Table of Contents
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
AI is boosting the edge
Edge AI represents a critical paradigm for modern applications, and on-device vector databases, such as ObjectBox, are an indispensable component of the enabling technology stack. The core drivers for Edge AI - enhanced privacy, superior reliability, real-time speed, and improved sustainability - have not only remained relevant but have been significantly amplified by recent technological advancements and evolving market dynamics.
The 2025 landscape is marked by several critical inflection points that update the perspective on Edge Computing from early 2024. First, market growth forecasts for both Edge Computing and - most notably - Edge AI have been substantially revised upwards. A synthesis of data from leading analyst firms indicates a consensus on hyper-growth, with the combined market projected to exceed $380 billion by 2028. This accelerated trajectory outpaces earlier, more conservative estimates.
At the same time, the emergence of "Agentic AI" as a dominant technological trend represents another significant market shift. Defined as autonomous systems capable of planning and executing complex, multi-step tasks, Agentic AI is highlighted by both Gartner and McKinsey as a top trend for 2025. [Gartner Source], [McKinsey Source 1], [McKinsey Source 2] This leap from generative to autonomous capabilities fundamentally increases the demand for sophisticated on-device processing and the "long-term memory" function that vector databases uniquely provide. The need for real-time, context-aware decision-making in the physical world makes the edge the only viable deployment environment for these advanced systems.
On top, the broader Generative AI market is entering a new phase of maturity. Gartner's placement of GenAI in the "Trough of Disillusionment" signals a crucial transition away from speculative hype toward strategic, long-term enterprise investment in foundational infrastructure. [Procurement Magazine], [TechRepublic]
Finally, the technical complexity of deploying and managing AI on resource-constrained devices has become more pronounced. This challenge is driving a significant market shift, with IDC forecasting that enterprise spending on edge-related services will surpass hardware spending by 2028, as organizations grapple with the complexities of integration, management, and security in distributed environments.
The convergence of these trends - market acceleration, the rise of autonomous agents, GenAI maturation, and the growing complexity challenge - creates a powerful business case for a robust on-device AI stack. For organizations to capitalize on the next wave of AI, particularly the transformative potential of agentic systems, investment in on-device vector data management is no longer an optional consideration but a strategic imperative for future competitiveness.
The Strategic Imperative of Edge AI: A 2025 Validation
Real-time Performance and Reliability: From Low Latency to Autonomous Action
On-device processing is significantly faster and enables real-time decision making, making it a primary driver for edge adoption. [ObjectBox] In numerous critical applications, the latency introduced by a round-trip to the cloud is not merely an inconvenience but a functional impossibility. The demand for millisecond-level response times is a core requirement in industrial automation, autonomous navigation, and real-time medical diagnostics. [Promwad] A 2025 McKinsey analysis of the automotive sector provides a concrete example, quantifying that edge-based voice assistants can achieve response latencies of 300-700 milliseconds, a stark improvement over the 1000-2200 milliseconds typical of pure cloud solutions. [McKinsey] This performance gap is a critical factor in user experience and, in the case of vehicle control systems, safety. This need for immediacy is fueling the rapid growth of Edge AI adoption across key verticals. In manufacturing, edge systems enable predictive maintenance and real-time quality control on the factory floor. [Grand View Research] In healthcare, they power continuous patient monitoring and immediate analysis of diagnostic data.
However, the strategic importance of edge performance is evolving beyond simply reducing latency for discrete tasks. The emergence of Agentic AI, which both Gartner and McKinsey identify as a transformative trend for 2025, shifts the focus from speed to autonomy. [McKinsey], [Forbes] Agentic systems are not designed to answer single queries but to execute complex, multi-step workflows. For example, an agent in a smart factory might be tasked with a goal like, "Optimize production line 7 for the next shift." This requires a sequence of actions: detecting a potential defect in a component, autonomously rerouting the workflow to a backup line, updating inventory management systems, and scheduling a maintenance request for the faulty component. [Forbes]
Attempting to orchestrate such a complex process from the cloud would involve numerous, sequential data round-trips, each introducing latency and a potential point of failure. The cumulative delay and network dependency would render the system unreliable and non-performant. Therefore, the strategic imperative is no longer just "low latency" for a single action but "sustained operational autonomy" for a complex, goal-oriented process. The edge becomes the only viable environment for these agentic systems to perceive their surroundings, reason through a plan, and act upon it reliably and in real time. This evolution elevates the role of edge computing from a performance optimization to a mission-critical enabler for the next generation of intelligent, autonomous AI.
