Microsoft on Monday unveiled the Surface RTX Spark Dev Box, a compact desktop computer designed to let software developers run large AI models on their desks instead of paying for cloud computing — a move that directly challenges the per-token pricing model that has defined the AI industry’s economics since ChatGPT launched three and a half years ago.
The device, announced at Microsoft Build 2026, packs Nvidia’s new Blackwell-architecture RTX Spark processor and 128 gigabytes of unified memory into a small-form-factor chassis, delivering what Nvidia rates at one petaflop of AI compute. In practical terms, that means a developer can load, run and interact with AI models exceeding 120 billion parameters without sending a single API call to the cloud.
“These class of devices, we think, will get to about 100 billion parameter model running,” Pavan Davuluri, Microsoft’s executive vice president of Windows and Devices, said during a press briefing ahead of the event. He emphasized that raw model size is only part of the equation: “The model size is one thing, but for the model to be effective, it kind of needs to be able to have enough context, because a larger model, you feed it larger context.” At 100,000 tokens of context, he noted, the key-value cache alone can consume 40 to 50 gigabytes of memory — which is precisely why Microsoft and Nvidia engineered the device around a 128-gigabyte unified memory pool shared dynamically between the CPU and GPU.
The machine will be available later this year in the United States, sold exclusively through Microsoft.com. The company did not disclose pricing.
Why Microsoft is betting that AI’s future runs on fixed costs, not cloud meters
The Surface RTX Spark Dev Box arrives at a moment when the economics of AI development have become a boardroom-level concern. Companies large and small are grappling with cloud GPU bills that scale unpredictably: every fine-tuning run, every inference call, every agentic workflow that loops through a frontier model accumulates cost. For a developer iterating rapidly on a prototype — running the same model dozens or hundreds of times a day — those charges compound fast.
Microsoft is framing the Dev Box as a release valve for that pressure. Andrew Hill, corporate vice president of Surface, wrote in the announcement blog post that the device “changes that equation” by letting developers “reserve frontier model calls for truly frontier problems and handle the rest on their own hardware.” The pitch is not that cloud computing is obsolete, but that much of the work currently being sent to remote data centers does not require state-of-the-art models and would be better served by capable local hardware with predictable, fixed costs.
This is a significant strategic shift for Microsoft, a company that derives tens of billions of dollars in annual revenue from Azure cloud services. By selling hardware that explicitly reduces customers’ cloud dependency, Microsoft is acknowledging a tension that has been building across the industry: the marginal cost of AI inference at scale is unsustainable for many teams, and the market is demanding alternatives. The bet appears to be that developers who prototype locally will still deploy to Azure when they need to scale — and that owning both ends of that workflow is more valuable than owning only the cloud.
Inside the 128GB unified memory architecture that makes local AI possible
The technical architecture of the Dev Box reflects a set of deliberate engineering choices aimed at sustained, not peak, performance — a distinction that matters enormously for AI workloads that can run for hours.
At the center is Nvidia’s RTX Spark system-on-chip, which combines an ultra-efficient ARM-based CPU with a Blackwell-generation RTX GPU. In a traditional Windows PC, Davuluri explained during the briefing, this configuration would require four separate components: a CPU, a discrete GPU, dedicated graphics memory and system RAM. The RTX Spark collapses all of that into a single chip paired with a single unified memory pool.
That unification is the critical design decision. Conventional gaming laptops with high-end Nvidia GPUs top out at roughly 24 gigabytes of GPU-accessible memory. The Dev Box’s 128 gigabytes of unified memory — accessible to both the CPU and GPU through what Nvidia calls its Unified Memory Access architecture — is what makes it possible to load models that would otherwise require cloud GPU instances with specialty high-bandwidth memory configurations.
Microsoft did substantial work at the operating system level to exploit this architecture. The company implemented new memory management logic in Windows that raises the ceiling on how much system memory the GPU can address, introduces smarter page-size allocation for shared memory regions and ensures that heavy GPU workloads do not starve the CPU of the resources it needs for multitasking. The Windows scheduler was also optimized for RTX Spark’s heterogeneous core layout, routing demanding workloads to performance cores while keeping efficiency cores available for background tasks.
