Centralized data centers use a lot of energy and water, emit a lot of CO2, and generate a lot of electronic waste. In fact, cloud data centers are already responsible for around 300 Mt of CO2-eq greenhouse gas emissions [1]. And the energy consumption of data centers is increasing at an exponential rate [2].
This challenge is further compounded by the exploding demand for AI workloads. With AI adoption accelerating, the demand for data center capacity is projected to grow by over 20% annually, potentially reaching ~300 GW by 2030. Remarkably, 70% of this capacity will be dedicated to hosting AI workloads. Gartner predicts that without sustainable AI practices, AI alone could consume more energy than the human workforce by 2025, significantly undermining carbon-zero initiatives.
While more data centers are switching to green energy [3], this approach is not nearly enough to solve the problem. A more sustainable approach is to reduce unnecessary cloud traffic, central computation, and storage as much as possible by shifting computation to the edge. In our experience, just reducing data overhead and unnecessary data traversals can easily cut 60-90% of data traffic and thus significantly impact the CO2 footprint of an application, as well as costs.
Edge Computing stores and uses data on or near the device on which it was created. This reduces the amount of traffic sent to the cloud and, on a large scale, has a significant impact on energy consumption and carbon emissions.
Why do Digitization projects need to think about sustainability now?
Given the gravity of the climate crisis, every industry needs to assess its potential environmental impact and find ways to reduce its carbon footprint. The digital world, and its most valuable commodity, data, should not be any different. The digital transformation is ongoing and with it electronic devices and IT usage numbers are exploding. Thus, new apps must consider their carbon footprint throughout their lifecycle, especially resource use in operation and at scale [4].
Also, think about this: The share of global electricity used by data centers is already estimated to be around 1-1.5% [1] and data centers generate 2% of worldwide CO2 emissions (on par with the aviation industry) [5]. Recent analyses by Gardian suggests that the greenhouse gas emissions from the in-house data centers of major tech companies—Google, Microsoft, Meta, and Apple—are likely about 7.62 times higher than their official reports indicate. [6]. On top of this, providing and maintaining cloud infrastructure (manufacturing, shipping of hardware, buildings and lines) also consumes a huge amount of greenhouse gasses [7] and produces a lot of abnormal waste (e.g. toxic coolants) at the end of life [8].
Bearing that in mind, the growth rate for data center demand is concerning. The steady increase in data processing, storage, and traffic in the future, comes with a forecasted electricity consumption by data centers to grow by 10% a year [9]. In fact, estimations expect the communications industry to use 20% of all the world’s electricity by 2025 [10].
Shifting to green energy is a good step. However, a more effective and ultimately longer term solution requires looking at the current model of data storage, filtering, processing and transferal. By implementing Edge Computing, we can reduce the amount of useless and wasteful data traversing to and from the cloud as much as possible, thus reducing overall energy requirements in the long term. Of course, everyone can make a difference with their daily behavior and for developers that is especially true: Applying green coding principles helps producing applications that produce lower CO2 emissions over the whole app lifetime.
What is Edge Computing?
Until recently 90% of enterprise data was sent to the cloud, but this is changing rapidly. In fact, this number is dropping to only 25% by 2025,according to Gartner. By then, most of the data will be stored and used locally, on the device it was created on, e.g. on smartphones, cars, trains, machines, watches. This is Edge Computing, and it is an inherently decentralized computing paradigm (as opposed to the centralized cloud computing approach). Accordingly, every edge device needs the same technology stack (just in a much smaller format) as a cloud server. This means: An operating system, a data storage / persistence layer (database), a networking layer, security functionalities etc. that run efficiently on restricted hardware.
As you can only use the devices’ resources, which can be pretty limited, inefficient applications can push a device to its limits, leading to slow response rates, crashes, and battery drain.
EDGE DEVICE ARCHITECTURE
Edge Computing is much more than some simple data pre-processing, which takes advantage of only a small portion of the computing that is possible on the edge. AnEdge Database is a prerequisite for meaningful Edge Computing. With an Edge Database, data can be stored and processed on the devices directly (the so-called edge). Only useful data is sent to the server and saved there, reducing the networking traffic and computing power used in data centers tremendously, while also making use of the computing resources of devices which are already in use. This greatly reduces bandwidth and energy required by data centers. On top, Edge Computing also provides the flexibility to operate independently from an Internet connection, enables fast real time response rates, and cuts cloud costs.
Why is Edge Computing sustainable?
Edge Computing reduces network traffic and data center usage
With Edge Computing the amount of data traversing the network can be reduced greatly, freeing up bandwidth. Bandwidth is a measure of the quantity / size of data a network can transfer in a given time frame. Bandwidth is shared among users. Accordingly, the more data is supposed to be sent via the network at a given moment, the slower the network speed. Data on the edge is also much more likely to be useful and indeed used on the edge, in context of its environment. Instead of constantly sending data strems to the cloud, it therefore makes sense to work with the data on the edge and only send that data to the cloud that really is of use there (e.g. results, aggregated data etc.).
