updated Edge Computing / Edge AI article

obx-vivien · obx-vivien · commit c700e2e54123 · 2025-10-10T12:44:54.000+02:00
diff --git a/docs/edge-computing-edge-ai-local-ai-marketanalysis.mdx b/docs/edge-computing-edge-ai-local-ai-marketanalysis.mdx
@@ -1,7 +1,7 @@
 ---
 id: edge-computing-edge-ai-local-ai-marketanalysis
 title: "Is Edge Computing dead?"
-description: "A comprehensive analytical review of the Edge Computing and Edge AI (On-device AI) market in 2025, examining market trajectories, technological developments, and the critical role of on-device vector databases."
+description: "A comprehensive analytical review of the Edge Computing and Edge AI (On-device AI) market in 2025, examining market trajectories, technological developments, and the critical role of on-device [vector database](https://objectbox.io/vector-database/)s."
 slug: edge-computing-state-2025
 tags: [edge-computing, edge-ai, vector-databases, agentic-ai, on-device-ai, market-analysis]
 hide_table_of_contents: false
@@ -12,7 +12,6 @@ image: /img/social/is-edge-computing-dead.jpg
 <summary><strong>Table of Contents</strong></summary>
 
 - [Is Edge Computing dead?](#is-edge-computing-dead)
-      - [State of the Edge 2025: An Analytical Review of Edge Computing market with primary focus on Edge AI (On-Device AI) and the critical role of vector databases](#state-of-the-edge-2025-an-analytical-review-of-edge-computing-market-with-primary-focus-on-edge-ai-on-device-ai-and-the-critical-role-of-vector-databases)
   - [AI is boosting the edge](#ai-is-boosting-the-edge)
   - [The Strategic Imperative of Edge AI: A 2025 Validation](#the-strategic-imperative-of-edge-ai-a-2025-validation)
     - [Real-time Performance and Reliability: From Low Latency to Autonomous Action](#real-time-performance-and-reliability-from-low-latency-to-autonomous-action)
@@ -153,9 +152,7 @@ As the energy and hardware demands of the cloud continue to grow, the economic c
 The market for edge computing and Edge AI is characterized by a strong consensus among leading analyst firms on a trajectory of rapid, sustained growth.
 ###  Gartner's 2025 Prediction: A Nuanced Reality
 
-While Gartner famously forecast that [75% of enterprise data would be processed at the edge by 2025](https://www.gartner.com/smarterwithgartner/what-edge-computing-means-for-infrastructure-and-operations-leaders), towards the end of 2025 the reality is closer to 35% ([estimated based on Forrester 2025](https://www.forrester.com/report/more-than-half-of-enterprise-data-is-in-the-cloud/RES185482)). Despite the slower initial adoption, as so often happens with these predictions, the edge has now become a strategic priority, with its growth being rapidly accelerated by AI's requirements for real-time processing, low-latency, data privacy, and scalability.
-
-This is the domain of Edge AI - deploying AI models locally, directly on devices. It reduces latency, ensures offline availability, and enhances user privacy by keeping data on-device. And it’s growing fast: [Gartner predicts that by 2029, at least 60% of enterprise-deployed generative AI models will be running on edge devices](https://www.gartner.com/en/documents/5270463) rather than in centralized cloud services. We'll see if this holds true.
+While Gartner famously forecast that [75% of enterprise data would be processed at the edge by 2025](https://www.gartner.com/smarterwithgartner/what-edge-computing-means-for-infrastructure-and-operations-leaders), towards the end of 2025 the reality is closer to 35% ([estimated based on Forrester 2025](https://www.forrester.com/report/more-than-half-of-enterprise-data-is-in-the-cloud/RES185482)). Despite the slower initial adoption, as so often happens with these predictions, the edge has now become a strategic priority[Gartner predicts that by 2029, at least 60% of enterprise-deployed generative AI models will be running on edge devices](https://www.gartner.com/en/documents/5270463) rather than in centralized cloud services. We'll see if this holds true.
 
 A Gartner survey published in April 2025 reports that 27% of manufacturing enterprises have already deployed edge computing. Moreover, 64% of enterprises in the sector expect to have deployments in place by the end of 2027 [Gartner via AT&T]. This shift underscores the sector’s strong momentum toward edge adoption, with analysis capabilities steadily moving closer to where data is generated.  
 
