Microsoft to Discontinue Using Claude: Too Expensive, or a Calculated Move?

marsbitPublished on 2026-05-25Last updated on 2026-05-25

Abstract

Microsoft is discontinuing internal access to the AI coding assistant Claude Code for its engineers, citing high costs and transitioning them to its own GitHub Copilot CLI. Reports indicate that Claude Code's usage-based pricing model led to significant expenses, with estimates of $500-$2000 per engineer monthly, prompting cost reviews even at large firms like Uber. While Microsoft officially attributes the move to cost, analysis suggests it may also be a strategic move. Microsoft allowed internal use of Claude Code for six months to benchmark it against Copilot, gathering feedback to improve its own product. This "experiment" highlights deeper challenges: Microsoft lacks a competitive in-house general-purpose AI model, heavily relying on external partners like OpenAI and Anthropic. Claude Code has gained substantial market traction, surpassing Copilot in features like context window size and task completion rates, and is eroding Microsoft's control over the developer ecosystem. The shift signals broader industry concerns over AI pricing models and the strategic risks of dependency on external, rapidly evolving AI tools.

Who would have thought that Microsoft—the tech giant that has cumulatively invested over $10 billion in OpenAI—has recently suspended the internal use of Claude internally, also citing that it is "too expensive to use."

Here's what happened: Recently, an internal message circulated at Microsoft, stating that starting June 30, thousands of engineers responsible for work related to Windows, Microsoft 365, Teams, Outlook, and Surface will no longer be permitted to use Claude Code. Microsoft is directing them to switch to its own GitHub Copilot CLI.

Microsoft has not publicly disclosed the specific amount spent on Claude Code, but informed sources reveal that the suspension of Claude Code was indeed due to the excessively high costs, high enough to make even Microsoft feel the "pinch."

Not long ago, Uber made the same choice as Microsoft.

According to leaks, Claude Code costs Uber roughly $500 to $2000 per engineer per month in AI tool expenses.

What does this mean? For a hundred-person technical team, this single AI tool alone amounts to several million dollars per year. Uber's AI budget for 2026 was "burned through" by April.

Behind this lies a change many companies haven't yet grasped but are starting to find troublesome: the pricing model for AI is shifting from a previous "subscription plan" to a current "pay-per-use" model.

In the past, many AI tools adopted a fixed monthly fee model, with relatively predictable costs. But now, more and more AI assistants for programming scenarios are switching to a token-based billing method—the more complex the query, the more frequent the calls, the deeper the tasks, the higher the cost incurred. For technical teams that need to handle large volumes of coding work daily, this expense is rapidly swelling into a significant financial pressure that cannot be ignored.

Against this backdrop, even tech companies the size of Microsoft and Uber are forced to recalculate: Are the high costs of third-party AI tools truly worth it? Should they continue paying the ever-increasing bills, or switch to more economical open-source solutions, or use their own tools as replacements?

Microsoft's choice is clear: replace Claude Code with its own GitHub Copilot CLI. Although the feature experience might be slightly inferior, the costs are controllable, and internal resource flow is more efficient.

This choice sends a clear signal—AI pricing that even Microsoft finds "expensive" is forcing companies to re-examine their technology procurement strategies.

After all, the saved costs will ultimately be directly reflected in profits.

However, The Verge also points out that canceling the Claude Code license will not affect the Foundry agreement between Microsoft and Anthropic. This agreement includes an investment of up to $5 billion in Anthropic, providing Foundry customers with access to Claude models, and Anthropic's commitment to spend $30 billion on Azure computing capacity.

2 Was Letting Employees Use Claude Code Just an Experiment?

Microsoft's sudden revocation of Claude Code usage permissions for internal engineers came six months after allowing employees to use the tool, leading outsiders to view this not as a hasty ban, but rather a carefully arranged experiment.

According to an internal Microsoft memo, Rajesh Jha, Executive Vice President of the Experiences & Devices group, provided an explanation: "When we started offering both Copilot CLI and Claude Code, our goal was to learn quickly, benchmark these tools in real engineering workflows, and understand which tools best support our teams. Claude Code played a significant role in this learning process... At the same time, Copilot CLI brought us something particularly important: a product we can build directly with GitHub, tailored to Microsoft's codebases, workflows, security expectations, and engineering needs."

