What can I build with Datastreamer?

Datastreamer is used to build Data Streams that collect, process, and deliver web and social data. Common use cases include:

• Social listening and brand monitoring
• Threat intelligence and dark web monitoring
• Competitive intelligence
• News and media monitoring
• Market research and consumer insights
• Compliance and risk monitoring

The platform was designed for high-volume, low-latency processing of semi-structured web data.

How does scaling work?

The pipeline components within a Data Stream scale automatically. The underlying infrastructure monitors resource usage across components and scales up or down within seconds based on demand.

This is part of why pricing is usage-based. Costs reflect actual consumption, which can vary month to month.

What are Add-Ons?

Add-Ons are capabilities you can include in a Data Stream beyond what is available by default. They come from Datastreamer's partner and provider ecosystem and include AI and NLP models, specialized data sources, scraping networks, and integrations with BI and analytics platforms.

What is included in Data Streams?

Data Streams have no separate platform fee. They include everything needed to build and run them:

• Unlimited Data Streams, pipelines, users, and integrations
• Data management capabilities: Unify, schema transformation, batching, logging, deduplication, and more
• Data collection automation: the Job system, scheduling, failover, and volume management
• Pipeline tools: deployment, auto-scaling, version control, alerting, metrics, and diagnostics

Data collection is billed as DVU usage: 1 DVU per Job Run plus 1 DVU per 100 documents after the first 100 in each run. Enrichments are billed separately at their own per-component rates. See Data Volume Units.

For full pricing details, see How Data Streams are Priced or visit datastreamer.io/pricing.

Can I bring my own data sources or models?

Yes. Data Streams are designed to work with your existing infrastructure. You can include:

• Your own data sources
• Your own API credentials for providers you already use
• Your own AI or ML models
• Custom schemas and transformation logic
• Your own storage and destination systems

See Direct Integrations for how to connect specific providers and bring your own credentials.