@@ -168,7 +165,7 @@ Instead, they work with high-dimensional numerical representations of that data,
 Academic research from 2025 confirms that vector databases are pivotal for providing the "semantic context" that allows AI systems to understand and reason about the meaning of data, rather than just its literal content.
 [[arXiv]](https://arxiv.org/pdf/2503.04847)
 
-This architectural pattern is the foundation of Retrieval-Augmented Generation (RAG), which has become the most popular and effective method for grounding LLMs with specific, private, or real-time information.
+This architectural pattern is the foundation of Retrieval-Augmented Generation ([RAG](https://objectbox.io/retrieval-augmented-generation-rag-with-vector-databases-expanding-ai-capabilities/)), which has become the most popular and effective method for grounding LLMs with specific, private, or real-time information.
 [[Accenture]](https://www.accenture.com/us-en/insights/technology/technology-trends-2024) The RAG process relies on a vector database to perform an ultra-fast similarity search to find the most relevant snippets of context from a knowledge base before that context is passed to the LLM to generate a response.
 [[ObjectBox]](https://objectbox.io/the-first-on-device-vector-database-objectbox-4-0/), [[ObjectBox]](https://objectbox.io/the-on-device-vector-database-for-android-and-java/) This is precisely the mechanism that enables an LLM to "chat with your documents" or access up-to-the-minute information, all while running locally and privately on a device.
 :::tip Agentic AI Memory Architecture
@@ -196,6 +193,7 @@ This allows them to effectively handle synonyms, ambiguous language, and fuzzy q
 **Multimodal Search:** A key advantage of vector embeddings is their ability to represent different data types—text, images, audio, sensor readings—in a shared mathematical space.
 This allows a vector database to perform unified multimodal search, for example, finding images that match a textual description or retrieving documents related to a specific sound clip.
 [[ObjectBox]](https://objectbox.io/on-device-vector-databases-and-edge-ai/) This capability is becoming increasingly important, as Gartner predicts that by 2026, multimodal AI models will be utilized in over 60% of all enterprise GenAI solutions.
+
 **Retrieval-Augmented Generation (RAG):** 
 By providing relevant, factual context from a vector database, RAG helps to decrease model "hallucinations," enables the use of real-time or proprietary data, and allows for highly personalized responses.
 [[ObjectBox]](https://objectbox.io/on-device-vector-databases-and-edge-ai/), [[Accenture]](https://www.accenture.com/us-en/insights/technology/technology-trends-2024), [[ObjectBox]](https://objectbox.io/the-first-on-device-vector-database-objectbox-4-0/)
@@ -207,7 +205,7 @@ This saves processing cycles, reduces latency, and lowers power consumption—al
 ### Addressing the On-Device Infrastructure Gap
 
 
-[[ObjectBox]](https://objectbox.io/on-device-vector-databases-and-edge-ai/) This gap is now the central point of innovation and competition in the database landscape.
+This gap is now the central point of innovation and competition in the database landscape.
 The first wave of the vector database market has been dominated by cloud-native or server-first solutions such as Pinecone, Weaviate, Milvus, and Qdrant.
 [[Greenrobot]](https://greenrobot.org/database/top-vector-databases/), [[DataCamp]](https://www.datacamp.com/blog/the-top-5-vector-databases) These systems are architected for massive scalability, high throughput, and distributed deployments within data centers.
 However, the technological requirements for the edge are fundamentally different.
@@ -253,12 +251,14 @@ Relying on the cloud for the constant sense-plan-act loop of an autonomous agent
 ### The Future-Ready On-Device Stack
 
 
-[[ObjectBox]](https://objectbox.io/on-device-vector-databases-and-edge-ai/) The rise of Agentic AI makes the requirements for this stack far more demanding and specific.
+The rise of Agentic AI makes the requirements for this stack far more demanding and specific.
 It is no longer sufficient to simply run an inference model on a device.
 A future-ready on-device stack must provide a complete, integrated framework to support the entire lifecycle of an autonomous agent's operation:
 
 **Perception:** The stack must efficiently process real-time data from a variety of on-device sensors, including cameras (computer vision), microphones (speech recognition), and other IoT sensors.
+
 **Reasoning & Planning:** It needs to host an efficient LLM or other specialized planning model that can take a high-level goal and break it down into a sequence of concrete, executable steps.
+
 **Memory:** It must include a high-performance on-device vector database that the agent can query to retrieve relevant knowledge, context, and past experiences to inform its planning process.
 This is the lynchpin of the entire system.
 