In other words, Microsoft proactively allowed a competitor's product into its engineering teams, exposing Copilot CLI's shortcomings using Claude Code. Then, over six months, they collected feedback, closed the gaps, and finally shut down the competitor's tool, migrating all engineers back to their own product.

On LinkedIn, a user summarized this strategy as: first let the competitor be the "sparring partner," then close the net after learning.

A LinkedIn user commented on this: "If Microsoft wanted to continue using Claude, cost would definitely not be a hindering factor. Microsoft's former Tokenmaxxing strategy seems like it was aimed at learning from the start."

Another user stated, "Using a competitor's product to stress-test your own requires strong discipline. And putting the learned knowledge into practice requires even more effort."

Looking at the results, Microsoft indeed did this. Copilot CLI underwent multiple iterations over the six months based on engineers' comparative usage feedback.

Therefore, this discontinuation is interpreted not as a passive abandonment due to "can't afford it," but as an active conclusion of this internal experiment after leveraging the competitor's strength to patch up its own weaknesses.

However, outside views on this are not unanimous. Some developers point out that Microsoft could do this because it simultaneously possesses underlying cloud infrastructure, its own code hosting platform GitHub, and a sufficiently large engineer base as "experimental samples." Most companies lack these conditions—they can only simply "not afford it," whereas Microsoft can choose to "learn and then stop."

3 Behind Halting Claude Code, Microsoft Faces Three Major Dilemmas

However, cost pressure and external speculation about an "experimental test" might only be the tip of the iceberg. Microsoft's decision to halt Claude Code touches upon a more unsettling fact for this software giant: in the industrial chain of the large model era, Microsoft is losing its power to define.

In March 2026, enterprise spend management platform Ramp released an AI Index report. Among enterprises purchasing AI services for the first time, Anthropic won about 70% of direct confrontations with OpenAI. This was completely opposite to the trend Ramp observed in 2025, when OpenAI's adoption rate surpassed any other model company. Anthropic's annualized revenue soared to $19 billion, closing in on OpenAI's $25 billion.

By April, according to Ramp, Anthropic's enterprise AI adoption rate had reached 34.4%, surpassing OpenAI's 32.3% for the first time, becoming the new top AI supplier in the enterprise market. The core engine driving this reversal was precisely Claude Code—this programming tool achieved $1 billion in annualized revenue within just six months of its release, accounting for 4% of all GitHub code commits at the time.

In this market shift, Microsoft occupied almost no significant position.

While Microsoft was forced to rely on external models from OpenAI and Anthropic, the annualized revenue of AI startups reached $80 billion in 2026, with OpenAI and Anthropic together capturing 89% of that.

This reveals a harsh reality: the commercial value of foundation models is flowing back to the model developers, while Microsoft—is just a distributor. When a distributor tries to block the source, it can only indicate one thing: it can no longer purchase a real ticket to the game.

Summarizing perhaps: Microsoft has been marginalized on three battlefields: models, developers, and ecosystem control.

Dilemma One: No Cutting-Edge Foundation Model, Heavy External Reliance

To this day, Microsoft's biggest hidden worry is that it still lacks a truly cutting-edge general-purpose large model of its own.

Since 2019, Microsoft has cumulatively invested over $13 billion in OpenAI, acquiring approximately 27% equity, but its self-developed large language models have never managed to match GPT-4 or Claude.

In April 2026, the three MAI series models released by Microsoft's AI research lab—MAI-Transcribe-1, MAI-Voice-1, and MAI-Image-2—only covered speech transcription, voice generation, and image creation, and did not launch a general-purpose large language model.

Although Microsoft possesses one of the world's strongest AI commercial entry points, it lacks the "foundation model control" that truly determines the upper limit of AI capabilities.

