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Q3 2026: A quarter of steady infrastructure. StorX Network continued to grow with: 🔹 2,000+ independent node operators 🔹 5+ PB of stored data 🔹 117,000+ users 🔹 70+ integrations 🔹 Continuous node payouts since 2021 This quarter we looked at how the network works, where it stands today, and what we’re building next. Read the full Q3 Newsletter 👇 🔗 medium.com/storx-network/5d3… #StorX #DePIN #DecentralizedStorage

Agent memory is turning storage into a latency problem, not just a capacity one. Fast reads matter, but so does knowing where the data physically lives and who can switch it off. Both belong in the spec. #SRX #DePIN

Capacity was never the scarce thing in decentralized storage. Recurring, real workloads are. Good to see demand-side plays like this, because they help the whole category. For us: 2,000+ nodes, 5+ PB, 117,000+ users, and $SRX burns tied to actual usage. #DePIN #DecentralizedStorage

Mumbai, we’re joining the conversation. 🚀 StorX Network is proud to be an Ecosystem Partner alongside @lawblockstoken and @XDCNetwork for this exclusive #XDCNetwork event bringing together AI, Quantum and Web3. Looking forward to connecting with builders, influencers, ecosystem partners and the wider Web3 community to explore what’s next. 📍 Goregaon East, Mumbai 🕔 5 PM onwards Join us 👇 luma.com/0qwpaxre #StorX #XDC #AI #Quantum #Web3

Node check-in. A newer operator asked what happens if they want to leave. The answer is unremarkable: unstake, wait out the period, take the hardware back. Their file pieces get rebuilt elsewhere by the repair process before they go. Nobody is trapped in this network. It works because staying is worth it, not because leaving is hard.

A fair objection we get: Cloudflare R2, Backblaze B2 and Wasabi also do not charge egress fees, and they are centralised, simpler to adopt, and involve no token at all. If zero egress is the only reason to move, they are a shorter path. Our answer is not that they are wrong. It is that zero egress and distributed architecture are separate properties, and you can have one without the other. If what you want is a lower bill, several roads get you there. If you also want no single operator holding complete access to your files, that narrows it considerably. Pick based on the property you actually need.

Node issues happen. Knowing how to recover matters. If your StorX node has been disqualified or isn’t running after an upgrade, we’ve put together a step-by-step recovery guide covering: • Disqualified node recovery • Docker cleanup • Fresh bootstrap & identity creation • Node restart after upgrades • Configuration & verification Get your node back on track 👇 medium.com/storx-network/bfa…

Hosting rewards on the network reduce on a schedule. Operators who joined in 2021 earned more per terabyte than operators joining now, and operators joining next year will earn less than operators joining today. This is deliberate. A reward that never decreases requires either infinite token supply or infinite demand growth, and neither of those exists. Declining issuance is what makes a payout schedule survivable across a decade rather than a cycle. If you are modelling node returns, model the decline. Anyone showing you a flat projection is showing you a chart, not a plan. #SRX #DePIN

The advice that keeps surfacing from teams who have actually done this: keep hot, active data on centralised infrastructure where latency matters, and move cold data — backups, archives, media, historical datasets — to a distributed network where cost matters. That is not a compromise position we have been backed into. It is the correct architecture. Most of what an organisation stores is cold, and cold data is where a pricing difference compounds. We would rather hold a large share of the workload we are right for than a small share of everything.

The most common criticism of decentralised storage is latency, and the criticism is correct. Reaching multiple independent nodes and reconstructing a file takes longer than reading from a single nearby data centre. Milliseconds against seconds, depending on conditions. For a database backing a live application, that is disqualifying. For backup, archive, media libraries and datasets, it is not — those workloads were never latency-sensitive to begin with. The honest position is not that the criticism is wrong. It is that it applies to a category of workload we are not asking for.

Node check-in. Backup traffic has a shape. It arrives in windows — overnight, weekends — and the network sees roughly the same pattern every week. Operators experience it as bursts of write activity followed by quiet. Nothing to manage. It is simply what a network serving business backup looks like from the inside.

Our Google Drive backup and restore integration went through Google's verification process. Worth being precise about what that means: the integration meets Google's requirements for accessing Workspace data on a customer's behalf. It does not mean Google endorses decentralised storage. It means the connector was reviewed and does what it says it does. Small distinction, but the kind worth making. Inflating a verification into an endorsement is how trust gets spent. #SRX #StorX #googledrive #googleworkspace #backup

Most storage pilots fail on the wrong evidence. A team uploads a few gigabytes, downloads them, confirms it works, and then cannot answer the question their director actually asks: what happens at month eighteen, at real volume, when somebody needs a file back in a hurry. A pilot that answers the first question takes an afternoon. A pilot that answers the second one runs a real workload, at real size, for long enough to see a bad week. We would rather you run the second kind, even though it is slower to a decision. #SRX #DePIN

Enterprises do not adopt storage protocols. They adopt backup software, and the storage ends up wherever that software is pointed. Which is why the integrations that matter are the ones inside tools teams already run — Acronis, Veeam, ICONIK, Workspace backup. The decision stops being "should we move to decentralised storage" and becomes "which target do we select." A protocol nobody's tooling supports is a research project. The integration work is the adoption work. #SRX #DePIN #BuiltonXDC

Node check-in. Retrieval volume was up this week, mostly larger objects. A handful of nodes on slower connections dropped out of the fastest reconstruction sets. Files still assembled. That is how the selection is meant to work. Slower nodes are not penalised for existing. They are just not the ones the client reaches first.

Writing to a distributed network is straightforward. Reading is where the architecture actually gets tested, because the client has to reach enough independent nodes, fast enough, to reconstruct the file. For archival, backup and dataset retrieval, seconds are fine and the economics are clearly better. For workloads that need consistent sub-50ms reads, they are not, and we would rather say that now than have you discover it during a pilot. The right test is your workload against our endpoint. Not our number against someone else's.

If your compute and your storage come from the same provider, you get convenience and you get a single dependency. Migrating later means moving both at once, which is a large part of why most teams never do. Separating them costs something up front — two vendors, two bills, two sets of credentials. What it buys is the ability to change one without changing the other. Whether that is worth paying for depends entirely on how likely you think it is that you will ever want to change either one.

GPU marketplaces and inference providers need somewhere to stage datasets and park model artifacts that is not the same vendor running the compute. Coupling the two means one procurement decision governs both. Our S3-compatible endpoint means a pipeline that already writes checkpoints to object storage can write them to us instead by changing a URL and a set of credentials. No rewrite, no new SDK. That is the whole integration.

Training a model is a compute story. Almost everything around it is a storage story. Datasets have to be staged before the run and kept afterwards for reproducibility. Checkpoints are written throughout. Outputs, evaluations and logs accumulate for as long as anyone might need to audit what the model did. Most of that data is written once and read rarely, but it cannot be deleted. That is the workload profile distributed storage is genuinely suited to — large, durable, infrequently accessed, and expensive to hold anywhere else. #SRX #DEPIN

Hourly onchain proofs are the right instinct. Storage you can verify beats storage you have to trust. The next question is who the providers are and where they sit. Verification gets stronger when the network is also independent and spread out. That's the thinking behind #StorX: 2,000+ nodes across 50+ countries on #XDC.

Node check-in. A few operators mentioned drive prices this week. Some are holding off on expansion until the market settles. Others are running what they already have and adding nothing new. Capacity growth is slower than it was in spring. Availability is unaffected. The drives already in the network do not care what new ones cost.