How a 3D-printed aluminum chassis doubles as a heatsink
The thermal design is equally deliberate. The Dev Box operates within an approximately 100-watt sustained thermal envelope — modest by desktop standards, but meaningful for a device intended to run training jobs and inference workloads continuously. The aluminum chassis itself is engineered to function as a passive heatsink, and the method Microsoft used to build it is among the most striking details about the machine.
The top panel is manufactured using metal 3D printing, a process that enables internal geometries too complex for conventional CNC machining or injection molding. The perforations are not simple through-holes; they are angled in multiple directions around the internal fan to optimize airflow from cold-air intake through heat dissipation. During the press briefing, Harry, a Surface industrial designer, explained the rationale: “The complexity is something other manufacturers wouldn’t be able to do, like CNC, or like any molding, because of the complexity of shape.”
When asked whether 3D printing would constrain mass production, the designer acknowledged the challenge but suggested Microsoft had developed a process robust enough to scale. The result is a machine that runs quietly enough for an open office while sustaining the kind of continuous GPU workloads that would throttle most conventional desktops of similar size. For a device that Microsoft expects developers to leave running overnight on fine-tuning jobs, quiet sustained performance is not a luxury — it is a requirement.
A developer-first setup that eliminates hours of configuration
Microsoft is shipping the Dev Box with Windows 11 Pro pre-configured at the image level for development work — a detail that sounds minor but reflects a growing recognition that the out-of-box experience for developer hardware has historically been poor.
The machine boots into a dark theme with a simplified taskbar, widgets removed and Do Not Disturb enabled. Developer Mode is turned on. PowerShell 7 is the default shell. WSL 2 — the Windows Subsystem for Linux — comes pre-installed with GPU passthrough and CUDA support already configured. Visual Studio Code, GitHub Copilot, Git, Python and Node.js are all installed and ready.
“We’ve said, ‘Hey, you know what, we got you, you want to go fast,'” a Microsoft engineer who demonstrated the configuration during the briefing told VentureBeat. The philosophy, he explained, is that developers were going to install all of these tools anyway — the friction was in the hours of setup and configuration that stood between unboxing a machine and writing the first line of code.
The Dev Box also ships with integration points across Microsoft’s AI stack: AI Toolkit for VS Code for model conversion and fine-tuning, Windows ML and Windows Copilot Runtime for local inference, and Microsoft Foundry for connecting local prototypes to cloud deployment pipelines. For enterprises, the device integrates with Entra ID and Intune for identity and device management, and includes Secured-core PC architecture, BitLocker encryption and Microsoft Defender.
Why Apple’s Mac Mini may not be the real competition anymore
The most obvious competitive comparison is Apple’s Mac Mini, which has dominated the compact-desktop category and has been widely adopted by developers drawn to Apple Silicon’s unified memory architecture and power efficiency.
Davuluri addressed the comparison directly during the briefing, saying the Dev Box is “in a different class of performance than Mac Minis, intentionally.” He declined to share specific benchmarks, noting that detailed specifications and performance targets would come closer to the fall launch. But the architectural advantage Microsoft is claiming is clear: while the current Mac Mini with M4 Pro tops out at 48 gigabytes of unified memory and the M4 Max configuration reaches 128 gigabytes, the RTX Spark Dev Box pairs its 128 gigabytes with a Blackwell-class GPU that has a fundamentally different CUDA-based compute model — one that the vast majority of the AI/ML ecosystem’s tooling (PyTorch, TensorRT, llama.cpp, Hugging Face frameworks) is already optimized for.
That CUDA ecosystem advantage is difficult to overstate. While Apple’s Metal framework has made progress, the overwhelming majority of AI training and inference frameworks are built and tested first against Nvidia’s CUDA stack. A developer running models on the Dev Box can use the same code, the same libraries and the same workflows they would use on a cloud GPU instance — a level of portability that Apple Silicon cannot currently match.
From laptop to supercomputer: Microsoft’s three-tier plan for local AI hardware
The Dev Box is one piece of a three-tier hardware strategy Microsoft laid out at Build. The Surface Laptop Ultra, announced days earlier at Computex, brings the same RTX Spark silicon into a 15-inch laptop form factor for developers and creators who need portability. At the other end of the spectrum, the DGX Station for Windows — built on Nvidia’s GB300 Grace Blackwell Ultra Superchip — targets organizations that need to run frontier models up to one trillion parameters on a deskside system. That machine is expected in the fourth quarter of this year.
