NEW YORK, June 10, 2026 /PRNewswire/ -- Botslab, a smart security technology brand focused on AI-powered home and vehicle protection, today announced the launch of the Botslab W101 Window Camera, an innovative dual-camera security solution designed to simplify home monitoring through easy window-mounted installation.
Unlike traditional outdoor security systems that often require drilling, wiring, or complex setup, the W101 Window Camera is designed to be attached directly to glass surfaces in minutes. The device allows users to transform almost any window into a smart monitoring point, making it ideal for apartments, rental homes, garages, front doors, and backyards — without drilling, permanent installation, or landlord permission. Its portable design also makes it easy to move and reinstall when relocating.
The W101 Window Camera combines dual 2.5K cameras into a single device, featuring an outward-facing "OutView" camera for outdoor monitoring and an inward-facing "InView" camera for indoor visibility. Through the app, users can easily switch between indoor and outdoor camera views, allowing flexible monitoring from a single window-mounted device without requiring multiple cameras.
Designed to address common challenges associated with window monitoring, the W101 Window Camera features an anti-reflection optical design that helps reduce glare and improve image clarity when recording through glass. The indoor camera also includes AI full-color night vision powered by an F1.0 large-aperture lens to deliver clearer nighttime footage in low-light environments.
The camera further integrates AI-powered smart notification and smart search capabilities. Users can receive instant alerts for detected human activity and quickly search for important events in the app without manually reviewing hours of recordings.
Additional features include:
- 24/7 continuous recording
- Local and cloud storage options
- Dual-band 2.4GHz and 5GHz Wi-Fi
- Two-way audio and visual voice interaction
- Family sharing access
Privacy protection is also built into the product design. Unlike many indoor security cameras that continuously record inside the home, the W101 window camera's inward-facing camera does not automatically record by default. Users can manually access the live view, capture photos, or start recording through the app whenever needed, providing greater control over indoor privacy. The indoor camera also includes a physical privacy shutter for added peace of mind.
With growing demand for flexible and renter-friendly home protection, Botslab believes the W101 Window Camera introduces a new category of window camera for home security by combining dual-camera coverage, AI features, and tool-free installation into a single device.
For more information, visit the Botslab Official Website
** This press release is distributed by PR Newswire through automated distribution system, for which the client assumes full responsibility. **
Botslab Launches W101: First Dual Indoor & Outdoor Window Camera
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Available now in public preview on Zilliz Cloud, Vector Lakebase keeps production vector search at its core and adds shared lake-native storage and on-demand compute — bringing real-time serving, interactive discovery, and batch analytics onto one data foundation.
REDWOOD CITY, Calif., June 10, 2026 /PRNewswire/ -- Zilliz, the company behind Milvus, the world's most widely adopted open-source vector database, today announced the public preview of Zilliz Vector Lakebase, a major Zilliz Cloud release that pairs the production vector database with a shared, lake-native data foundation.
Vector Lakebase keeps Zilliz Cloud's real-time vector search at the core — the engine Zillow, OpenEvidence, Exa, Filevine, MiniMax, and more than 10,000 enterprises and AI teams already rely on — and extends it with three new ways to operate on the same data: interactive discovery, large-scale batch analytics, and search directly on external data lakes. The result is a single data foundation in which every workload runs against a single logical copy of the data, with on-demand and batch jobs billed only when compute is active.
"Production vector search is and will remain at the heart of what Zilliz does — it's why thousands of teams choose Milvus and Zilliz Cloud, and it's getting faster and more cost-efficient every release," said Charles Xie, Founder and CEO of Zilliz. "Vector Lakebase is what we believe comes next: one data foundation where the same vectors can serve a production query, anchor a discovery session, and power a multi-petabyte training-data pipeline — without copies, migration, or a parallel stack."
Why a Single Data Foundation Matters
AI systems are no longer a single-query retrieval problem. They run as a continuous loop — serve, learn from feedback, mine and prepare better data, then serve again — and each turn typically requires separate systems for serving, exploration, and large-scale processing. Moving billions of vectors between those systems can take days. The cost and complexity are so high that many teams skip the loop altogether, leaving valuable data retrievable but never improved.
Vector Lakebase closes that gap with a zero-copy semantic data plane on shared lake-native storage: real-time serving, interactive discovery, and batch analytics all run against one logical copy of the data, scaling from gigabytes to petabytes.
"Teams asked for a way to keep their data in one place and run very different workloads against it — from real-time agent memory to overnight semantic deduplication," said Robert Guo, VP of Product at Zilliz and one of the architects behind Milvus. "Vector Lakebase delivers that through a unified storage layer on Vortex, tiered serving for the production path, and on-demand compute for everything else."
