Text Classification API: Custom Models vs Generic Solutions

The Text Classification Landscape

Every application that processes text eventually needs classification. The question is: build, buy, or customize?

Option 1: Generic Cloud APIs

Examples

• Google Cloud Natural Language
• AWS Comprehend
• Azure Text Analytics
• IBM Watson NLU

What They Offer

Pre-trained models for common tasks:

• Sentiment analysis (positive/negative/neutral)
• Entity extraction (people, places, organizations)
• Language detection
• Content moderation

Pros

• Instant integration: No training required
• Scalable: Handle any volume
• Maintained: Provider handles updates

Cons

• Fixed categories: Can't add custom labels
• Generic accuracy: 70-80% on domain-specific content
• No customization: One-size-fits-all model
• Cost at scale: Per-character or per-request pricing adds up

Best For

• Quick prototypes
• Standard sentiment/entity tasks
• When 75% accuracy is acceptable

Option 2: Build Your Own ML Pipeline

What's Involved

• Data collection and labeling
• Model selection and architecture
• Training infrastructure (GPUs)
• Hyperparameter tuning
• Model serving and scaling
• Monitoring and retraining

Pros

• Full control: Exactly what you need
• Maximum accuracy: Optimized for your data
• No per-request fees: Fixed infrastructure cost

Cons

• High upfront investment: Months of development
• ML expertise required: Data scientists, MLOps engineers
• Infrastructure complexity: GPUs, serving, monitoring
• Ongoing maintenance: Models drift, need retraining

Best For

• Large companies with ML teams
• Core product features worth significant investment
• Unique requirements generic solutions can't meet

Option 3: TinyModels (Custom + Simple)

How It Works

1. Describe your categories in plain English
2. AI generates training data (or upload your own)
3. Fine-tune a model in minutes
4. Get an instant API endpoint

Pros

• Custom categories: Your labels, your model
• High accuracy: 85-95% on domain-specific tasks
• No ML expertise: Conversational interface
• Fast iteration: Retrain in minutes, not weeks
• Simple pricing: Per-prediction, no infrastructure costs

Cons

• Text-only: Not for images/audio (yet)
• Newer platform: Less enterprise history

Best For

• Teams needing custom classification without ML overhead
• Rapid prototyping with production-quality results
• Replacing generic APIs that aren't accurate enough

Comparison Table

When to Choose Each

Choose Generic APIs When:

• Standard sentiment/entity extraction is sufficient
• You need something working in hours
• Accuracy requirements are modest
• Volume is low enough that per-request costs are acceptable

Choose DIY ML When:

• Classification is a core competitive advantage
• You have an ML team and infrastructure
• You need maximum control and customization
• Volume justifies fixed infrastructure investment

Choose TinyModels When:

• Generic APIs don't have your categories
• You need custom classification without ML complexity
• Accuracy matters but you can't justify a full ML team
• You want to iterate quickly on classification logic

Migration Path

From Generic API to TinyModels

1. Export labeled data: Most generic APIs can export predictions
2. Review and correct: Fix mislabeled examples
3. Define custom categories: Add labels generic APIs couldn't handle
4. Train custom model: Upload data or let AI generate
5. A/B test: Run both in parallel, compare accuracy
6. Switch over: Update API endpoint

From DIY ML to TinyModels

1. Export training data: Your labeled examples
2. Upload to TinyModels: CSV with text + label columns
3. Train: Fine-tune on your data
4. Validate: Compare accuracy metrics
5. Simplify: Decommission ML infrastructure

API Specification

Endpoint

POST to https://api.morphllm.com/predict

Request Format

Send a JSON body with:

• model: Your classifier name
• text: The text to classify

Response Format

Returns:

• label: The predicted category
• confidence: Confidence score (0-1)
• all_scores: Scores for all categories

Batch Endpoint

POST to https://api.morphllm.com/predict/batch with an array of texts.

Rate Limits

• Standard: 100 requests/second
• Enterprise: Custom limits available

Get Started

Build a custom classifier in minutes. No ML expertise required. Start now.