Enterprise Data Cloud: Managing Data, Not Just Storage
August 6 | Register Now Infrastructure teams have always managed applications. AI requires them to manage data. That's a different challenge, and most platforms weren't built for it. The Enterprise Data Cloud architecture from Everpure bridges that gap, bringing data intelligence into the same platform infrastructure teams already manage, so infrastructure and data teams are finally working from a single, unified view. In this session, we'll dig into what it means to manage a platform built for both operational performance and AI readiness, and the changes when infrastructure can finally gain visibility into its data, not just storage. Key takeaways: Why the shift from application-centric to data-first infrastructure changes how platforms need to be managed How Everpure brings data intelligence into the operational layer without adding tools, teams, or complexity What shared visibility across infrastructure and data teams actually unlocks for AI initiatives How to manage a platform that serves every workload, from archive to AI Register Now!
Claude Code as Database SRE: Catching What Your Monitoring Never Will with Everpure Fusion MCP
Your DR site might be quietly unprotected and no alert will tell you. That's the gap Anthony Nocentino, Principal Architect at Everpure, Microsoft Data Platform MVP, and self-described computer nerd set out to catch. He built a Database SRE agent using Claude Code and the Everpure Fusion MCP server to audit SQL Server fleets against compliance policy, uncovering a silently unprotected DR instance before disaster struck. Read the full report at "Using Claude Code as a Database SRE Agent with the Everpure Fusion MCP Server"
Fusion MCP Server Is Now Released & Open Source
There’s a narrow band between “AI demo” and “actually useful in production,” and most tools miss it by a country mile. Fusion MCP Server doesn’t. Now that it’s open source, MCP-compatible AI assistants get a controlled bridge into Everpure FlashArray and FlashBlade environments, one built to answer real operational questions about fleet inventory, capacity, performance, alerts, volumes, file systems, workloads, and presets, without turning your storage estate into a science fair project. The AI Agent doesn’t get to vibe its way through your infrastructure. It works through a clean tool surface backed by real Everpure APIs, and writes stay hidden until you flip them on yourself. What it actually solves If you’ve ever wanted to ask a storage question in plain English and get something better than a dashboard scavenger hunt, this is that. Fusion MCP Server sits between your AI agent and your arrays: the assistant talks to the server, the server authenticates with configured array API tokens, calls supported Everpure APIs, and returns structured results. Your assistant never touches the arrays directly. Practically, that means engineers can ask things like: Show me the fleet overview Which arrays have capacity concerns Show array performance for the last 24 hours List workload presets available in this fleet Show active alerts with remediation links The data was never the problem. Storage teams already have it. What eats the day is bouncing between menus, tabs, and API docs just to answer something like “which arrays are closest to full?” Think of it like swapping a pile of ad hoc curl commands and tribal knowledge for a typed interface your AI assistant can reason over. Calling it “screen scraping with confidence” undersells it. It behaves more like a junior SRE who actually reads the schema. Why engineers should care The release leads with reads, which is exactly the right default for infrastructure tooling. Out of the box, Fusion MCP Server covers fleet overview, capacity and performance, storage objects, configuration audit, and optional supervised actions for placement recommendations, preset creation and updates, and workload deployment. A few details stand out: It works with FlashArray and FlashBlade environments in a Fusion fleet, including mixed environments, as long as you provide at least one token for each platform type for Remote Execution. If the API version you have on your arrays does not yet have the endpoints with Remote Execution capability enabled, you must supply an API token for every array in the fleet. More on that in the next section. Fleet discovery covers the supported read workflows broadly, though some, especially performance, still need a direct token for each array you want to query. Built-in prompts handle fleet, performance, and config workflows, and it also works through plain natural-language questions if your agent doesn’t expose MCP