Data Sovereignty and Privacy: A Growing Mandate
Edge AI enhances data ownership and privacy by processing and storing data on the user's device, and this has become more critical in 2025 than ever before. [ObjectBox] This shift is propelled by a dual mandate of declining public trust and increasingly stringent data protection regulations. A 2024 McKinsey survey revealed that public confidence in AI providers has fallen to just 53%, making on-device processing a powerful feature for building user trust. [McKinsey via DHInsights] When data, particularly sensitive personal information, remains on a user's device, it fundamentally alters the power dynamic, giving them greater control and reducing exposure to breaches on centralized servers.
This consumer sentiment is now being codified into law. New regulations, such as the April 2025 US rules that prohibit outbound transfers of biometric and health data to certain nations, create a legal requirement for on-premise or on-device processing in the healthcare sector. [Mordor Intelligence] This regulatory pressure reinforces the need for advanced security paradigms tailored for distributed environments.
This convergence of consumer demand and regulatory enforcement is transforming privacy from a compliance obligation into a significant competitive differentiator. The base argument is that edge processing is inherently more private. This is now being reinforced by regulations that mandate this architecture in high-stakes domains like healthcare. The crucial next step in this logical chain is the connection to market dynamics. McKinsey's finding that "companies prioritizing digital trust outperform peers financially" elevates privacy from a feature to a strategic growth lever. [McKinsey via DHInsights]
This implies that a market bifurcation is underway. While many applications will continue to leverage the cloud, a substantial and growing segment of high-value applications—especially in healthcare, finance, consumer electronics, and personal wellness—will compete directly on the basis of demonstrable privacy and data sovereignty. Companies that architect their products around an "on-device first" privacy model can command greater market trust, which can translate directly into higher adoption rates, increased customer loyalty, and premium pricing power. The underlying infrastructure that enables this—including on-device databases, secure enclaves in processors, and privacy-preserving machine learning techniques—ceases to be a simple technical implementation and becomes a core source of sustainable competitive advantage. This creates a powerful and enduring tailwind for the entire on-device technology stack.
Economic and Sustainability Drivers: The Hidden Costs of Cloud AI
Edge computing can significantly reduce bandwidth costs and data traffic—by as much as 60–90 percent—while lowering an application's CO2 footprint, a benefit strongly supported by current economic and infrastructural pressures. [ObjectBox] The relentless growth of data is straining network capacity, with IDC data from 2024 showing that 30% of enterprises are experiencing bandwidth demand increases of over 50% per year. [IDC] This creates immense and escalating cost pressure on cloud-centric architectures that rely on constant data transmission. Simultaneously, the surging demand for compute-intensive AI workloads is placing "new demands on global infrastructure," leading to systemic challenges like data center power constraints and rising energy costs. [McKinsey], [McKinsey]
Edge architectures offer a direct solution to these challenges. By processing data locally, they minimize the need for costly and energy-intensive data transfers. Accenture notes that training and fine-tuning smaller, specialized language models (SLMs) on edge devices is inherently more efficient and results in "smaller carbon footprints" compared to their massive, cloud-based counterparts. [Accenture] This aligns with a broader industry push toward sustainability; Gartner predicts that by 2028, growing sustainability initiatives will drive 30% of all Generative AI implementations to be optimized for energy efficiency—a goal that is naturally supported by edge architectures that reduce data movement. [RTInsights]
The economic argument for the edge is also evolving. The initial value proposition was tactical: reducing data egress fees from cloud providers. However, the systemic costs associated with the hyperscale AI boom are fundamentally shifting the Total Cost of Ownership (TCO) equation. Gartner forecasts that by 2028, hyperscalers will operate an astonishing $1 trillion worth of AI-optimized servers. [Procurement Magazine] This massive capital expenditure on specialized hardware will inevitably be passed on to customers in the form of higher prices for AI services.