@@ -270,38 +270,6 @@ It is what allows an agent to learn from its experiences and improve its perform
 Achieving this vision will require the convergence of multiple advanced technologies identified in the 2025 research, including new, lightweight AI frameworks, smaller and more efficient on-device models, specialized AI chips and hardware accelerators (NPUs, ASICs), and advanced, low-latency connectivity like 5G where necessary.
 :::
 
-## Overcoming Implementation Hurdles: A 2025 Perspective
-
-
-[[ObjectBox]](https://objectbox.io/on-device-vector-databases-and-edge-ai/) Research from 2025 provides a much more detailed and structured understanding of these hurdles and the strategies being developed to address them.
-### The Optimization Triad: A Framework for Edge Deployment
-
-The core problem of Edge AI is the fundamental mismatch between the immense computational and memory requirements of state-of-the-art AI models and the severely limited resources of typical edge devices.
-[[Promwad]](https://promwad.com/news/edge-ai-model-deployment), [[arXiv]](https://arxiv.org/abs/2501.03265), [[arXiv]](https://arxiv.org/pdf/2501.03265)
-
-| **Industry Vertical** | **Primary Use Cases** | **Key Business Drivers** | **Market Position & Growth** |
-|---|---|---|---|
-| **Manufacturing** | Predictive Maintenance, Quality Control, Machine Automation, Industrial IoT | Real-time processing, operational efficiency, cost reduction |
-Largest market share in 2024, ~25% of 2025 spending |
-| **Healthcare** |
-Remote Patient Monitoring, Real-time Diagnostics, Robotic Surgery, Medical Imaging |
-Data privacy (HIPAA), low latency for critical care, improved patient outcomes |
-Highest projected growth rate (AI in HC CAGR: 44.0%) [[Grand View Research]](https://www.grandviewresearch.com/industry-analysis/edge-computing-market), [[Mordor Intelligence]](https://www.mordorintelligence.com/industry-reports/edge-computing-in-healthcare-market), [[Mordor Intelligence]](https://www.mordorintelligence.com/industry-reports/edge-computing-in-healthcare-market), [[Fortune Business Insights]](https://www.fortunebusinessinsights.com/industry-reports/artificial-intelligence-in-healthcare-market-100534) |    
-> **Note:** Current Mordor (2025–2030) projects USD 8.16B (2025) → USD 19.96B (2030) at 19.61% CAGR.
-> **Note:** Latest GVR (2025–2033) projects USD 33.44B (2025) → USD 327.79B (2033) at 33% CAGR.
-| **Automotive** |
-ADAS, Autonomous Driving, In-Cabin Experience (Voice/Gesture), Predictive Maintenance | Safety (low latency), enhanced user experience, data reduction |
-Edge AI Auto Market: $3.8B in 2025. AI in Auto Market CAGR: 53.
-| **Retail & Services** |
-Real-time Video Analytics, Personalized Customer Offers, Inventory Management | Improved customer experience, operational efficiency, loss prevention |
-Largest share of investment in 2025 (~28% of total) |
-:::info Strategic Market Insights
-This industry-specific overview provides an invaluable strategic tool for product managers and market strategists.
-It allows for effective market sizing and prioritization by clearly identifying which industries are currently leading in adoption and spending (Manufacturing, Retail) and which possess the highest future growth potential (Healthcare).
-Furthermore, by explicitly linking the primary use cases in each vertical to their core business drivers (e.g., connecting Remote Patient Monitoring to the driver of Data Privacy), it enables the tailoring of product features and marketing messages to resonate with the specific needs and pain points of each industry.
-This understanding is crucial for developing a targeted go-to-market strategy and for analyzing the competitive landscape within each distinct vertical ecosystem.
-:::
-
 ## Strategic Outlook and Concluding Analysis
 
 The technological and market forces of 2025 have solidified the strategic importance of the edge.
@@ -322,17 +290,6 @@ For enterprises and product developers, the time for isolated experimentation wi
 The competitive landscape now demands the development of a strategic, scalable platform for deploying intelligent applications at the edge.
 This is no longer a niche consideration but a competitive necessity to unlock the next wave of operational efficiency, intelligent automation, and transformative user experiences.
 
-Explain the technical differences between cloud-native and on-device vector databases.
-
-What are the security implications of on-device AI?
-
-Provide an example of an agentic AI workflow.
-
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