Edge computing is optimized for efficiency
Edge “data centers” are typically more efficient than cloud data centers. As described above, resources on edge devices are restricted. Therefore, and as opposed to cloud infrastructure, edge devices do not scale horizontally. That is one reason why every piece of the edge tech stack is – typically and ideally – highly optimized for resource efficiency. Any computing done more efficiently helps reduce energy consumption. Taking into account the huge number of devices already deployed , the worldwide impact of reducing resource use for the same operations is significant.
Edge Computing uses available hardware
There is a realm of edge devices already deployed that is currently underused. Many existing devices are capable of data persistence, and some even for fairly complex computing. When these devices – instead – send all of their data to the cloud, an opportunity is lost. Edge Computing enables companies to use existing hardware and infrastructure (retrofitting), taking advantage of the available computing power. If these devices continue to be underused, we will need to build bigger and bigger central data centers, simultaneously burdening existing network infrastructure and reducing bandwidth for senselessly sending everything to the cloud.
Cloud versus Edge: an Example
Today, many projects are built based on cloud computing. Especially in first prototypes or pilots, cloud computing offers an easy and fast start. However, with scale, cloud computing often becomes too slow, expensive, and unreliable. In a typical cloud setup, data is gathered on edge devices and forwarded to the cloud for computation and storage. Often a computed result is sent back. In this design, the edge devices are dumb devices that are dependent upon a working internet connection and a working cloud server; they do not have any intelligence or logic of their own. In a smart home cloud example, data would be sent from devices in the home, e.g. a thermostat, the door, the TV etc. to the cloud, where it is saved and used.
If the user would want to make changes via a cloud-based mobile app when in the house, the changes would be sent to the cloud, changed there and then from there be sent to the devices. When the Internet connection is down or the server is not working, the application will not work.
With Edge Computing, data stays where it is produced, used and where it belongs – without traversing the network unnecessarily. This way, cloud infrastructure needs are reduced in three ways: Firstly, less network traffic, secondly, less central storage and thirdly less computational power. Rather, edge computing makes use of all the capable hardware already deployed in the world. E.g. in a smart home, all the data could stay within the house and be used on site. Only the small part of the data truly needed accessible from anywhere would be synchronized to the cloud.
Take for example a thermostat in such a home setting: it might produce 1000s of temperature data points per minute. However, minimal changes typically do not matter and data updates aren’t necessary every millisecond. On top, you really do not need all this data in the cloud and accessible from anywhere.
With Edge Computing, this data can stay on the edge and be used within the smart home as needed. Edge Computing enables the smart home to work fast, efficiently, and autonomous from a working internet connection. In addition, the smart home owner can keep the private data to him/herself and is less vulnerable to hacker attacks.
How does ObjectBox make Edge Computing even more sustainable?
ObjectBox improves the sustainability of Edge Computing with high performance and efficiency: our 10X speed advantage translates into less use of CPU and battery / electricity. With ObjectBox, devices compute 10 times as much data with equivalent power. Due to the small size and efficiency, ObjectBox runs on restricted devices allowing application developers to utilize existing hardware longer and/or to do more instead of existing infrastructure / hardware.
ObjectBox’ Sync solution takes care of making data available where needed when needed. It allows synchronization in an offline setting and / or to the cloud. Based on efficient syncing principles, ObjectBox Sync aims to reduce unnecessary data traffic as much as possible and is therefore perfectly suited for efficient, useful, and sustainable Edge Computing. Even when syncing the same amount of data, ObjectBox Sync reduces the bandwidth needed and thus cloud networking usage, which incidentally reduces cloud costs.
Finally, ObjectBox’ Time Series feature, provides users an intuitive dashboard to see patterns behind the data, further helping users to track thousands of data points/second in real-time.
How Edge Computing enables new use cases that help make the world more sustainable
As mentioned above, there are a variety of IoT applications that help reduce waste of all kinds. These applications can have a huge impact on creating a more sustainable world, assuming the applications themselves are sustainable. Three powerful examples to demonstrate the huge impact IoT applications can have on the world:
Reducing Food Waste
From farm to kitchen, IoT applications can help to reduce food waste across the food chain. Sensors used to monitor the cold chain, from field to supermarket, can ensure that food maintains a certain temperature, thus guaranteeing that products remain food safe and fresh longer, reducing food waste. In addition, local storage can be used to power apps that fight household waste (you can learn how to build a food sharing app yourself in Flutter with this tutorial).
Smart City Lighting
Smart City Lighting: Chicago has implemented a system which allows them to save approx. 10 million USD / year and London estimates it can save up to 70% of current electricity use and costs as well as maintenance costs through smart public lighting systems [10].
Reducing Water Waste
Many homes and commercial building landscapes are still watered manually or on a set schedule. This is an inexact method of watering, which does not take into account weather, soil moistness, or the water levels needed by the plant. Using smart IoT water management solutions, landscape irrigation can be reduced, saving water and improving landscape health.
These positive effects are all the more powerful when the applications themselves are sustainable.