Lacking a self-developed general model, Microsoft cannot achieve a technological closed loop in core scenarios like general conversation and programming reasoning. Its core AI capabilities are tied to OpenAI. In April 2026, Microsoft and OpenAI jointly announced the end of their seven-year exclusive binding. Azure is no longer OpenAI's only cloud outlet, and IP licensing has shifted from exclusive to non-exclusive.

In the past, one of Microsoft's biggest moats was "exclusive access to OpenAI." But now, that exclusivity is loosening.

And once Microsoft loses its exclusive binding with OpenAI, it must face a brutal reality: it does not have an underlying model capable of replacing GPT-4 or Claude. This is why Microsoft today exhibits a very fragmented state in the AI field: it is one of the companies with the deepest AI commercialization globally, but simultaneously, it is highly reliant on external models for core capabilities. This "strong platform, weak model" structure is essentially a form of technological hollowing out.

Dilemma Two: Its Own Product Prowess Is Inferior to Competitors

What's even more disheartening for Microsoft is that not only does it lack a standout general-purpose large model, but even Copilot, which once captured the early intelligent programming window, is being substantively surpassed by Claude Code.

Over the past two years, GitHub Copilot has been synonymous with AI programming. But the AI programming market in 2026 has fundamentally changed. Where Claude Code truly revolutionized the industry is by turning a "code completion tool" into a "long-context engineering agent."

Traditional Copilot is more like: "help you write a few lines of code." Claude Code can directly participate in the entire software engineering process.

Inside Microsoft, the most popular programming tool is not its own Copilot, but Claude Code.

According to The Verge reporter Tom Warren, Microsoft engineers have "clearly preferred" Claude Code over their own tool in recent months. This preference does not stem from employees "not loving their own product," but from a substantive gap in product capability itself.

According to test data, Claude Code scored 80.8% on SWE-bench, while GitHub Copilot based on GPT-4o scored only 72.5%, a difference of 8.3 percentage points.

Claude Code supports a million-token context window, capable of processing about 3000 files in a single session, whereas Copilot CLI's limit is only 128K tokens. In scenarios involving refactoring or debugging across dozens of files, Claude Code's completion rate is 89%, while Copilot's is only 60%.

Engineers using Claude Code daily means development workflows, debugging data, and operational habits are all being deposited within Anthropic's ecosystem. According to The Verge, before opening Claude Code internally, 91% of Microsoft engineering teams used GitHub Copilot, but over the past six months, Claude Code usage has "severely eroded" this proportion.

Rajesh Jha, head of Microsoft's Experiences & Devices group, acknowledged in the memo that Claude Code was "an important part of the learning process," yet still mandated the switch. The root of this contradiction lies in a fundamental anxiety at the strategic level—when engineers entrust a key link in the development toolchain to an external product, Microsoft's control over its own technology stack is gradually weakening.

Long-term employee use of external tools means cultivating user habits for competitors, potentially taking development skills and process knowledge directly to competing companies in the future.

A Microsoft insider told The Information that Claude Code satisfaction among Microsoft engineers is as high as 91%. When a company's core developers are less satisfied with their own tools than with an external competitor's product, its technological cohesion faces a major challenge. This is not about "fearing the competitor making money," but worrying about the development culture being infiltrated by external tools, leading to core talent and development processes being locked into a competitor's product.

Dilemma Three: Weakening Ecosystem Control

What makes Microsoft even more uncomfortable is: not only are internal engineers shifting to Claude, but the entire enterprise market is also beginning to show a similar trend.

Microsoft has invested in its two main partners, OpenAI and Anthropic, but both are gradually shedding their dependence on Microsoft.

According to Ramp's AI Index data, in April 2026, Anthropic's enterprise-paid adoption rate reached 34.4%, surpassing OpenAI's 32.3% for the first time. Over the past 12 months, Anthropic's enterprise adoption rate surged from just 9% to 34.4%, a near four-fold increase, while OpenAI's enterprise adoption rate grew only 0.3% over the same period.

When enterprises purchased AI services for the first time in 2026, in about 70% of direct confrontations, the final contract went to Claude, not ChatGPT.

The core engine driving this reversal is precisely Claude Code.