prompts directly. Read-only endpoint documentation plus a whitelisted authenticated GET fetch tool cover supported API surface beyond the dedicated tools. Let’s talk Tokens first Back in Purity REST API version 2.38, Everpure started to include a capability for API endpoints called Remote Execution. This is the mechanism that lets a client invoke a Purity REST API request on a different fleet member, and that request executes as though it were initiated locally on that remote member. The catch is that both arrays must have the same API version available on them as well as the endpoint being executed against must have Remote Execution capability. As of today, not all endpoints have this capability, so there must be an API token specified for each array in the fleet until they have all been enabled. We are diligently working to get all endpoints enabled to make this easier for everyone. Stay tuned! Installation (that does not require a PhD) The setup flow is refreshingly direct: Download the latest binary from GitHub Releases or build from source. Generate API tokens for the arrays you want to query. Run generate-config with your FlashArray and/or FlashBlade targets. Drop the generated config into your MCP-capable agent using the standard start --auth-config pattern over STDIO. generate-config does more than write boilerplate. It validates tokens, detects each array’s API version, resolves array names, and writes the auth config with restrictive permissions: the config directory gets 0700 and the file gets 0600. Want an even easier way? How you just tell your AI Agent to “Read this repository at https://github.com/PureStorage-OpenConnect/fusion-mcp-server and the included USER_GUIDE.md file and add the Fusion MCP server to this agent.” Easy-peasy as it’ll step you thought the process and create the config file for you. A few caveats are worth flagging before you point this at anything that matters: The published binaries aren’t signed, so macOS and Windows may throw a warning on first run. Build from source if that’s a dealbreaker. It’s all there, have at it! Keep the generated auth-config.json local, don’t share it, and rotate tokens if one ever leaks. None of that is friction. It’s the fine print you’d want before trusting a tool with API tokens. Supervised write actions: powerful, optional, and very much not on by default Now for the part everyone asks about first, and the part some people should absolutely not enable first: write actions. Fusion MCP Server hides write tools by default. You turn them on explicitly, either during config generation with --enable-write-tools or later with update-config --enable-write-tools. Enabled, the supervised actions cover these processes with more to come as the product evolves: Placement recommendations Workload preset creation Workload preset updates Workload deployment The approval step is the clever bit. Write operations sit behind an explicit confirmation. If the agent supports MCP Elicitation, the server pops up an interactive dialog for every write tool, so you can review the proposed action and approve or decline before anything changes. If the agent doesn’t support Elicitation, it falls back to telling the agent, in plain instructions, to ask you for approval before resubmitting the call. One practical wrinkle: the write tools inherit whatever permissions live on the API tokens you configure, so the workflow only works if those tokens can perform the write. So when should you flip the switch? Enable write tools if you want supervised acceleration on repeatable workflows: placing a workload from a known preset, updating a policy-backed preset, or turning a natural-language request into a deployment action that still needs a human to sign off. Skip it if you’re still validating token scope, using the server mainly for observability, or introducing MCP to a team that hasn’t built trust in the read-only workflows yet. Start with read-only. Make it boring. Then decide whether supervised writes are the next move. Write tools toggle on and off with a simple update-config command, so this isn’t a one-way door: turn them off again anytime with update-config --enable-write-tools=false. Use cases that actually matter This release isn’t for people who like screenshots of AI chats. It’s for engineers and operators who want faster answers and safer workflows. A few obvious wins: Fleet triage Ask for a fleet overview with alerts, array inventory, Purity version, and fleet connections, the kind of first-response context you want before guessing which dashboard to open. Capacity and performance review Ask which configured arrays have the highest used capacity, which have high latency, or