Furthermore, energy is becoming a primary operational and financial concern. Gartner also predicts that by 2028, Fortune 500 companies will be forced to shift $500 billion from energy operational expenses to the development of microgrids specifically to address the immense power demands of their AI initiatives. [CFOtech Asia] This indicates that the cost of powering AI is becoming a board-level concern. When viewed together, these trends show that the TCO of a purely cloud-centric AI strategy is escalating rapidly, driven by both direct service costs and indirect, systemic costs related to energy and infrastructure. In this context, edge computing transforms from a tactical cost-saving measure into a strategic hedge against the systemic and rapidly increasing costs of centralized, hyperscale AI. As the energy and hardware demands of the cloud continue to grow, the economic case for processing data locally becomes exponentially stronger.
Market Trajectory and Adoption Forecasts (2025-2030)
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, towards the end of 2025 the reality is closer to 35% (estimated based on Forrester 2025). Despite the slower initial adoption, as so often happens with these predictions, the edge has now become a strategic priorityGartner predicts that by 2029, at least 60% of enterprise-deployed generative AI models will be running on edge devices 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.
The Architectural Necessity for On-Device AI
The claims that vector databases are "the databases for AI applications" and are essential for managing the vector embeddings that AI models use are fundamentally correct. [ObjectBox] Modern AI models, particularly Large Language Models (LLMs) and computer vision models, do not operate on raw text or images. Instead, they work with high-dimensional numerical representations of that data, known as vector embeddings. [Greenrobot], [NVIDIA] A vector database is a specialized system designed to efficiently store, index, and query these embeddings. 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]
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. [Accenture] 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], [ObjectBox] 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.
The role of the vector database, however, extends far beyond simple RAG applications. "Long-term memory for AI" has a profound meaning in the context of the shift toward Agentic AI. [ObjectBox] For a simple chatbot, "memory" might mean persisting conversation history. But for an autonomous AI agent, "memory" is the foundation of its ability to learn and make intelligent decisions. [McKinsey] An autonomous agent operating in the physical world needs to remember its past actions, the outcomes of those actions, the various states of its environment it has observed, and the procedures it has learned in order to make informed decisions about its future actions.
A vector database provides the ideal architecture for this form of sophisticated, experience-based memory. It can store complex, multimodal experiences (e.g., "the visual data from the camera, the error code from the sensor, and the corrective action I took") as a single vector embedding. When the agent encounters a new, similar situation, it can perform a similarity search against its vector database of past experiences to retrieve the most relevant "memories" to inform its current plan. For instance, an agent controlling a factory robot could query its local vector database with the current sensor readings to ask, "What was the most successful outcome the last time I encountered a similar pattern of vibrations on this machine?" This elevates the on-device vector database from a simple RAG component to the foundational cognitive architecture for autonomous systems at the edge. It becomes the agent's memory, enabling learning, adaptation, and complex, context-aware decision-making.
Enabling Advanced AI Capabilities On-Device
The specific use cases for vector databases highlighted in
Semantic Search: By calculating the distance between vectors, these databases find results based on semantic meaning rather than exact keyword matches. This allows them to effectively handle synonyms, ambiguous language, and fuzzy queries, providing a far more intuitive and accurate search experience. [ObjectBox], [Greenrobot]
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] 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], [Accenture], [ObjectBox]
Caching and Efficiency: On resource-constrained edge devices, efficiency is paramount. A vector database can act as an intelligent cache. By embedding an incoming user query and searching for highly similar past queries, the system can potentially return a cached response without needing to engage the more computationally expensive LLM. This saves processing cycles, reduces latency, and lowers power consumption—all critical considerations for battery-powered devices. [ObjectBox]
Addressing the On-Device Infrastructure Gap
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], [DataCamp] 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. The rapid development of smaller, yet powerful, on-device models (like Apple's "LLM in a Flash," Microsoft's Phi-3, and Google's Gemma) and the broader strategic shift toward Edge AI have created a new and distinct market segment: the on-device vector database. [ObjectBox], [ObjectBox]
This emerging market has created a new battleground for database supremacy. The technical requirements for success at the edge are not about managing petabytes of data across thousands of servers, but about extreme resource efficiency, a minimal memory and binary footprint, ACID compliance for transactional safety, and robust offline-first capabilities. This has opened the door for new, specialized players or existing companies with deep expertise in embedded and mobile databases to address the market. Solutions like ObjectBox are explicitly targeting this gap, engineering their vector search capabilities from the ground up to be optimized for restricted environments, emphasizing features like a small binary size (around 1MB), a low memory footprint, and a sophisticated multi-layered caching system that minimizes the amount of data that must be held in RAM at any given time. [ObjectBox], [ObjectBox]
This situation presents a classic "innovator's dilemma" for the incumbent cloud-native database companies. Their existing architectures, optimized for distributed scale, are not easily adapted to the unique constraints of a single, resource-limited device. The vector database market is not monolithic; a new, highly specialized "edge" segment is forming rapidly. The winners in this space will be the companies that master resource optimization and efficient on-device performance, not just distributed scaling.