Sustainable digitization needs an edge
The benefits of cloud computing are broad and powerful, however there are costs to this technology. A combination of green data centers and Edge Computing helps to resolve these often unseen costs. With Edge Computing we can reduce the unnecessary use of bandwidth and server capacity (which comes down to infrastructure, electricity and physical space) while simultaneously taking advantage of underused device resources. Also with AI growing in popularity, Edge Computing will become very relevant for sustainable AI applications. AI applications are very resource intensive and Edge AI will help to distribute workloads in a resourceful manner, lowering the resource-use. One example of this is an efficient local vector database. ObjectBox amplifies these benefits, with high performance on small devices and efficient data synchronization – making edge computing an even more sustainable solution.
The healthcare industry is experiencing an unprecedented surge in data generation, responsible for approximately 30% of the world’s total data volume. This vast and fast-growing amount of health data is the primary force behind the digital transformation of healthcare. Only through the adoption of advanced technologies can healthcare providers manage, analyze, and secure this information. While COVID-19 accelerated this shift, contributing to the explosion of health data, the ongoing demand for real-time patient insights, personalized treatment, and improved operational efficiency continues to drive the sector toward digitalization and AI. Simultaneously, growing data privacy concerns, increasing costs, and heavier regulatory requirements are challenging the use of cloud computing to manage this data. A megashift to Edge Computing and Edge AI is addressing these challenges, enabling a faster, safer, and more reliable digital healthcare infrastructure.
The digital healthcare market 2024 and beyond, a high-speed revolution
Prior to COVID, growth in digital health adoption stalled. However, digitalization in the healthcare industry has sky-rocketed since the start of the pandemic. Reflecting this market turnaround, followed by the rise of advanced digital tools like AI, recent years have been record-breaking for investments in healthcare companies. A trend that will continue in the next years, as analysts predict rapid growth across digital healthcare market sectors:
Drivers of growth and change in digital healthcare
Digital Healthcare Growth Driver 1: Growing Medical IoT Device Adoption
There will be a projected 40 billion IoT devices by 2030.IoMT devices already accounted for 30% of the entire IoT device market in 2020. Internet of Medical Things (IoMT) are hardware devices designed to process, collect, and/or transmit health–related data via a network. According to Gartner, 79% of healthcare providers are already using IoT in their processes, i.e. remote health monitoring via wearables, ingestible sensors, disinfection robots, or closed-loop insulin delivery systems.IoMT devices increase safety and efficiency in healthcare, and future technical applications, like smart ambulances or augmented reality glasses that assist during surgery, are limitless.
IoMT devices accounted for 30% of the IoT device market
Digital Healthcare Growth Driver 2: The Explosion of Health Data
Growing IoMT adoption is subsequently driving a rapid increase in the amount of collected health data. According to the RBC study, the healthcare industry is now responsible for approximately 30% of the world’s total data volume. By 2025, healthcare data is expected to continue growing at a 36% CAGR, outpacing data volumes from sectors like manufacturing, financial services, and media. Big health data sets are being used to revolutionize healthcare, bringing new insights into fields like oncology, and improving patient experience, care, and diagnosis. According to the Journal of Big Data: “taken together, big data will facilitate healthcare by introducing prediction of epidemics (in relation to population health), providing early warnings of disease conditions, and helping in the discovery of novel biomarkers and intelligent therapeutic intervention strategies for an improved quality of life.” In fact, the healthcare analytics market is projected to reach $129.7 billion by 2028, growing at a 23.5% CAGR. This growth is driven by the need for real-time data processing, personalized medicine, and predictive analytics to manage chronic conditions and optimize hospital operations.
Healthcare data occupies ~30% of the world’s total data volume
Digital Healthcare Growth Driver 3: Artificial Intelligence
The increase in healthcare data opens up new opportunities and challenges to apply advanced technologies like big data analytics and artificial intelligence (AI) to improve healthcare delivery, patient outcomes, and operational efficiency. For instance, AI is being used to analyze medical imaging data, identify patterns in electronic health records, and predict patient outcomes, contributing to improved patient care. By 2026, AI is projected to save the global healthcare industry over $150 billion annually, by answering “20 percent of unmet clinical demand.”
Digital Healthcare Growth Driver 4: Artificial Intelligence
With the rise of IoMT and the boost in healthcare data,Edge Computing is becoming a key driver of healthcare digitalization. The majority of IoMT devices (55.3 %) currently operate on-premise rather than in the cloud, ensuring faster, more secure real-time data processing. This shift to Edge Computing enhances data privacy and reduces latency, which is critical in life-critical medical applications. Additionally, the development ofSmall Language Models (SLMs) for on-device AI (Edge AI) allows healthcare providers to deploy AI-powered solutions directly on medical devices. This helps with tasks like remote monitoring and diagnostics without the need for cloud connectivity, which is particularly beneficial in environments with limited internet access.
As IoMT continues to evolve, Edge Computing will play an essential role in supporting healthcare’s increasing demand for real-time data processing. By 2025, it is projected that 75% of the healthcare data will be generated at the Edge, further driving the adoption of these technologies across the industry.
