Explore your options for storage solutions for AI to learn which is right for your business.
The recent surge of data-driven insights and groundbreaking algorithms in artificial intelligence (AI) is unprecedented. Today, nearly 80% of devices use AI in some way, and almost 85% of C-suite executives name AI as a key tool in driving growth.
With more and more applications leveraging AI, the quest for innovation is on. And at its core lies a pivotal component: storage. But your choice of storage for AI is about more than hoarding data. It's about fueling the very essence of AI, enabling it to learn, adapt, and evolve.
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The biggest bottleneck in AI isn't compute anymore-it's getting data to the compute fast enough. Storage for AI workloads needs to be rethought from the ground up. Andrej Karpathy, Former Director of AI @ Tesla
The right storage solution is your gateway to harnessing the full potential of AI, bridging raw data and intelligent insights, unraveling mysteries within datasets, and propelling your projects to new heights. In addition to storing necessary data, storage built for AI has the speed, scalability, and adaptability to meet ever-evolving AI demands.
Your path to positioning your business at the forefront of AI innovation begins with a single step: choosing the right storage solution. Let's take a look at the intricacies of AI storage solutions to unravel the characteristics that set them apart and guide you in making an informed decision tailored to your unique AI needs.
AI applications, ranging from machine learning (ML) to deep learning (DL), need storage solutions that can handle vast volumes of data, deliver high throughput, offer low-latency access, and store data securely. Ultimately, the storage infrastructure must be able to support the iterative, data-intensive nature of AI workloads to facilitate seamless data ingestion, processing, and analysis.
According to NVIDIA's data center trends, AI training datasets are doubling in size every few months, making scalable storage infrastructure more critical than ever.
AI workloads require storage solutions that provide high throughput and low latency to accommodate the rapid processing of large datasets. High throughput ensures data can be read and written swiftly. Low latency guarantees quick data access, which is essential for real-time AI applications.
As AI models evolve and datasets grow, your storage infrastructure should scale accordingly. A scalable storage solution allows for the expansion of storage capacity and performance, ensuring the system can adapt to the increasing demands of AI applications.
Ensuring data durability and availability is crucial for AI applications. A robust storage solution must safeguard data against loss and provide continuous access to data, even in the event of hardware failures or other disruptions.
Several types of data and information need to be stored for AI applications. Each serves a distinct purpose in developing, training, and deploying AI models. Here are the key elements that require storage:
Raw data is unprocessed, unfiltered information collected from various sources, ie. analyst reports, Slack archives, product feedback, that serves as the foundation for training AI models. Depending on the application, this data can come in text, images, audio, video, or sensor readings.
Once raw data is collected, it often undergoes preprocessing to clean, normalize, and transform it into a suitable format for model training. Storing preprocessed data saves time and computational resources by avoiding redundant preprocessing steps.
Training datasets are subsets of preprocessed data used to train AI models. They're labeled with the correct output, which the model learns to predict. Storing training datasets is crucial for refining models and evaluating their performance.
Validation and test datasets are used to assess the performance of AI models during and after the training process. They help in tuning model parameters and avoiding overfitting, ensuring the model's generalization of unseen data.
AI models consist of parameters and weights that are adjusted during training. Storing these elements is vital for preserving the trained state of the model, enabling further fine-tuning, and deploying the model for inference.
The structure or architecture of an AI model, including the arrangement of layers and nodes, needs to be stored for model reconstruction and deployment.
Hyperparameters are external configurations for the model training process, such as learning rate and batch size. Storing hyperparameters is necessary for replicating training conditions and experimenting with model optimization.
Feature engineering involves creating new features from raw data to improve model performance. Artifacts from this process, such as feature selection criteria and transformation logic, need to be stored for consistency in model training and deployment.
Performance metrics, evaluation results, and logs generated during training and testing are stored for analyzing model performance, diagnosing issues, and making improvements.
Data used for making predictions, along with the resulting outputs, may be stored for auditing, monitoring model performance in production, and further refining the model.
Embedding refers to a technique used to represent discrete categorical data-such as words, sentences, or identifiers-in continuous vector spaces. They're essential for converting symbolic data into a form that can be processed by machine learning models, particularly neural networks.
Embeddings are widely used in natural language processing (NLP), recommendation systems, and various other AI applications. Storing data as vectors can reduce the time taken for inference.
The codebase, scripts, and notebooks used for data preprocessing, model training, evaluation, and deployment are stored for version control, collaboration, and reproducibility.
Documentation detailing the development process, model architecture, and usage-along with metadata describing the datasets and model configurations-is stored for reference, compliance, and knowledge sharing.
By systematically storing these elements, organizations can ensure the robustness, reproducibility, and continuous improvement of AI applications while adhering to best practices for data management and compliance.
Clearly, AI requires storing tons of data. So, a key piece of fast generative AI is quick access to large datasets. But deep learning algorithms such as large language models (LLMs) are too, well, large to run on memory alone. That's where stored data becomes necessary.
Several storage options cater to the diverse needs of AI. Evaluating these options involves considering factors such as performance, scalability, cost, and the specific requirements of the AI workload.
| Storage Type | Best For | Latency | Scalability | Cost |
|---|---|---|---|---|
| Object storage | Training data, model artifacts, datasets | Medium | Excellent | Low |
| Block storage | Databases, high-IOPS workloads | Low | Good | Medium |
| File storage | Shared datasets, legacy pipelines | Medium | Good | Medium |
| Vector storage | Embeddings, semantic search, RAG |
Object storage is a scalable, cost-effective solution ideal for storing large volumes of unstructured data. It offers high durability and availability, making it suitable for AI applications that require long-term data retention and access. Major providers include Amazon S3, , and .