According to market estimates, approximately 4% of global GitHub public commits involve participation by Claude Code, and Anthropic expects this to exceed 20% by the end of 2026. Claude Code holds 54% share of the AI programming tools market, with 8 of the world's top ten Fortune companies as paying customers. In terms of annualized revenue, Claude Code surpassed $1 billion in November 2025, reaching $2.5 billion by early 2026. In comparison, OpenAI's Codex annualized revenue is about $1 billion.

According to Goldman Sachs statistics, the total annualized revenue of AI startups in 2026 is about $80 billion, with OpenAI around $25 billion, Anthropic around $19 billion, the two combined accounting for 89%. When Claude Code's revenue is captured by Anthropic and not Microsoft, the role Microsoft essentially plays is still that of a distributor—providing computing power and some investment, yet failing to capture the thickest profits from the core value of large models.

In April 2026, Amazon and OpenAI reached a strategic partnership, with Amazon committing to invest up to $50 billion, and AWS also becoming the exclusive third-party cloud distributor for OpenAI's enterprise-level platform Frontier.

According to Business Insider, internal Microsoft assessments show GitHub Copilot's share in the AI programming tools market has fallen to about 25%.

These data points mean: AI competition is shifting from "chatbots" to "engineering systems."

And in this round of competition, Claude Code is becoming the new infrastructure entry point. The problem is—Microsoft was supposed to be the biggest beneficiary of this AI programming revolution. Because GitHub already holds the world's largest developer ecosystem.

But now Claude Code captures developer mindshare, Anthropic takes enterprise growth, OpenAI gradually detaches from Microsoft's exclusive system, and more frighteningly, GitHub Copilot is starting to be marginalized.

Microsoft suddenly realizes: it owns GitHub, but may not own the next-generation AI programming ecosystem.

4 One Misstep Leads to Another

The problem Microsoft faces today is no longer as simple as a single product lagging.

On the surface, this is just a management action of "internally discontinuing Claude Code." But looking deeper, one finds behind it an entire chain that is spiraling out of control.

In the beginning, it was Microsoft's delay in creating a truly self-developed general-purpose large model capable of matching GPT-4 or Claude. After lacking foundational model capabilities, it could only long-term rely on OpenAI for core AI capabilities. But the problem is, OpenAI is now also gradually shedding its exclusive binding with Microsoft. Their relationship has shifted from "deep binding" to "cooperative but non-exclusive."

On the other side, something even more dangerous is happening inside Microsoft.

More and more Microsoft engineers are starting to use Claude Code daily, instead of their own Copilot. Superficially, it's just a choice of development tool, but in reality, it affects the entire development ecosystem: code workflows, debugging habits, engineering context, Agent usage patterns—all migrate along with the tool. For a platform company, the scariest thing is never that competitors make money, but that its own developers start working within a competitor's ecosystem.

Next, the problem begins to transmit further.

After developers shift en masse to Claude Code, the real money is made by Anthropic. Enterprise clients also start migrating, and Claude's voice in the AI programming field rapidly expands. Although Microsoft can still make money by providing computing power through Azure, the portion of value with the thickest profits and strongest control in the AI era is being taken by model companies and Agent platforms.

In this situation, a subtle state begins to emerge within Microsoft: products cannot compete, but they cannot continue allowing employees to fully defect to external tools. So the final approach taken is not to first catch up with Copilot's capabilities, but to first suspend internal usage permissions for Claude Code.

This actually shows that the problem is evolving from "product competition" to "organizational defense."

According to The Verge, Microsoft even once considered acquiring Cursor to bridge the gap in AI programming experience for Copilot, but later did not proceed due to factors like regulatory risks.

To some extent, this precisely exposes Microsoft's most awkward position now: it possesses one of the world's strongest developer platforms and the most extensive enterprise customer system, but the most critical entry point in the AI programming era—the Agent tool developers actually use daily—is falling into others' hands.

And once developer habits, workflows, and engineering ecosystems are all re-established, trying to reclaim them later is no longer as simple as adding a few features or changing a product strategy once.