75% of the healthcare data will be generated at the Edge in 2025
Digital Healthcare Growth Driver 5: Underlying Social Megatrends
The global population is growing; global life expectancy is rising. Accordingly, by 2030 the world needs more energy, more food, and more water. Explosive population growth in some areas versus declines in others contributes to shifts in economic power, resource allocation, societal habits, and norms. Many Western populations are aging rapidly. E.g. in America, the number of people 65+ is expected to nearly double to 72.1 million by 2034. Because the population is shrinking at the same time, elder care is a growing challenge and researchers are looking to robots to solve it.
Health megatrends focus not only on the prevention of disease, but also on the perception of wellness, and new forms of living and working. Over this decade more resources will be spent on health and longevity, leading to artificially and technologically enhanced human capabilities. More lifestyle-related disorders and diseases are expected to emerge in the future.
A focus on health and longevity will lead to artificial & tech-enhanced human capabilities
The Challenges of Healthtech
Along with more data, more devices, and more opportunities also comes more responsibility and more costs for healthcare providers.
Data Volume and Availability With the growing number of digital healthcare and medical devices, a dazzling volume of health data is created and collected across many different channels. It will be vital for the healthcare industry to reliably synchronize and combine data across devices and channels. Due to the sheer volume, reliable collection and analysis of this data is a major challenge. After it’s been processed, data needs to be available on demand, i.e. in emergency situations that require reliable, fast, available data.
IT Costs Medical devices contribute a large portion to healthcare budgets. However as data volumes grow, data costs will also become a relevant cost point. Sending all health data to the cloud to be stored and processed is not only slow and insecure, it is also extremely costly. To curb mobile network and cloud costs, much health data can be stored and processed at the edge, on local devices, with only necessary data being synced to a cloud or central server. By building resilient data architecture now, healthcare providers (e.g. hospitals, clinics, research centers) can avoid future costs and headaches.
Edge Computing is Integral to Data-driven Healthcare Ecosystems
With big data volumes, industries like healthcare need to seek out resilient information architectures to accommodate growing numbers of data and devices. To build resilient and secure digital infrastructure, healthcare providers will need to utilize both cloud computing and edge computing models, exploiting the strengths of both systems.
Cloud & Edge: What’s the Difference?
Cloud Computing information is sent to a centralized data center, to be stored, processed and sent back to the edge. This causes latency and a higher risk of data breaches. Centralized data is useful for large-scale data analysis and the distribution of data between i.e. hospitals and doctors’ offices.
Edge Computing Data is stored and processed on or near the device it was created on. Edge Computing works without an internet connection, and thus is reliable and robust in any scenario. It is ideal for time-sensitive data (real-time), and improved data privacy and security.
Edge Computing contributes to resilient and secure healthcare data systems
Transforming Healthcare with Edge Computing
Use Case: Secure and Up to Date Digital Record Keeping in Doctors Offices
For private doctors’ offices, embracing digitalization comes with different hurdles than larger healthcare providers. Often, offices do not keep a dedicated IT professional on staff, and must find digital solutions that serve their needs, while allowing them to comply with ever-increasing data regulations. As an industry used to legislative challenges, GPs know that sensitive patient data must be handled with care.
Solution providers serving private doctors’ offices are using edge databases to help keep patient data secure. An edge database allows private GPs to collect and store digital data locally. In newer practice setups, doctors use tablets, like iPads, throughout their practice to collect and track patient data, take notes and improve flexibility. This patient data should not be sent or stored in a central cloud server as this increases the risk of data breaches and opens up regulatory challenges. In a cloud-centered setup, the doctor also always needs to rely on a constant internet connection being available, making this also a matter of data availability
Accordingly, the patient data is stored locally, on the iPads, accessible only by the doctor treating the patient. Some of the data is synchronized to a local, in-office computer at the front desk for billing and administration. Other data is only synchronized for backup purposes and encrypted. Such a setup also allows synchronizing data between iPads, enabling doctors to share data in an instant.
Use Case: Connected Ambulances – Real-Time Edge Data from Home to Hospital
Between an incidence location and the hospital, a lot can happen. What if everything that happened in the ambulance was reliably and securely tracked and shared with the hospital, seamlessly? There are already trials using 5G technology to stream real-time data to hospitals, allowing ambulance medics to access patient data while in transit. Looking to the future, Edge Computing will enable digital healthcare applications to function in real-time and reliably anywhere and anytime, e.g. a moving ambulance, in the tunnel, or a remote area, enabling ambulance teams and doctors to give the best treatment instantly / on-site, while using available bandwidth and networks when available to seamlessly synchronize the relevant information to the relevant healthcare units, e.g. the next hospital. This will decrease friction, enhance operational processes, and improve time to treatment.
Digital Healthcare: Key Take-Aways
Digital healthcare is a fast-growing industry; more data and devices alongside new tech are empowering rapid advances. Finding ways to utilize growing healthcare data, while ensuring data privacy, security and availability are key challenges ahead for healthcare providers. The healthcare industry must find the right mix of technologies to manage this data, utilizing cloud for global data exchange and big data analytics, while embracing Edge Computing for it’s speed, security, and resilience.
It’s all about data availability. Either in emergency situations, or simply to provide a smooth patient experience, data needs to be fast, reliable, and available: when you need it where you need it.
Edge computing alongside other developing technologies like 5G or Artificial Intelligence will empower a new and powerful digital healthcare ecosystem.