Block storage provides low-latency, high-performance access to data, making it well-suited for AI workloads that demand rapid data processing. It's typically used for structured data and is ideal for applications like databases and file systems.
File storage offers a hierarchical structure for organizing and accessing data. It's versatile and can support both structured and unstructured data, making it a viable option for various AI applications.
Cloud storage solutions offer scalability, flexibility, and a pay-as-you-go pricing model. They provide a range of storage options, including object, block, and file storage, allowing organizations to choose the best fit for their AI workloads.
Implementing the right storage solution for AI involves adhering to best practices that optimize performance, scalability, and cost.
Understanding the specific requirements of your AI workload is fundamental. Assess the volume of data, the need for high throughput and low latency, and the scalability and availability requirements to select the most suitable storage solution for your application's needs.
As AI becomes key to business performance, a hybrid approach-which combines on-premise and cloud storage-can offer the best of both worlds. It allows organizations to experience the high performance of on-premise storage while benefiting from the scalability and flexibility of cloud storage. It also provides failovers to aid in disaster recovery.
Safeguarding data is paramount. Implement robust security measures, including encryption and access controls, and ensure compliance with data protection regulations to protect sensitive information.
Regularly monitor the performance of your storage infrastructure and optimize it to meet the changing demands of your AI applications. Proactive monitoring and optimization can prevent bottlenecks and ensure efficient functioning of your AI workloads.
Choosing the right storage solution for AI is a nuanced decision that involves evaluating various options and aligning them with the specific needs of your AI workloads. By understanding the storage demands of AI, assessing different storage options, and adhering to best practices, you can implement a storage infrastructure that optimally supports the growth and evolution of AI in your applications.
Navigating the multifaceted landscape of AI storage solutions can be overwhelming, but the rewards are worth the effort. By finding a solution that works for your AI application, you can position your business at the forefront of AI innovation by meticulously evaluating your AI application's unique requirements and aligning them with a storage solution that offers the optimal blend of speed, scalability, and adaptability.
But staying ahead of the curve involves more than embracing AI. You have to bolster your AI strategy with the right tools and infrastructure. The choice of storage solution you make today will shape the trajectory of your AI projects, influence the insights you glean, and determine the impact you make. It's a decision that calls for foresight, discernment, and a deep understanding of the symbiotic relationship between AI and storage.
Telnyx's Storage combined with our Inference tool helps you achieve that symbiosis in one platform, giving you end-to-end control over your AI products. Our low-cost, low-latency storage solution gives you quick access to affordable data storage. Equipped with our AI embed, you can vectorize that data in seconds to build and train fast iterative AI applications. With Inference, you can leverage that custom data in proprietary and open-source models or build your own on dedicated GPU infrastructure for fast inference at low costs.
Our competitive pricing for Storage and Inference makes our tools accessible to small teams and enterprise businesses. With pay-as-you-go structures, you only pay for what you need, allowing you to scale as your AI needs grow. And our award-winning customer service team is available 24/7 for any questions you might have.
Contact our team to learn how you can use Telnyx Storage and Inference to create next-gen AI applications for your business. Or try the solution for yourself by signing up for a free Telnyx account.
What is storage for AI?
AI storage is infrastructure tuned for machine learning and AI workloads, combining scalable capacity, high throughput, and strong durability. It typically spans object, block, file systems, and specialized vector indexes that serve different stages of the pipeline.
How much storage do AI projects require?
Many projects need terabytes of capacity and some grow to petabytes as data, versions, and model artifacts accumulate. A practical rule is to budget 2–3x your raw dataset size to cover preprocessing outputs, checkpoints, and backups.
Which storage type is best for AI workloads?
Object storage, such as S3-compatible object storage, fits large datasets and model artifacts thanks to flat namespaces, high durability, and cost efficiency. Use block storage for high-IOPS training scratch or databases, and shared file systems when POSIX semantics simplify legacy pipelines.
How should I choose the right storage for my AI needs?
Start from your workload profile, mapping dataset size, access patterns, latency tolerance, compliance, and budget to a mix of tiers and protocols. A structured decision checklist in this guide to choosing the right storage helps weigh object, block, and file options for training, inference, and archival.
How is vector storage used in AI?
Vector stores hold embedding vectors for semantic search, retrieval-augmented generation, and similarity matching, enabling low-latency lookups over unstructured content. Modern overviews of how vector storage works explain indexing methods, dimensionality, and the tradeoffs between recall and speed.
Is 32GB of RAM enough for AI?
For small datasets or classic ML, 32GB of system RAM can be adequate, but deep learning training often hinges on GPU VRAM and the ability to stream data efficiently from disk. Treat RAM, VRAM, and storage as separate bottlenecks, then use sharding, efficient data loaders, and on-the-fly preprocessing to stay within limits.
How can I scale to petabytes without runaway costs?
Use tiering across hot object buckets, colder archival classes, and lifecycle rules, and minimize data movement to cut egress and replication overhead. Patterns outlined for petabyte-scale cloud storage include compression, deduplication, multi-region placement, and granular retention policies.
| Low |
| Good |
| Varies |