pull performance for the last 24 hours. For teams juggling multiple arrays, this turns routine health checks into a single conversation. Storage object lookup Query volumes by naming pattern, list file systems on FlashBlade, or inspect workloads on a specific array. Useful for anyone who inherited naming conventions from a previous geological era. Configuration audit Use the built-in documentation and read-only fetch coverage to compare settings across arrays and check for policy consistency. Handy if you’re trying to catch drift without hand-rolling an audit script every quarter. Workload lifecycle acceleration Enable supervised writes and the assistant can recommend placement, create or update presets, and deploy workloads from those presets. At that point the server stops acting like a reporting tool and starts acting like an interface layer for intent-driven operations. On My Soapbox: Why the Open Source release matters Being open source here changes the trust model, not just the distribution channel. You can inspect how the bridge works, check the security assumptions yourself, and contribute fixes instead of filing a ticket into the void. When reality disagrees with the documentation, which happens to every project sooner or later, you can open an issue instead of just living with it. The repository is public under Apache 2.0, with contribution guidance, architecture notes, a developer guide, and a dedicated issue tracker for support, issues, and feature requests. That means the people who’ll stress-test this in real environments can also be the ones fixing it. For a tool sitting between AI agents and production-adjacent storage workflows, that’s where the engineering conversation belongs. Final thought Fusion MCP Server is short on hype and long on mechanical sympathy. Read workflows stay front and center, write workflows require your explicit sign-off, and installation doesn’t eat your afternoon. If you’re running Fusion-managed fleets (which y’all should be!), it’s worth a look. Grab the latest release, point it at your MCP-capable agent, start read-only, and see how fast “show me my fleet overview” becomes second nature. It’s open source. Once you’ve kicked the tires, contribute code if you build something useful, and open an issue when you hit an edge case. That’s the whole deal.Purity Fusion Recipes
I have been loading up our fleet with a bunch of these predefined recipes and management have really enjoyed the information. I will say that most of these are very straight forward to setup but the Rubrik one was giving me troubles. I stumbled upon this link and it walked me everything perfectly. Almost all the recipes are listed here step by step and make this so easy. https://support.purestorage.com/bundle/m_pure1_manage_workflows/page/Pure1/Pure1_Manage/pure1_manage_workflows/topics/reference/recipe_template/protection/r_rubrik_cyber_resilience_visibility_template.htmlAnnouncing the Everpure Fusion™ Mastery Program
Looking for a practical way to build your Everpure Fusion™ expertise? We're excited to introduce the Everpure Fusion™ Mastery Program—a guided, self-paced program designed to help you get more value from Everpure Fusion while earning rewards along the way. Short learning activities are combined with hands-on technical tasks you can apply directly in your own environment. You'll build skills, gain confidence, and put Everpure Fusion to work in real-world scenarios. And the more of the program you complete, the more points you get to use on fun prizes. The program follows three stages: Activation Readiness Prepare your environment and successfully activate Everpure Fusion. Use & Optimization Apply Everpure Fusion to operational workflows, automation, and day-to-day management. Advocacy Share your expertise, contribute to the community, and help others on their journey to unified fleet management. Whether you're just getting started or already using Everpure Fusion, the program meets you where you are. Current users can even earn credit for work they've already completed while continuing to build deeper Everpure Fusion expertise. And because progress deserves recognition, you'll earn Everpure Fusion Points as you complete activities and milestones. Redeem your points for rewards while advancing your Fusion skills. Best of all, the program is designed for busy infrastructure teams. Activities are self-paced and manageable, allowing you to make progress whenever it fits your schedule. Build your skills. Put Everpure Fusion to work. Earn rewards. It’s really that simple. Ready to get started? Click hereGet rid of stressful infrastructure headaches. Everpure Fusion handles your data, autonomously.