ObjectBox provides edge data software, to empower scalable and resilient digital innovation on the edge in healthcare, automotive, and manufacturing. ObjectBox’ edge database and data synchronization solution is 10x faster than any alternative, and empowers applications that respond in real-time (low-latency), work offline without a connection to the cloud, reduce energy needs, keep data secure, and lower mobile network and cloud costs.
What is Edge AI?Edge AI (also: “on-device AI”, “local AI”) brings artificial intelligence to applications at the network’s edge, such as mobile devices, IoT, and other embedded systems like, e.g., interactive kiosks. Edge AI combines AI with Edge Computing, a decentralized paradigm designed to bring computing as close as possible to where data is generated and utilized.
What is Cloud AI? As opposed to this, cloud AI refers to an architecture where applications rely on data and AI models hosted on distant cloud infrastructure. The cloud offers extensive storage and processing power.
An Edge for Edge AI: The Cloud
Example: Edge-Cloud AI setup with a secure, two-way Data Sync architecture
Today, there is a broad spectrum of application architectures combining Edge Computing and Cloud Computing, and the same applies to AI. For example, “Apple Intelligence” performs many AI tasks directly on the phone (on-device AI) while sending more complex requests to a private, secure cloud. This approach combines the best of both worlds – with the cloud giving an edge to the local AI rather than the other way around. Let’s have a look at the advantages on-device AI brings to the table.
Faster Response Rates. Processing data locally cuts down travel time for data, speeding up responses.
Increased Availability. On-device processing makes apps fully offline-capable. Operations can continue smoothly during internet or data center disruptions.
Sustainability/costs. Keeping data where it is produced and used minimizes data transfers, cutting networking costs and reducing energy consumption—and with it, CO2 emissions.
Challenges of Local AI on the Edge
Data Storage and Processing: Local AI requires an on-device database that runs on a wide variety of edge devices (Mobile,IoT, Embedded) and performs complex tasks such as vector search locally on the device with minimal resource consumption.
Data Sync: It’s vital to keep data consistent across devices, necessitating robust bi-directional Data Sync solutions. Implementing such a solution oneself requires specialized tech talent, is non-trivial and time-consuming, and will be an ongoing maintenance factor.
Small Language Models:Small Language Models (SLMs) like Phi-2 (Microsoft Research), TinyStories (HuggingFace), and Mini-Giants (arXiv) are efficient and resource-friendly but often need enhancement with local vector databases for better response accuracy. An on-device vector database allows on-device semantic search with private, contextual information, reducing latency while enabling faster and more relevant outputs. For complex queries requiring larger models, a database that works both on-device and in the cloud (or a large on-premise server) is perfect for scalability and flexibility in on-device AI applications.
On-device AI Use Cases
On-device AI is revolutionizing numerous sectors by enabling real-time data processing wherever and whenever it’s needed. It enhances security systems, improves customer experiences in retail, supports predictive maintenance in industrial environments, and facilitates immediate medical diagnostics. On-device AI is essential for personalizing in-car experiences, delivering reliable remote medical care, and powering personal AI assistants on mobile devices—always keeping user privacy intact.
Personalized In-Car Experience: Features like climate control, lighting, and entertainment can be adjusted dynamically in vehicles based on real-time inputs and user habits, improving comfort and satisfaction. Recent studies, such as one by MHP, emphasize the increasing consumer demand for these AI-enabled features. This demand is driven by a desire for smarter, more responsive vehicle technology.
Remote Care: In healthcare, on-device AI enables on-device data processing that’s crucial for swift diagnostics and treatment. This secure, offline-capable technology aligns with health regulations like HIPAA and boosts emergency response speeds and patient care quality.
Personal AI Assistants: Today’s personal AI assistants often depend on the cloud, raising privacy issues. However, some companies, including Apple, are shifting towards on-device processing for basic tasks and secure, anonymized cloud processing for more complex functions, enhancing user privacy.
ObjectBox for On-Device AI – an edge for everyone
The continuum from Edge to Cloud
ObjectBox supports AI applications from Edge to cloud. It stands out as the first on-device vector database, enabling powerful Edge AI on mobile, IoT, and other embedded devices with minimal hardware needs. It works offline and supports efficient, private AI applications with a seamless bi-directional Data Sync solution, completely on-premise, and optional integration with MongoDB for enhanced backend features and cloud AI.
Interested in extending your AI to the edge? Let’s connect to explore how we can transform your applications.
In today’s fast-paced, decentralized world valuable data is generated by everything, everywhere, and all at once. To harness the vast opportunities offered by this data for data-driven organizations and AI applications, you need to be able to access the data and seamlessly distribute it to when and where it’s needed.
The key to achieving this lies in efficient, offline-first on-device data storage, reliable bi-directional data sync, and a scalable data management backend in the cloud. In other words, you need the infrastructure to manage data flows bi-directionally to tap into fresh data throughout your organization, processes, and applications at the right time.