The three devices map to a tiered computing model that Microsoft is calling “unmetered intelligence”: small on-device language models (the company’s new Aion 1.0 family) handle lightweight tasks at zero marginal cost; RTX Spark-class hardware runs mid-range models locally for the bulk of development work; and cloud resources are reserved for genuinely frontier-scale problems.
The GitHub Copilot CLI is getting a concrete implementation of this model with a new feature called /fleet, which allows a cloud-based primary agent to build a plan, assess the complexity of each task and route appropriate subtasks to a local model running on the developer’s hardware. The cloud agent handles what requires frontier capability; the local model handles what does not. The result, in theory, is lower cost without lower quality.
The real question is whether hybrid AI can shift from buzzword to business model
Whether Microsoft’s bet pays off depends on questions that will take months to answer. How does the Dev Box actually perform under sustained, real-world workloads? What will it cost? How quickly will the open-source model ecosystem continue to produce capable models in the 70-to-120-billion-parameter range that fit within its memory envelope? And perhaps most critically: will enterprise procurement teams, trained to think of AI as a cloud line item, accept a capital expenditure on desk hardware as an alternative?
The strategic logic, however, is difficult to dismiss. For three years, the AI industry has operated on an implicit assumption: serious AI work happens in the cloud, and the economics of that arrangement are simply the cost of doing business. Microsoft, a company with every incentive to reinforce that assumption, is now selling a machine that undermines it. That is not a contradiction — it is a recognition that the market is moving, and that the company that controls the developer’s local environment and the cloud they deploy to has a more durable advantage than one that controls only the cloud.
Every dollar a developer does not spend on cloud inference is a dollar that can fund another experiment, another iteration, another prototype. For years, the AI industry told developers they needed to rent their intelligence by the token. Microsoft is now asking a different question: what if you could just buy it?
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Zip, the AI procurement platform valued at $2.2 billion, announced two products on Monday that mark a turning point in its evolution from procurement software to autonomous AI platform: a suite of five AI “Superagents” that can review contracts, code invoices, and negotiate with vendors inside Zip’s governance framework, and a procurement-native implementation of the Model Context Protocol (MCP) that pipes Zip’s data directly into AI assistants like Claude and ChatGPT — without sacrificing audit trails or compliance controls.
The announcements, unveiled at Zip’s AI Summit in New York with speakers from Anthropic, OpenAI, Datadog, and Humana, arrive at a moment when the procurement technology sector has become one of the fiercest battlegrounds in enterprise AI. SAP unveiled its “Autonomous Enterprise” vision at Sapphire 2026 just weeks ago, introducing more than 50 domain-specific Joule Assistants across finance, supply chain, and procurement. Coupa launched its own Compose platform and Catalyst services bundle at Inspire 2026 in Las Vegas in May, an environment for building and orchestrating AI agents across procurement, along with a forward-deployed engineering services offering. And Gartner predicts 40% of enterprise applications will include task-specific AI agents by end of 2026, up from less than 5% today.
What makes Zip’s approach distinct — and what makes it a potentially important test case for the broader enterprise AI market — is not the agents themselves, but where they run and what constrains them.
Why procurement teams are uploading sensitive financial data into personal AI accounts
The announcement centers on an enterprise anxiety that procurement chiefs increasingly describe in private but rarely say publicly: their employees are already using AI for sensitive financial work, they’re just doing it in unmonitored, personal accounts.
Across the enterprise, employees are uploading spend data into Claude to analyze it, redlining sensitive contracts inside ChatGPT, and generating internal financial analyses in personal Gemini or Copilot accounts. Every time they do, sensitive enterprise data leaves systems where every action is controlled and audited, entering environments with no oversight, no compliance controls, and no record of what was done.
The consequences for getting this wrong are not hypothetical. SOX violations carry fines of up to $25 million. Executives can face prison time. Public companies that fail compliance audits can be delisted from the stock exchange. When an auditor asks how a decision was made six months later, no one can produce a record.
“After working with hundreds of enterprises — including the world’s leading AI companies — we’ve learned that this kind of work is already happening, with or without governance,” said Lu Cheng, Co-Founder and CTO at Zip. “Even the companies building AI themselves want this work governed.”