The Next Wave: Agentic AI and the Evolving Edge Stack
The future outlook presented in the original article requires a significant update to incorporate the most impactful trend of 2025: the transition from generative to Agentic AI. This leap toward autonomy fundamentally transforms the requirements for the edge and solidifies the role of the on-device stack as a mission-critical component of future intelligent systems.
From Generative to Agentic AI: The Autonomy Leap
The distinction between Generative AI and Agentic AI is crucial. Generative AI excels at creating novel content in response to a specific prompt; it is a powerful tool for content creation, summarization, and translation. [Gartner] Agentic AI, however, represents a significant step beyond this. An agentic system can perceive its environment, set and refine goals, and "autonomously plan and take actions" to achieve those goals, often with little to no direct human intervention. [Forbes], [Wire19] McKinsey identifies this as a major new trend, describing these systems as "virtual coworkers" that can independently plan and execute complex, multistep workflows. [McKinsey], [McKinsey via DHInsights]
The market is projected to adopt this technology with remarkable speed. Gartner predicts that by 2028, 15% of all edge computing deployments will utilize agentic AI, a dramatic increase from near zero in 2024. In parallel, they forecast that 33% of all enterprise software applications will include agentic capabilities by the same year. [Gartner], [Wire19] This rapid adoption signals a fundamental shift in how enterprises will leverage AI, moving from tools that assist humans to autonomous partners that execute business processes.
This shift has profound implications for the edge. Agentic AI is a perfect architectural match for edge computing. The use cases envisioned—such as a smart factory where an agent autonomously detects production anomalies and reroutes workflows in real time, or a smart city grid where an agent automatically optimizes energy distribution based on live demand data—all depend on the core strengths of the edge. [Forbes], [Wire19] These systems require the real-time data processing, low-latency decision-making, and operational reliability that only a localized, on-device deployment can provide. Relying on the cloud for the constant sense-plan-act loop of an autonomous agent would be untenable due to network latency and connectivity issues.
The Future-Ready On-Device Stack
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.
Action: The stack must provide secure and reliable interfaces for the agent to interact with other software APIs or hardware controllers to execute the steps of its plan in the physical or digital world.
The on-device vector database is the central, indispensable component of this stack. It provides the persistent, queryable memory that connects perception to action. It is what allows an agent to learn from its experiences and improve its performance over time, transforming it from a simple automaton into a truly intelligent system. 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.
Strategic Outlook and Concluding Analysis
The technological and market forces of 2025 have solidified the strategic importance of the edge. The confluence of data gravity, the non-negotiable demand for data privacy, the imperative for real-time autonomous action, and the escalating economic and environmental costs of centralized AI is fundamentally repositioning the edge in the global technology architecture. It is no longer a mere peripheral extension of the cloud; it is rapidly becoming a primary center of innovation, value creation, and competitive differentiation. McKinsey's observation of "accelerating innovation 'at the edge'" captures this pivotal and enduring shift. [McKinsey]
Within this new edge-centric paradigm, the on-device vector database has emerged as more than just a useful component; it is the foundational element that enables localized intelligence. It provides the essential long-term memory for the next generation of agentic systems, the rich semantic context for generative models, and the secure, private repository for personal data. As on-device AI models become more sophisticated and autonomous, the role of this underlying data layer will only grow in importance. It is the architectural lynchpin that allows an edge device to transform from a simple data collector into an intelligent, learning, and acting entity.
Based on this analysis, the strategic recommendations for key stakeholders are clear. For technology vendors and platform providers, the focus must be on delivering a complete, optimized, and developer-friendly on-device stack that abstracts away the immense complexity of the underlying data, model, and system optimizations. For enterprises and product developers, the time for isolated experimentation with Edge AI is over. 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.