Together, MongoDB and ObjectBox provide developers with a robust solution to empower seamless workload and data flows on the edge and from the edge to the cloud. ObjectBox seamlessly syncs data bi-directionally across devices even without Internet and syncs back to the cloud and MongoDB when connected. With ObjectBox devices stay in sync also in environments with intermittent connectivity, high latency, or flaky networks. Capture and unlock the value of all your data, anytime, anywhere, without relying on a constant Internet connection, with MongoDB + ObjectBox.
Seamless Offline-First Data Sync for Edge Devices
Maintaining service continuity is essential, even when devices are offline. Your customers, users, operations, and employees need to be able to rely on essential data at all times. That’s where ObjectBox comes in. It comprises of two key components: the ObjectBox Database and ObjectBox Data Sync.
The ObjectBox Database is a lightweight, on-device solution that is highly resource-efficient and fast on restricted hardware like mobile, IoT, and embedded devices, and even in the cloud.
ObjectBox Data Sync enables seamless bi-directional data synchronization between devices. By handling only incremental changes in a compressed binary format, ObjectBox Sync ensures minimal data transfer, automatic conflict resolution, and a seamless user experience even in fluctuating network conditions. This approach effectively simplifies the development process by offering complex sync logic via easy native-language APIs, allowing developers to focus on core app functionality.
Once a connection is available, ObjectBox Data Sync instantly synchronizes changes with MongoDB, providing real-time, bi-directional data sync between edge devices and MongoDB’s robust cloud backend.
The Benefits of Offline-First and Real-Time Data Sync with MongoDB and ObjectBox:
Resource-efficiency & Highspeed: ObjectBox excels at consuming minimal computational resources (CPU, power, memory, …) while delivering data persistence speed that is typically on-par with in-memory caches for read operations.
Offline-First Operation: Ensure continuous app performance, even with no internet connection. ObjectBox stores and syncs data bi-directionally on the edge and additionally with MongoDB once connected.
Real-Time Data Sync: Get reliable, bi-directional data synchronization across devices and MongoDB, enabling real-time updates and data consistency.
Scalable Edge: Easily handle 100k operations / second on a single device. Host the Sync server on any edge device (like a phone) and easily handle 3M clients with a three-node cluster.
Scalable Cloud Backend: With MongoDB, businesses can scale their applications to handle growing data and performance demands, seamlessly syncing data between millions of devices and the cloud.
Flexible Setup Scenarios: Tailor Data Sync to Your Needs
ObjectBox and MongoDB offer flexible setup scenarios to meet different application needs. The two main setup options are the central sync and the edge sync setup.
The Central Sync Setup syncs data between edge devices and MongoDB in the cloud, providing centralized data management while retaining offline-first functionality. The ObjectBox Sync Server runs in the cloud or on-premise.
The Edge Sync Setup allows devices to operate and sync data efficiently offline between ObjectBox instances within an edge, e.g. within one location, or within a car. When reconnected, changes are synchronized back to MongoDB making it ideal for environments with intermittent connectivity or distributed devices that need to function independently while syncing back to the cloud when possible.
This structure offers a flexible approach to integrating edge and cloud systems, empowering organizations to choose the setup that best fits their specific use case. More details.
Use Cases for MongoDB + ObjectBox :
Data-Driven Organizations: In a data-driven organization, every decision relies on access to relevant, up-to-date data. ObjectBox enables real-time data collection and synchronization from edge devices, ensuring access to critical data, even when devices are intermittently connected. This streamlines operations, improves decision-making, and enhances analysis across distributed teams and IoT systems. With MongoDB’s scalable cloud infrastructure, decentralized data integrates seamlessly with the cloud backend for efficient management.
Point-of-Sale (PoS) & Retail Edge Computing: A seamless customer experience and the ability to keep selling and never lose a transaction, even during internet outages, are essential for PoS systems / in retail. ObjectBox enables offline-first data storage and syncing for PoS systems, allowing transactions to be processed locally, even without internet connectivity. When connectivity returns, ObjectBox syncs transaction data back to MongoDB in real time, ensuring data consistency across multiple locations. Retailers can then leverage MongoDB’s analytics to gain insights into customer behavior and optimize inventory management.
Software-Defined Vehicle (SDV) & Connected Cars: Modern vehicles generate vast amounts of data from sensors and onboard systems. ObjectBox enables efficient on-device storage and processing, providing real-time access to data for navigation, diagnostics, and infotainment systems. ObjectBox Data Sync ensures that local data is synced back to MongoDB when connectivity is available, supporting centralized analytics, fleet management, and predictive maintenance, optimizing performance and safety while enhancing the user experience.
Manufacturing & Smart Shopfloor Apps: In smart factories, machines and sensors continuously generate data that must be analyzed and processed in real time. ObjectBox enables local data storage and fast data sync on-premise without the necessity for an Internet connection, ensuring that critical systems that are not connected to the Internet can run smoothly on-site. With a connected instance, ObjectBox takes care of synchronizing this data with the cloud and MongoDB for further analysis and central dashboards.
AI-Applications with On-device Vector Search: ObjectBox is the first and only on-device vector database, empowering developers to run AI locally on mobile, IoT, embedded, and other commodity devices (Edge AI). In combination with a Small Language Model (SLM), this allows developers to build local AI applications (e.g. RAG, genAI) that run directly on the device—without needing a cloud connection. By syncing with MongoDB, businesses can combine the power of on-device AI with centralized cloud data for even greater insights and performance. This is especially beneficial in scenarios requiring real-time decision-making, such as personalized customer experiences and predictive maintenance.
