Backblaze Feature Trails

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AI Agent Storage Integration

Backblaze B2's AI Agent Storage Integration capability, enhanced by B2 Overdrive, addresses the specific storage demands of Generative AI and RAG pipelines. It provides an always-hot architecture with no tiering or retrieval delays, ensuring sustained high throughput and free data movement. This is crucial for the continuous, mixed traffic patterns of RAG pipelines, which involve diverse storage needs across ingestion, active training, checkpointing, output storage, and inference. The integration with B2 Neo as a white-label product allows neoclouds to offer a tiered storage architecture, combining flash for active training with cost-effective, high-capacity storage for other lifecycle stages, preventing data gravity issues with hyperscalers and enabling neoclouds to retain customer relationships.

3 posts · 2026

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Object Lock for Immutable Backups

Backblaze B2 Cloud Storage offers Object Lock to provide immutability for backups, preventing accidental or malicious deletion of data. This feature is crucial for ransomware protection and ensuring data integrity. The capability has evolved to address common IT team misunderstandings regarding its configuration, limitations, and interaction with backup software. Key aspects include the distinction between immutability and encryption, the bucket creation and retention period settings, and the integration with enterprise backup solutions like Veeam. The focus is on educating users to correctly implement and leverage object lock for robust immutable backup strategies.

1 post · 2026

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Jamf Integration for Backup Deployment

Backblaze Computer Backup continues to enhance its zero-touch deployment capabilities for Mac fleets, focusing on seamless integration with MDM solutions like Jamf Pro, Kandji, and Addigy. The emphasis is on automating the entire installation and configuration process, ensuring that backup agents are deployed silently and correctly without any end-user interaction or post-install IT intervention. This includes handling system permissions and user registration through MDM profiles, which is critical for managing device ownership and ensuring consistent backup coverage. The latest update introduces two flexible methods for matching devices to users: fixed email assignment for controlled environments and dynamic user detection based on login for real-world scenarios, allowing for mixed deployment strategies within the same fleet.

3 posts · 2026

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S3 Native File Streaming for B2

Backblaze B2 integrates with Suite Studios' S3 Native File Streaming, allowing B2 buckets to be mounted as high-performance local drives. This enables applications to stream only the necessary parts of files, rather than requiring full downloads, making cloud storage behave like a shared NAS. This is particularly beneficial for media production, life sciences, geospatial data, engineering, and research, where large datasets need active and accessible file-based access. Backblaze B2 provides the storage foundation for TAMS workflows, enabling media segments to be stored and accessed by time, facilitating real-time editing and collaboration through partners like LiveWyer (TAMOSS), CuttingRoom (browser-based editor), and Drastic Technologies (ingest/playback).

1 post · 2026

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Default Server-Side Encryption for B2

Backblaze B2 Cloud Storage now enforces Server-Side Encryption with Backblaze-managed keys (SSE-B2) using AES-256 as the default for all new uploads and destination copies. This change is applied automatically without requiring application modifications, additional costs, or performance impacts. Existing objects retain their original encryption state. Server-Side Encryption with Customer-Managed Keys (SSE-C) remains available for users who need to provide their own encryption keys.

2 posts · 2026

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AI Data Pipeline Ingest and Archive

Backblaze B2's AI Data Pipeline Ingest and Archive capability has evolved to emphasize the critical role of data collection and ingest in AI project success. The post details the five key phases of the AI data pipeline for model training, highlighting how storage infrastructure is integral to each stage. It stresses the importance of streaming ingestion for AI, requiring constant low-latency access, high rate limits, and high throughput. The capability now includes detailed guidance on implementing data governance and making informed storage infrastructure decisions to avoid costly egress fees and retrieval latencies incompatible with active AI pipelines, particularly for multimodal datasets. The focus is on aligning AI strategy with data strategy from the outset to ensure scalable AI programs.

3 posts · 2026

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Generative AI Media Pipelines

Backblaze B2 integrates with Genblaze SDK and GMI Cloud to simplify generative media pipelines. The platform supports swappable models, durable object storage, and provenance manifests. Recent developments focus on advanced features like object locking, scoped application keys, lifecycle rules, event notifications, presigned URLs, and robust error handling for AI-generated media. The Genblaze SDK provides a unified API for orchestrating multi-provider generative media workflows, enabling developers to build pipelines that string together various AI models for image, video, and audio generation. The SDK emphasizes provenance tracking, allowing for verification and replay of generated assets, and integrates seamlessly with Backblaze B2 for durable storage and features like event notifications and object lock.

3 posts · 2026

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Network Traffic Analysis and Capacity Planning

Backblaze continues to analyze network traffic patterns, with a focus on the impact of AI workloads. This quarter's report introduces variance analysis to distinguish stable traffic from volatile AI-driven patterns. The analysis covers total traffic volume, magnitude (bits per IP), and communication uniqueness across different network types (CDN, Hosting, Hyperscaler, ISP-regional, Neocloud) and regions (US-West, US-East, CA-East, EU-Central). The data highlights the dynamic nature of neocloud and AI-driven traffic, emphasizing the need for flexible infrastructure that can adapt to evolving AI ecosystems and the importance of observing network telemetry over time to distinguish durable trends from short-term experimentation.

4 posts · 2026

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