dataset of crypto headlines, news stories, and market commentary so the model learns both short-form catalysts and longer contextual narratives.

Advanced validation

Advanced validation combines strict holdouts, noisy-sample screening, and repeated regression checks across multiple text formats before a release is promoted.

Modern BERT methods

Built with modern BERT-style encoder methods so the model captures context, negation, tone shifts, and domain-specific phrasing more reliably than keyword-driven sentiment pipelines.

The problem we solve

Raw text in. Market signal out.

Stop building sentiment models. Start using sentiment data.

Without BitBabble

“SEC Chair hints at Bitcoin regulation changes amid growing institutional pressure”

Unstructured text. No numeric signal. Can't feed this to a model.

With BitBabble

Same headline, one API call later:

{
"sentiment": "positive",
"score": 0.82,
"confidence": "high"
}
Drop this straight into your feature vector

Features

Designed for prediction models

Clean inputs, predictable outputs, and infrastructure that stays fast when markets get noisy.

Sub-60ms inference

<60msavg response

Low enough latency for real-time trading decisions and live data pipelines. No batching required.

Crypto-native model

89%accuracy

Trained on crypto market language — ETF approvals, halvings, liquidations, regulatory nuance.

One endpoint, one key

1endpoint

JSON in, JSON out. Integrate in minutes. No SDKs, no dependencies, no configuration.

Burst-ready infrastructure

99.9%uptime

Scales automatically during market events. Your sentiment pipeline handles volume spikes.

Use cases

Built for teams that move markets

From solo quants to institutional platforms — structured sentiment fits wherever you need it.

Algorithmic trading

Add sentiment as a real-time feature in your signals. Gate entries and exits on headline tone.

Hedge fund research

Enrich pipelines with structured sentiment from news sources across the entire crypto market.

Trading bots

Add a news-aware layer to automated strategies. Check sentiment before every execution.

Dashboards & monitoring

Build tools that track real-time sentiment across assets alongside price and volume.

Pricing

Simple, credit-based pricing

Pay only for what you use. No subscriptions, no minimums, no surprises.

Credit-based usage

10 credits per inference · 1 credit for cached results

10

credits / inference

100

free credits on signup

$0

monthly minimum

Starter

$10

500 credits · $0.020/credit

Popular

Growth

$40

2,500 credits · $0.016/credit

Custom — 5,000+ credits

Pick your amount, pay less per credit as you scale.

$0.012/credit

5,000–9,999

$0.010/credit

10,000–24,999

$0.008/credit

25,000–49,999

$0.006/credit

50,000+

  • 100 free credits on signup — no credit card required
  • Credits never expire
  • Full API access from day one
  • Buy more credits anytime as you scale
Get Started

Need enterprise volume? Contact us

Get started today

Your model is only as good as its inputs.

Add crypto sentiment as a feature in minutes. Every signup gets 100 free credits — no credit card required.