In today’s data-driven world, a data-first strategy requires seamless integration between edge and cloud data management. The combination of MongoDB and ObjectBox unlocks the full potential of your data. MongoDB’s powerful cloud platform, together with ObjectBox’s efficient on-device database and offline-first capabilities, is ideal for capturing the value of your data from anywhere, including distributed edge devices where valuable data is generated all the time. This partnership empowers businesses to seamlessly handle decentralized data, enabling fast and reliable operations at the edge while syncing back to the cloud for centralized management. Whether on IoT devices, mobile, embedded systems, or commodity hardware, ObjectBox and MongoDB ensure optimal performance everywhere. From remote areas to bad networks, our joint solution keeps data flowing reliably between the edge and the MongoDB backend, even when connectivity or nodes are lost.
As artificial intelligence (AI) continues to evolve, companies, researchers, and developers are recognizing that bigger isn’t always better. Therefore, the era of ever-expanding model sizes is giving way to more efficient, compact models, so-called Small Language Models (SLMs). SLMs offer several key advantages that address both the growing complexity of AI and the practical challenges of deploying large-scale models. In this article, we’ll explore why the race for larger models is slowing down and how SLMs are emerging as the sustainable solution for the future of AI.
From Bigger to Better: The End of the Large Model Race
Up until 2023, the focus was on expanding models to unprecedented scales. But the era of creating ever-larger models appears to be coming to an end. Many newer models like Grok or Llama 3 are smaller in size yet maintain or even improve performance compared to models from just a year ago. The drive now is to reduce model size, optimize resources, and maintain power.
The Plateau of Large Language Models (LLMs)
Why Bigger No Longer Equals Better
As models become larger, developers are realizing that the performance improvements aren’t always worth the additional computational cost. Breakthroughs in knowledge distillation and fine-tuning enable smaller models to compete with and even outperform their larger predecessors in specific tasks. For example, medium-sized models like Llama with 70B parameters and Gemma-2 with 27B parameters are among the top 30 models in the chatbot arena, outperforming even much larger models like GPT-3.5 with 175B parameters.
The Shift Towards Small Language Models (SLMs)
In parallel with the optimization of LLMs, the rise of SLMs presents a new trend (see Figure). These models require fewer computational resources, offer faster inference times, and have the potential to run directly on devices. In combination with an on-device database, this enables powerful local GenAI and on-device RAG apps on all kinds of embedded devices, like on mobile phones, Raspberry Pis, commodity laptops, IoT, and robotics.
Advantages of SLMs
Despite the growing complexity of AI systems, SLMs offer several key advantages that make them essential in today’s AI landscape:
Accessibility As SLMs are less resource-hungry (less hardware requirements, less CPU, memory, power needs), they are more accessible for companies and developers with smaller budgets. Because the model and data can be used locally, on-device / on-premise, there is no need for cloud infatstructure and they are also usable for use cases with high privacy requirements. All in all, SLMs democratize AI development and empower smaller teams and individual developers to deploy advanced models on more affordable hardware.
Cost Reduction and Sustainability Training and deploying large models require immense computational and financial resources, and comes with high operational costs. SLMs drastically reduce the cost of training, deployment, and operation as well as the carbon footprint, making AI more financially and environmentally sustainable.
On-Device AI for Privacy and Security SLMs are becoming compact enough for deployment on edge devices like smartphones, IoT sensors, and wearable tech. This reduces the need for sensitive data to be sent to external servers, ensuring that user data stays local. With the rise of on-device vector databases, SLMs can now handle use-case-specific, personal, and private data directly on the device. This allows more advanced AI apps, like those using RAG, to interact with personal documents and perform tasks without sending data to the cloud. With a local, on-device vector database users get personalized, secure AI experiences while keeping their data private.
The Future: Fit-for-Purpose Models: From Tiny to Small to Large Language models
The future of AI will likely see the rise of models that are neither massive nor minimal but fit-for-purpose. This “right-sizing” reflects a broader shift toward models that balance scale with practicality. SLMs are becoming the go-to choice for environments where specialization is key and resources are limited. Medium-sized models (20-70 billion parameters) are becoming the standard choice for balancing computational efficiency and performance on general AI tasks. At the same time, SLMs are proving their worth in areas that require low latency and high privacy.
Innovations in model compression, parameter-efficient fine-tuning, and new architecture designs are enabling these smaller models to match or even outperform their predecessors. The focus on optimization rather than expansion will continue to be the driving force behind AI development in the coming years.
Conclusion: Scaling Smart is the New Paradigm
As the field of AI moves beyond the era of “bigger is better,” SLMs and medium-sized models are becoming more important than ever. These models represent the future of scalable and efficient AI. They serve as the workhorses of an industry that is looking to balance performance with sustainability and efficiency. The focus on smaller, more optimized models demonstrates that innovation in AI isn’t just about scaling up; it’s about scaling smart.