Five Capabilities on One Foundation
- Tiered Real-Time Serving. Three production tiers tuned for different workloads: Performance-Optimized (1,000+ QPS, single-digit-millisecond latency, in-memory); Capacity-Optimized (100–500 QPS, sub-100ms latency, memory + NVMe); and Tiered-Storage (10–50 QPS, ~100ms latency, spanning memory, NVMe, and object storage at significantly lower cost). All tiers default to 95–98% recall, tunable to 99%+, backed by Zilliz Cloud's 99.99% uptime SLA and Global Cluster cross-region high availability.
- On-Demand Search. Pay-as-you-go compute for workloads where infrastructure sits idle most of the time, billed directly for object storage and compute rather than serverless markups. In Zilliz's internal benchmark on one billion 768-dimension vectors with 10 hours of monthly active compute, On-Demand Search totaled $318 versus $4,937 for a comparable serverless path — roughly 1/15 the cost.
- External Data Lake Search. A zero-copy External Collection mode that adds state-of-the-art indexing and full-spectrum search directly to existing Lance, Iceberg, Parquet, and Vortex tables, with incremental sync on refresh. Source data stays where it lives.
- Full-Spectrum AI Search. Search across vectors (dense and sparse), text, JSON, and geospatial data, with hybrid retrieval, BM25, regex, multi-vector and iterative search, and multi-path retrieval. Results can be reranked with Cohere, Voyage AI, RRF, and weighted/boost/decay strategies.
- Unified Lake-Native Storage. Shared storage for serving and analytics built on Vortex, an open columnar format designed for faster, cheaper random reads than Lance and Parquet, paired with object-storage-aware indexes (vector, BM25, JSON) that cut read amplification by over 90%. A 100-million-row schema backfill typically completes in single-digit minutes — without disrupting active query traffic.
Together, these capabilities let AI teams consolidate what previously required parallel always-on serving clusters and separate batch systems onto one platform — with consistent indexes, versioned data, and compute that scales to zero between jobs.
Availability
Zilliz Vector Lakebase is available now in public preview on Zilliz Cloud, alongside Serverless, Dedicated, and BYOC deployment options across more than 30 regions on AWS, Google Cloud, and Microsoft Azure. New work email signups receive $100 in free credits at zilliz.com. Teams running serving, discovery, and analytics on separate stacks can contact the Zilliz team for a tailored walkthrough.
About Zilliz
Zilliz is a leading AI data infrastructure company and the creator of Milvus, the world's most widely adopted open-source vector database, with 44,000+ GitHub stars and over 100 million Docker pulls. Zilliz helps enterprises and AI startups make their unstructured data searchable, analyzable, and governable — turning text, images, audio, video, and more into a strategic asset for production AI.
Zilliz's technology centers on Milvus and Zilliz Cloud. Milvus is an open-source vector database purpose-built for 100-billion-scale vector search. Zilliz Cloud extends that foundation into a fully managed Vector Lakebase platform, combining the high-throughput, low-latency serving capabilities of vector databases with the openness, scalability, and economics of multimodal data lakes. Zilliz powers more than 10,000 enterprises and AI-native startups worldwide, including MiniMax, OpenEvidence, Filevine, Exa, Salesforce, and Read AI.
Headquartered in Redwood Shores, California, Zilliz is backed by leading investors, including Aramco's Prosperity 7 Ventures, Temasek's Pavilion Capital, Hillhouse Capital, 5Y Capital, Yunqi Partners, and Trustbridge Partners. Learn more at Zilliz.com.
Available now in public preview on Zilliz Cloud, Vector Lakebase keeps production vector search at its core and adds shared lake-native storage and on-demand compute — bringing real-time serving, interactive discovery, and batch analytics onto one data foundation.
REDWOOD CITY, Calif., June 10, 2026 /PRNewswire/ -- Zilliz, the company behind Milvus, the world's most widely adopted open-source vector database, today announced the public preview of Zilliz Vector Lakebase, a major Zilliz Cloud release that pairs the production vector database with a shared, lake-native data foundation.
Vector Lakebase keeps Zilliz Cloud's real-time vector search at the core — the engine Zillow, OpenEvidence, Exa, Filevine, MiniMax, and more than 10,000 enterprises and AI teams already rely on — and extends it with three new ways to operate on the same data: interactive discovery, large-scale batch analytics, and search directly on external data lakes. The result is a single data foundation in which every workload runs against a single logical copy of the data, with on-demand and batch jobs billed only when compute is active.
"Production vector search is and will remain at the heart of what Zilliz does — it's why thousands of teams choose Milvus and Zilliz Cloud, and it's getting faster and more cost-efficient every release," said Charles Xie, Founder and CEO of Zilliz. "Vector Lakebase is what we believe comes next: one data foundation where the same vectors can serve a production query, anchor a discovery session, and power a multi-petabyte training-data pipeline — without copies, migration, or a parallel stack."