Zip’s CEO Rujul Zaparde put a finer point on it in an interview with VentureBeat, describing the competitive dynamics that make procurement an unusually high-stakes domain for AI governance. “Most enterprises don’t operate on a single procurement platform,” Zaparde said. “They’re running SAP as their ERP, Coupa for some sourcing, ServiceNow for IT requests, contract management tools for legal, risk and compliance platforms for vendor due diligence, and a long tail of point tools alongside them.”
He argued that this fragmentation gives Zip, as the orchestration layer connecting all of those systems, a unique advantage: “AI can only be as good as the data it has access to. Because Zip sits above all of these tools, with visibility into each, and orchestrates the entire procurement process from request to payment, its AI can take action across the full procurement workflow in ways point solutions cannot.”
Inside the five Superagents Zip built to automate procurement’s hardest bottlenecks
Zip is launching five Superagents, each targeting a specific pressure point in the procurement lifecycle. A Procurement Superagent unblocks stalled requests and manages tail-spend negotiation. A Legal Superagent reviews and redlines contracts against company-approved playbooks. An AP Superagent sorts, codes, matches, and routes invoices. A Config Superagent identifies workflow bottlenecks and drafts configuration changes for admin review. And an Intake Superagent guides employees through compliant request creation, routing purchases to the right buying channel and nudging toward preferred suppliers.
The five agents are not standalone services. Zip’s engineering blog reveals the architectural philosophy underlying them: all agents at Zip — pre-built and custom — run on a shared execution engine built within the company’s App Studio workflow automation platform. They differ only in configuration: the prompt that defines behavior, the tools they can access, and the format of their output. Zip’s engineering team describes this as a “Lego block” model — the out-of-the-box agents are finished models; custom agents are whatever enterprises choose to build from the same components.
Under the hood, the agent architecture uses a four-node LangGraph state graph — preprocessing, orchestration, final synthesis, and post-processing — that separates information gathering from response generation. The orchestration node contains a ReAct (Reason + Act) agent that autonomously decides which tools to call: document retrieval via vector search, structured API data from purchase requests and contracts, or company-specific policy context from a reference library.
This separation is deliberate. As Zip’s engineering team explains, conflating research and synthesis into a single LLM call would mean asking one model to be both a diligent researcher and an eloquent writer simultaneously. Separating them allows Zip to optimize each independently — including using different model tiers for each.
What differentiates Zip’s agents from the slew of procurement AI announcements from SAP, Coupa, and others is the governance architecture. Every Superagent action is governed by the same roles, permissions, and controls that apply to human employees. High-impact steps like system updates and approvals use deterministic logic rather than LLM inference. And every action generates a complete audit trail.
What happens when an AI agent misclassifies a $150,000 contract
Zaparde shared a specific error case from beta testing to illustrate how Zip’s human-in-the-loop design handles real-world failures. “Our Intake Superagent flagged a $150K marketing services contract as a standard SaaS subscription,” he said. “But because every Superagent action hits a human-in-the-loop checkpoint before it executes, the procurement team caught the misclassification before it went anywhere. They corrected the category, the right approvers were routed in, and the GL coding flowed through accurately downstream.”
The error-and-correction anecdote is revealing because it highlights the tension at the heart of every enterprise AI deployment: these systems will make mistakes, and the question is whether the surrounding infrastructure catches them before they cause damage.
Zaparde was direct when asked who bears liability if a Superagent triggers a compliance failure: “Customers remain accountable for their procurement decisions, the same way they would be with any vendor or business process. That’s standard across enterprise software. Payroll vendors don’t take on liability for misclassified employees, ERP vendors don’t take on liability for misstated financials, and the same principle applies to AI-augmented work.”
But he was equally emphatic that the design goal is to make the liability question moot. “Zip’s Superagents are designed so this scenario shouldn’t happen in the first place. They don’t operate outside governance, they operate inside it. Every action is auditable, every high-impact step is gated by human review, and the audit trail makes it possible to demonstrate compliant decision-making to auditors and regulators.”
The Superagents are currently in beta, with general availability expected this summer. Zip has been deploying AI agents in procurement since 2024, and today more than 50 are live across hundreds of enterprise customers. Northwestern Mutual alone saved 1,400 hours from a single AI agent. Superagents represent the next evolution — more reasoning, more cross-system action, more autonomy — all inside Zip’s governance layer.