Artificial Intelligence (AI) has become an integral part of our daily lives in recent years. However, it has been tied to running in huge, centralized cloud data centers. This year, “local AI”, also known as “on-device AI” or “Edge AI”, is gaining momentum. Local vector databases, efficient language models (so-called Small Language Models, SLMs), and AI algorithms are becoming smaller, more efficient, and less compute-heavy. As a result, they can now run on a wide variety of devices, locally.
Figure 1. Evolution of language model’s size with time. Large language models (LLMs) are marked as celadon circles, and small language models (SLMs) as blue ones.
What is Local AI (on-device AI, Edge AI)?
Local AI refers to running AI applications directly on a device, locally, instead of relying on (distant) cloud servers. Such an on-device AI works in real-time on commodity hardware (e.g. old PCs), consumer devices (e.g. smartphones, wearables), and other types of embedded devices (e.g. robots and point-of-sale (POS) systems used in shops and restaurants). An interest in local Artificial Intelligence is growing (see Figure 2).
Figure 2. Interest over time according to Google Trends.
Why use Local AI: Benefits
Local AI addresses many of the concerns and challenges of current cloud-based AI applications. The main reasons for the advancement of local AI are:
In a world where data privacy concerns are increasing, local AI offers a solution. Since data is processed directly on the device, sensitive information remains local, minimizing the risk of breaches or misuse of personal data. No need for data sharing and data ownership is clear. This is the key to using AI responsibly in industries like healthcare, where sensitive data needs to be processed and used without being sent to external servers. For example, medical data analysis or diagnostic tools can run locally on a doctor’s device and be synchronized to other on-premise, local devices (like e.g. PCs, on-premise servers, specific medical equipment) as needed. This ensures that patient data never leaves the clinic, and data processing is compliant with strict privacy regulations like GDPR or HIPAA.
Accessibility: AI for Anyone, Anytime
One of the most significant advantages of local AI is its ability to function without an internet connection. This opens up a world of opportunities for users in remote locations or those with unreliable connectivity. Imagine having access to language translation, image recognition, or predictive text tools on your phone without needing to connect to the internet. Or a point-of-sale (POS) system in a retail store that operates seamlessly, even when there’s no internet. These AI-powered systems can still analyze customer buying habits, manage inventory, or suggest product recommendations offline, ensuring businesses don’t lose operational efficiency due to connectivity issues. Local AI makes this a reality. In combination with little hardware requirements, it makes AI accessible to anyone, anytime. Therefore, local AI is an integral ingredient in making AI more inclusive and to democratize AI.
Sustainability: Energy Efficiency
Cloud-based AI requires massive server farms that consume enormous amounts of energy. Despite strong efficiency improvements, in 2022, data centers globally consumed between 240 and 340 terawatt-hours (TWh) of electricity. To put this in perspective, data centers now use more electricity than entire countries like Argentina or Egypt. This growing energy demand places considerable pressure on global energy resources and contributes to around 1% of energy-related CO2 emissions.
The rise of AI has amplified these trends. According to McKinsey, the demand for data center capacity is projected to grow by over 20% annually, reaching approximately 300GW by 2030, with 70% of this capacity dedicated to hosting AI workloads. Gartner even predicts that by 2025, “AI will consume more energy than the human workforce”. AI workloads alone could drive a 160% increase in data center energy demand by 2030, with some estimates suggesting that AI could consume 500% more energy in the UK than it does today. By that time, data centers may account for up to 8% of total energy consumption in the United States.
In contrast, local AI presents a more sustainable alternative, e.g. by leveraging Small Language Models, which require less power to train and run. Since computations happen directly on the device, local AI significantly reduces the need for constant data transmission and large-scale server infrastructure. This not only lowers energy use but also helps decrease the overall carbon footprint. Additionally, integrating a local vector database can further enhance efficiency by minimizing reliance on power-hungry data centers, contributing to more energy-efficient and environmentally friendly technology solutions.
When to use local AI: Use case examples
Local AI enables an infinite number of new use cases. Thanks to advancements in AI models and vector databases, AI apps can be run cost-effectively on less capable hardware, e.g. commodity PCs, without the need for an internet connection and data sharing. This opens up the opportunity for offline AI, real-time AI, and private AI applications on a wide variety of devices. From smartphones and smartwatches to industrial equipment and even cars, local AI is becoming accessible to a broad range of users.
Consumer Use Cases (B2C): Everyday apps like photo editors, voice assistants, and fitness trackers can integrate AI to offer faster and more personalized services (local RAG), or integrate generative AI capabilities.
Business Use Cases (B2B): Retailers, manufacturers, and service providers can use local AI for data analysis, process automation, and real-time decision-making, even in offline environments. This improves efficiency and user experience without needing constant cloud connectivity.
Conclusion
Local AI is a powerful alternative to cloud-based solutions, making AI more accessible, private, and sustainable. With Small Language Models and on-device vector databases like ObjectBox, it is now possible to bring AI onto everyday devices. From the individual user who is looking for convenient, always-available tools to large businesses seeking to improve operations and create new services without relying on the cloud – local AI is transforming how we interact with technology everywhere.
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