It happens! It's 11:45 PM on a Friday. An alert fires and storage latency has spiked across a production workload. The operator digs in and traces it back to an automated tiering policy that quietly moved a hot dataset to a slower tier because it looked idle based on a 24-hour access window, right before a scheduled batch job that runs every weekend. Nobody changed anything. The policy did exactly what it was configured to do. But nobody remembers configuring it that way, the documentation hasn't been touched in two years, and the monitoring dashboard shows storage as "healthy" because utilization is fine. It's just in the wrong place. The operator overrides the tier, performance recovers, and spends the next hour writing an incident report for a problem that shouldn't exist. A system that was supposed to make life easier made a decision with no context, no warning, and no visibility into why. That's the anger that doesn't go away quickly. It's not just frustration at the incident. It's the feeling that the tools are working against you instead of with you. The core problem in that story was a system making decisions with no context, no warning, and no visibility. Everpure Fusion attacks each of those problems: Unified visibility across the entire fleet: Everpure Fusion provides a global dataset as a single source of truth for discovery, management, and configuration of storage arrays so the operator isn't piecing together what happened across multiple dashboards, multiple arrays, multiple tickets after the fact. They see the full picture in one place, before things go wrong. Intelligent workload placement: Rather than static policies quietly acting on stale access patterns, Everpure Fusion uses AI-guided placement to boost performance and efficiency for every workload. It understands workload behavior, not just utilization snapshots, the kind of context that would have caught a batch job pattern before tiering the dataset down. Policy-driven governance with real control: Automated orchestration cuts manual tasks and speeds service delivery, while unified controls simplify audits, reduce risk, and prove compliance fast. Policies are visible, documented, and governable. Not buried configs nobody remembers setting. Built into the platform, not bolted on: Evepure Fusion is now simply a part of Purity, meaning it is not an add-on you have to install or buy, but rather a core piece of the Purity operating system. The operator doesn't have to manage another tool. The intelligence is already there. The operator in that story didn't need more alerts. They needed a system that understood context, made decisions transparently, and gave them control without requiring them to be online at midnight to maintain it. That's exactly the gap Everpure Fusion is designed to close - with One Fleet, Zero Complexity. Why policy-driven storage operations matter Everpure Fusion is built as the core of Everpure intelligent control plane that manages all arrays including FlashArray, FlashBlade, and cloud as a unified fleet, with one topology, one API, and one operational framework regardless of protocol or local - datacenter, cloud or edge. That uniformity is what makes policy enforcement reliable at scale. Everpure Fusion introduces workload-based provisioning through presets, which are predefined policy-driven templates for specific workload types, encoding protection policies, replication, and SafeMode retention from the moment a workload is provisioned, not patched in after an incident. Admins no longer need to pre-plan and tune deployments manually, which reduces the risk of non-compliance and improves resiliency by ensuring workloads are provisioned correctly from the beginning. The result is infrastructure that enforces your intent, not just your last manual action. Intelligent placement, rebalancing, and fleet-scale capacity control If you manage storage at scale, you've probably seen this scenario play out more than once. One array is buried, running hot, and screaming for relief. Three aisles over, another array is sitting at 40% utilization, doing almost nothing. And somewhere in between, your team is scrambling to provision capacity, kick off an emergency migration, and explain to stakeholders why an SLA was missed on a workload that, in hindsight, never should have been placed there in the first place. This is not a people problem. It is a tooling problem. And it is remarkably common. Everpure Fusion starts solving this problem at the moment of provisioning. When a new workload lands, most storage systems do a simple capacity check and place it wherever space is available. Everpure Fusion does something fundamentally different. The placement engine evaluates every array in the fleet simultaneously, looking at IOPS headroom, throughput capacity, and physical utilization before making a decision. The goal is not just to find somewhere to put the workload. It is to find the right home for it, one where it can live comfortably for the long term without creating a bottleneck down the road. Think of it as placing workloads with intention rather than convenience. Of course, environments do not stay static. Workloads grow, usage patterns shift, and an array that looked healthy six months ago can become a problem today. Everpure Fusion accounts for this with continuous rebalancing built directly into its operation. When an array starts trending toward overload, Everpure Fusion detects it and begins orchestrating data movement across the fleet automatically. No manual intervention