When asked what percentage of agent actions require human escalation, Zaparde said there’s no single number because every agent handles a different type of task, but added: “In finance and procurement specifically, we deliberately err on the side of escalation any time a transaction touches risk thresholds, policy compliance, legal requirements, budget guardrails, or governance rules. That’s a deliberate design choice, not a limitation.”
How Zip’s procurement-native MCP could reshape where enterprise AI actually runs
The second announcement may prove more consequential for the broader enterprise AI market. Zip MCP is a vendor-hosted implementation of the Model Context Protocol — the open standard originally created by Anthropic in November 2024 and later donated to the Linux Foundation, with MCP SDK downloads reaching 97 million per month by March 2026, a 970x increase in 18 months.
A fundamental challenge has limited MCP’s enterprise adoption: organizations deploying MCP are running into a predictable set of problems — audit trails, SSO-integrated auth, gateway behavior, and configuration portability. The MCP protocol itself doesn’t yet natively solve for the governance requirements that regulated industries and compliance-sensitive functions like procurement demand.
Zip is attempting to solve this from the application layer. Its MCP server connects Zip’s procurement platform directly to any MCP-compatible AI assistant. An employee researching vendors in Claude, for instance, can have Zip proactively surface a request submission from that conversation. Power users can pull aggregated reporting across suppliers, requests, invoices, and payments from within a single AI conversation. Every action respects user permissions through OAuth, runs inside Zip’s compliance controls, and generates a complete audit trail. Zip claims this is the first time MCP has been implemented natively for enterprise procurement.
The claim matters because procurement is arguably the most governance-sensitive business function where MCP could deliver immediate value: it involves financial commitments, legal contracts, regulatory compliance, and supplier data that touch SOX, GDPR, and dozens of other regulatory frameworks.
When asked what happens to sensitive data once it reaches a third-party model’s context window, Zaparde was direct: “MCP is tied to an authenticated user, and the same role-based permissions that apply inside Zip apply through MCP as well — meaning MCP can only retrieve information the user is already authorized to see.” He added that Anthropic and OpenAI operate as Zip subprocessors, governed by data processing agreements with Zero Data Retention provisions, so “data flowing through MCP isn’t used for model training, and it’s protected by enterprise-grade controls at both ends of the connection.”
The companies building AI chose Zip instead of building their own procurement tools
Zip’s customer list for these announcements is impressive but still developing. Block, UCI Health, and Snowflake are the named launch customers for AI Spend Automation, the premium enterprise offering that bundles platform access, AI consumption credits, and Zip’s forward-deployed engineers.
UCI Health reported $20 million in cost avoidance from a single IT infrastructure project. Zaparde explained the methodology: “The $20 million came from a single IT infrastructure project at UCI Health where their procurement team used AI-powered benchmarking to enter vendor negotiations with real market data rather than internal assumptions alone.” He was careful to frame it as a collaborative result: “UCI Health’s procurement team did the negotiating and the AI gave them the benchmarks to do it well.”
Zip claims its broader customer base has saved more than $10 billion through its AI suite. Zaparde said that figure “includes direct cost reductions through better vendor negotiations, time savings from automating manual procurement workflows, risk reduction through avoided fines and compliance penalties, and indirect spend savings from improved renewal management.” A Forrester Total Economic Impact study modeled a 386% ROI for large enterprises using Zip, showing that on average, the platform pays for itself in under six months.
But the customer stories that matter most for Zip’s strategic narrative are its relationships with the companies whose models power its own agents. OpenAI has deployed more than 10 AI agents on Zip’s platform. Anthropic, whose Claude model Zip uses and whose engineers created MCP, more than doubled its procurement volume through Zip while keeping headcount flat.
The fact that both companies chose to buy rather than build is arguably Zip’s strongest competitive proof point: if the organizations with the most AI engineering talent on earth decided the procurement governance problem wasn’t worth solving internally, it suggests the moat is real. Beyond AI, the customer list spans T-Mobile, Dollar Tree, Canva, and Prudential — large, regulated enterprises where compliance failures carry material consequences.
“When the companies building AI choose Zip rather than build it themselves, that tells you something about the moat,” Zaparde said.
SAP, Coupa, and the intensifying AI arms race in enterprise procurement
Zip’s announcements don’t happen in a vacuum. The enterprise procurement AI market is experiencing a rapid convergence as every major platform races to embed agentic capabilities.