Why a Single Data Foundation Matters
AI systems are no longer a single-query retrieval problem. They run as a continuous loop — serve, learn from feedback, mine and prepare better data, then serve again — and each turn typically requires separate systems for serving, exploration, and large-scale processing. Moving billions of vectors between those systems can take days. The cost and complexity are so high that many teams skip the loop altogether, leaving valuable data retrievable but never improved.
Vector Lakebase closes that gap with a zero-copy semantic data plane on shared lake-native storage: real-time serving, interactive discovery, and batch analytics all run against one logical copy of the data, scaling from gigabytes to petabytes.
"Teams asked for a way to keep their data in one place and run very different workloads against it — from real-time agent memory to overnight semantic deduplication," said Robert Guo, VP of Product at Zilliz and one of the architects behind Milvus. "Vector Lakebase delivers that through a unified storage layer on Vortex, tiered serving for the production path, and on-demand compute for everything else."
Five Capabilities on One Foundation
- Tiered Real-Time Serving. Three production tiers tuned for different workloads: Performance-Optimized (1,000+ QPS, single-digit-millisecond latency, in-memory); Capacity-Optimized (100–500 QPS, sub-100ms latency, memory + NVMe); and Tiered-Storage (10–50 QPS, ~100ms latency, spanning memory, NVMe, and object storage at significantly lower cost). All tiers default to 95–98% recall, tunable to 99%+, backed by Zilliz Cloud's 99.99% uptime SLA and Global Cluster cross-region high availability.
- On-Demand Search. Pay-as-you-go compute for workloads where infrastructure sits idle most of the time, billed directly for object storage and compute rather than serverless markups. In Zilliz's internal benchmark on one billion 768-dimension vectors with 10 hours of monthly active compute, On-Demand Search totaled $318 versus $4,937 for a comparable serverless path — roughly 1/15 the cost.
- External Data Lake Search. A zero-copy External Collection mode that adds state-of-the-art indexing and full-spectrum search directly to existing Lance, Iceberg, Parquet, and Vortex tables, with incremental sync on refresh. Source data stays where it lives.
- Full-Spectrum AI Search. Search across vectors (dense and sparse), text, JSON, and geospatial data, with hybrid retrieval, BM25, regex, multi-vector and iterative search, and multi-path retrieval. Results can be reranked with Cohere, Voyage AI, RRF, and weighted/boost/decay strategies.
- Unified Lake-Native Storage. Shared storage for serving and analytics built on Vortex, an open columnar format designed for faster, cheaper random reads than Lance and Parquet, paired with object-storage-aware indexes (vector, BM25, JSON) that cut read amplification by over 90%. A 100-million-row schema backfill typically completes in single-digit minutes — without disrupting active query traffic.
Together, these capabilities let AI teams consolidate what previously required parallel always-on serving clusters and separate batch systems onto one platform — with consistent indexes, versioned data, and compute that scales to zero between jobs.
Availability
Zilliz Vector Lakebase is available now in public preview on Zilliz Cloud, alongside Serverless, Dedicated, and BYOC deployment options across more than 30 regions on AWS, Google Cloud, and Microsoft Azure. New work email signups receive $100 in free credits at zilliz.com. Teams running serving, discovery, and analytics on separate stacks can contact the Zilliz team for a tailored walkthrough.
About Zilliz
Zilliz is a leading AI data infrastructure company and the creator of Milvus, the world's most widely adopted open-source vector database, with 44,000+ GitHub stars and over 100 million Docker pulls. Zilliz helps enterprises and AI startups make their unstructured data searchable, analyzable, and governable — turning text, images, audio, video, and more into a strategic asset for production AI.
Zilliz's technology centers on Milvus and Zilliz Cloud. Milvus is an open-source vector database purpose-built for 100-billion-scale vector search. Zilliz Cloud extends that foundation into a fully managed Vector Lakebase platform, combining the high-throughput, low-latency serving capabilities of vector databases with the openness, scalability, and economics of multimodal data lakes. Zilliz powers more than 10,000 enterprises and AI-native startups worldwide, including MiniMax, OpenEvidence, Filevine, Exa, Salesforce, and Read AI.
Headquartered in Redwood Shores, California, Zilliz is backed by leading investors, including Aramco's Prosperity 7 Ventures, Temasek's Pavilion Capital, Hillhouse Capital, 5Y Capital, Yunqi Partners, and Trustbridge Partners. Learn more at Zilliz.com.
** This press release is distributed by PR Newswire through automated distribution system, for which the client assumes full responsibility. **
Zilliz Launches Vector Lakebase, Extending the World's Most Adopted Vector Database into a Unified Data Platform for AI