required. No application downtime. Data migrates in the background while workloads keep running, and arrays that were sitting underutilized suddenly become productive members of your infrastructure. At fleet scale, now supporting up to 64 arrays, this turns capacity management from a constant firefight into something that largely runs itself. What makes this possible without disruption is how Everpure Fusion executes the move under the hood. It leverages ActiveCluster to stretch the volume across both the source and target arrays simultaneously, creating a synchronous mirror in place. Once the stretch is established, volumes are connected on the target array and hosts auto-discover the new target paths through standard multipathing. The target then validates that path usage is healthy and confirmed before any cutover begins. Only after that validation is complete are the volumes disconnected from the source, ensuring there is zero gap in access at any point in the sequence. Everpure Fusion then unstretches from the source array to complete the rebalance and release its capacity. The result is a seamless, non-disruptive migration that the application never sees. What truly sets Everpure Fusion apart from a standard load balancer is what happens under the hood. Powered by Pure1 AI and up to 30 days of historical workload data, Everpure Fusion does not just look at what is happening right now. It looks at what is about to happen. Say you have a workload that runs a heavy batch job every Saturday night. Everpure Fusion knows that. It has seen the pattern. So when the placement engine is evaluating tier assignments, it will never recommend moving that workload to a lower-performance tier just because it looks quiet on a Tuesday afternoon. It understands what Tuesday quiet actually means in context. And if that workload somehow ends up on the wrong tier, perhaps through a manual change or a migration gone sideways, Everpure Fusion will proactively raise a violation before the weekend arrives. Not after the SLA is missed. Before. The cumulative effect is that customers can operate their fleets closer to full utilization without the anxiety that normally comes with it. Underused hardware gets activated, incremental purchases get deferred, and the reactive, always-behind-the-curve model of capacity management starts to look like a problem from a previous era. And Everpure Fusion does not stop at the infrastructure layer. Through its integration with Pure1 Application Intelligence, Everpure Fusion gains deeper visibility into the nature of the workloads themselves, not just how they behave, but what they actually are. That additional context means smarter decisions at every level, from initial placement to long-term tier management, grounded in a more complete picture of what your environment is really doing. Workload rebalance and mobility will be available towards the end of 2026. Compliance as part of the control plane Most storage compliance workflows follow the same pattern: an audit is announced, someone pulls reports from three different tools, cross-references configuration against a spreadsheet of expected settings, and spends two weeks proving that workloads are protected the way they're supposed to be. Then the audit ends and nothing changes until the next one. That model breaks at fleet scale. When you're managing dozens of arrays across multiple sites and protocols, manual audits don't just slow you down — they leave gaps that only get discovered at the worst possible time. Everpure Fusion Compliance is built into the control plane, not bolted on after provisioning. Because Everpure Fusion presets encode protection policies, replication requirements, SafeMode retention, and QoS settings at deployment time, Everpure Fusion always knows what every workload's intended configuration is. Drift detection is continuous — not periodic. When a workload deviates from its preset, Everpure Fusion instantly surfaces the violation — visible in the UI, queryable via API or CLI, and accessible to AI agents through an MCP server. Remediation can be triggered directly through the same interfaces, without pulling in a separate tool or writing a custom script. Fleet-wide compliance dashboards give storage admins a live view of posture across every array, with exportable audit-ready reports that don't require manual assembly. The shift is meaningful: compliance becomes a property of how the fleet operates, not a project that interrupts how the team works. Everpure Fusion Compliance will be available towards the end of 2026. From dashboards and scripts to natural-language fleet operations You know the drill. A latency spike hits production. You open three dashboards, run a handful of CLI queries, dig through alert logs, and piece together enough context to understand what happened — and by then, you've already spent 45 minutes on a problem that should have taken five. The issue isn't the tools. It's that the context your fleet holds is trapped across systems that don't talk to each other. Everpure Fusion MCP Server changes that. Built on the open Model Context Protocol standard, it connects any MCP-compatible AI assistant — Claude, ChatGPT, Copilot, or internal agents — directly to live Everpure Fusion fleet state. Arrays, workloads, capacity, performance metrics, alert history, configuration, and placement data are normalized into clean, structured JSON and made available to AI in real time, pulled directly from Everpure Fusion and Purity REST APIs. The result: instead of navigating dashboards and stitching together CLI output, you ask a question. "Which arrays are approaching capacity?" "What's driving latency on this workload?" "Which workloads are drifting from their preset?" Everpure Fusion MCP Server answers from live fleet context, not stale snapshots. This is the on-ramp to agentic storage operations. Everpure Fusion already enforces policy and placement across the fleet. Pure1 adds AI-driven analytics and recommendations on top. Together, they give infrastructure operators the foundation to move from reactive troubleshooting to intent-driven, increasingly autonomous fleet management. Using topology groups to encode real infrastructure boundaries If your Everpure Fusion fleet's topology model lives in a color-coded spreadsheet, three wikis, and the institutional memory of one senior admin who never takes vacation — this is for you. Everpure Fusion, built into Purity for FlashArray and FlashBlade, introduces Topology Groups: fleet-scoped objects that let you describe your arrays in the same language your architecture diagrams already use — regions, availability zones, datacenters, rows, racks. No more provisioning a Everpure Fusion workload and hoping it lands in the right building. A Everpure Fusion Topology Group is a hierarchical, tree-structured object. Groups nest up to 10 levels deep (global → us-east → az-us-east-1a → dc01 → row3 → rack12), each array belongs to exactly one parent, and cycles are rejected at write time. Critically, they encode placement semantics — not access control. RBAC stays in Pure1 Resource Groups; topology stays in Everpure Fusion topology. Once modeled, Everpure Fusion presets reference groups using <group>.arrays notation. Everpure Fusion intersects the preset's allowed arrays with the group's membership at placement time. If there's no overlap, Everpure Fusion provisioning fails fast with a clear error — not silently in the wrong zone. The Everpure Fusion CLI shorthand makes automation clean: purevol list --context az-us-east-1a.arrays Everpure Fusion membership changes propagate automatically across the fleet. You stop maintaining a second source of truth outside the control plane. Stop treating topology as tribal knowledge. With Everpure Fusion, make it a first-class part of the intelligent control plane. Extending the model to Kubernetes and virtualization Most infrastructure operators are managing two parallel storage worlds right now: traditional VMs and databases on one side, Kubernetes-based containerized workloads on the other. Separate toolchains. Separate provisioning workflows. Separate everything. Everpure Fusion changes that. Through the Portworx Fusion Controller, Everpure Fusion extends its policy and placement control plane directly into Kubernetes — without forcing developers to change their existing workflows. Everpure Fusion auto-discovers your FlashArray and FlashBlade fleet, then exposes Everpure Fusion presets as native Kubernetes StorageClasses. That means when a developer requests a persistent volume, Everpure Fusion's placement engine resolves it against your existing policy constraints — storage class, protection policy, topology group, replication requirements — the same way it does for any other Everpure Fusion workload. No separate control plane for modern environments. No array-by-array configuration for each cluster. New arrays added to the fleet are automatically discovered and configured, so the operational model stays consistent as infrastructure grows. For VMware environments, Everpure Fusion extends the same operational model through the Everpure Fusion vSphere plugin, connecting storage management directly into virtualization workflows instead of running it as a separate administrative domain. The result: one control plane, one set of policies, one placement engine — spanning VMs, containers, and databases across the fleet. That is fewer parallel stacks to operate, less configuration drift between environments, and a more scalable path to consistent storage operations across the full infrastructure stack. Everpure Fusion as the storage admin foundation for autonomous operations The through-line across everything covered in this blog is simple: Everpure Fusion gives infrastructure operators a unified, policy-driven control plane that enforces intent consistently — across provisioning, placement, compliance, topology, and now Kubernetes and virtualization. That foundation matters because autonomous storage operations do not start with AI. They start with structure. Topology groups encode where workloads belong. Presets encode how they should be configured. Everpure Fusion presets exposed as StorageClasses ensure Kubernetes environments follow the same rules as everything else. When that structure is in place, AI can recommend, optimize, and eventually act — because the context is already clean, trusted, and machine-readable. For storage admins, the shift is real: less time resolving incidents caused by placement decisions nobody remembers making, more time defining the intent that governs the fleet. Everpure Fusion is that foundation — built into Purity, not bolted on. Want to learn more about Everpure Fusion? Check out the following links to dive deeper: Join the Everpure Fusion Mastery Program to build expertise, complete hands-on activities and earn rewards. Sign up for a Fusion test drive to try it out on your own time. Check out more about Fusion product details. Watch our cool new Fusion demo videos. Read Everpure Fusion Datasheet