SAP has deployed more than 50 domain-specific Joule Assistants at Sapphire 2026, orchestrating a subset of over 200 specialized agents to execute precise tasks. SAP has even launched a Joule Agent in the SAP Ariba Intake Management solution that captures and routes procurement requests and connects to existing procurement systems — a move that reaches directly into Zip’s core territory. Coupa CEO Leagh Turner has argued her platform’s foundation sets it apart, saying that while others are “bolting AI onto aging systems,” Coupa has one platform that scales with governance. Coupa says it has deployed more than 20 specialized agents, and its $10 trillion dataset of historical transactions gives it a training data advantage that Zip cannot match.
Zaparde’s counter-argument rests squarely on Zip’s position as an orchestration layer rather than a point solution. “No matter how powerful those individual tools are, their AI is necessarily limited to the data inside each of their own systems,” he said. “Our moat is the orchestration layer and the AI agents built on top of it: agents that are uniquely able to reason and act across multiple systems and reconcile their data as a whole where needed.” He pointed to Zip’s recognition as a Leader in the first-ever IDC MarketScape for Spend Orchestration as evidence that the category itself has been validated.
The argument carries a strategic vulnerability, however, that Zaparde was asked about directly: Zip’s leading AI-company customers are also its model providers and potential competitors. What happens if Anthropic or OpenAI builds procurement tooling?
“The mistake is assuming procurement is fundamentally a model problem,” Zaparde responded. “Even if an LLM could perfectly understand a contract or negotiate with a vendor, it still needs to operate within company policies, approval chains, supplier relationships, ERP systems, and audit requirements. That context layer is what Zip has spent the past six years building. We see the model providers as accelerating what’s possible, while we focus on making that intelligence operational within the enterprise.”
Why Zip is trading SaaS margins for forward-deployed engineers and AI credits
The AI Spend Automation offering raises questions about Zip’s evolving business model. Bundling platform access, AI consumption credits, and forward-deployed engineers who build and deploy custom agents inside customer environments is a strikingly different margin profile than traditional SaaS — and it’s a model that Coupa, with its own new Catalyst services offering, is also now pursuing.
Zaparde was transparent about the tradeoff: “Yes, it is a different margin profile than pure SaaS, and we’re okay with that. Right now, our priority is adoption and proving value for customers. We believe that if we get the outcomes right, the economics follow. Companies that rush to protect margins before they’ve demonstrated real value end up with neither. We’re playing the long game.”
Zip is valued at $2.2 billion as of its October 2024 Series D round, the largest investment in procurement technology in over two decades. The company has raised approximately $371 million since its founding in 2020 and counts among its investors Y Combinator, BOND, DST Global, Tiger Global, and CRV.
The deepest technical signal in Monday’s announcement may be what it reveals about the infrastructure moat Zip is building beneath its agents. The company’s engineering team recently published detailed architecture for its internationalization system — a pipeline that uses LLM-based translation with glossary enforcement, Kafka change data capture, and a dedicated Redis caching cluster to translate user-generated content across multinational enterprise customers in real time.
The system uses a technique called “lazy persistence,” where translations are initially stored with a one-week TTL and only promoted to permanent storage when a user actually reads them. This kind of deeply procurement-specific infrastructure — designed to support AI agents that operate across languages, jurisdictions, and regulatory regimes — takes years to build, not quarters, and no general-purpose AI tool can replicate it with a better model alone.
The real product Zip is selling is the audit trail
The central question for Zip — and for every enterprise software company racing to embed agentic AI into regulated workflows — is whether governance-first AI agents will actually earn the trust of procurement teams that have spent decades building manual controls for very good reasons. The regulatory stakes are real: SOX fines, criminal liability for executives, stock exchange delisting for companies that fail compliance audits. When an auditor shows up and asks how a purchasing decision was made, someone has to produce a paper trail.
That is ultimately the bet Zip is making with Superagents and MCP. Not that AI can do procurement work — at this point, that’s table stakes — but that AI can do procurement work and leave a record that will satisfy an auditor two years from now. In a market flooded with companies promising autonomous agents, Zip is wagering that the most valuable thing an AI can produce isn’t a decision. It’s proof that the decision was made correctly.
Zip MCP and Zip Superagents are available in beta today, included with all core Zip products, with general availability expected this summer. Zip AI Spend Automation is available now for enterprise customers.
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