References:

https://ramp.com/leading-indicators/ai-index-may-2026?utm_source=chatgpt.com

https://tech.yahoo.com/ai/copilot/articles/microsoft-ditching-claude-code-copilot-133318848.htmlhttps://fortune.com/2026/05/22/microsoft-ai-cost-problem-tokens-agents/https://www.linkedin.com/posts/matthew-johnson-71a059b3_microsoft-gave-claude-code-to-thousands-of-activity-7462552767300272128-b0dx/

This article is from the WeChat public account "InfoQ" (ID: infoqchina), author: Dongmei.

Related Questions

QWhat is the main reason Microsoft is discontinuing internal use of Claude Code, according to the article?

AThe primary reason, as stated in the article, is the high cost of using Claude Code. The token-based pricing model led to expenses so significant that even Microsoft found them unsustainable. It's also suggested this may have been a planned experiment to benchmark and improve their own GitHub Copilot CLI.

QHow did Claude Code reportedly outperform GitHub Copilot in terms of technical capabilities?

AAccording to the article, Claude Code outperformed GitHub Copilot in several key areas: it achieved a score of 80.8% on SWE-bench compared to Copilot's 72.5%; it supported a 1 million token context window (vs. Copilot's 128K), allowing it to handle around 3000 files in one session; and it had an 89% completion rate for complex tasks like refactoring across dozens of files, versus 60% for Copilot.

QWhat strategic dilemma does Microsoft face regarding its AI model development, as described in the article?

AThe article describes Microsoft's key strategic dilemma as a lack of a proprietary, state-of-the-art general-purpose large language model (like GPT-4 or Claude). This makes it heavily reliant on external partners (OpenAI, Anthropic) for core AI capabilities, leading to a "platform strong, model weak" structure and a loss of control over the foundational technology and its associated profits in the AI value chain.

QWhat market shift does the success of Claude Code represent, according to the article's analysis?

AThe article states that Claude Code's success represents a market shift where AI competition is moving from 'chatbots' to 'engineering systems.' Claude Code transformed from a simple code completion tool into a 'long-context engineering agent' that can participate in entire software workflows, thereby capturing developer workflows and becoming a new infrastructure entry point in the AI programming ecosystem.

QBeyond cost, what internal risk did Microsoft face by allowing its engineers to use Claude Code extensively?

AThe internal risk was the potential erosion of Microsoft's control over its own developer ecosystem. As engineers adopted Claude Code, their development workflows, habits, and contextual knowledge became tied to a competitor's (Anthropic's) product. This threatened Microsoft's technical cohesion and risked having its core talent and processes 'locked' into a rival's platform, weakening its long-term strategic position.

Related Reads

The Hottest 00s Generation on Wall Street

"Wall Street's Hottest '00s Phenom: The 25-Year-Old Fund Manager Who Bet on AI's 'Boring' Backbone" At just 25, Leopold Aschenbrenner, once fired by OpenAI, now runs a hedge fund worth $13.7 billion. His strategy? Betting against the consensus. While others chased AI chips, he invested early in the physical infrastructure powering the AI boom: electricity, data centers, and energy. Expelled from OpenAI's safety team in 2024, Aschenbrenner foresaw the coming bottleneck. He argued that AI progress would be limited not by algorithms, but by power, chip capacity, and space. Acting on this, he founded Situational Awareness LP to go long on these "old economy" assets. His bets have paid off spectacularly. His fund's assets soared from $255 million in late 2024 to $13.7 billion by Q1 2026. His portfolio is a direct reflection of his thesis: major long positions in fuel cell company Bloom Energy and data center/bitcoin mining firms like CleanSpark and Riot Platforms, which control critical land and power resources. Conversely, he holds massive put options against overheated semiconductor giants like NVIDIA and AMD. A notable exception was his bullish bet on storage company SanDisk, which surged ~160% in Q2. Aschenbrenner's vision is materializing. Tech giants like Amazon, Alphabet, and Meta are ramping up colossal capital expenditure on data centers. Global data center power consumption is projected to skyrocket, with AI accounting for over half by 2030. The demand for enabling technologies like optical fiber and modules is also exploding. His story underscores a fundamental truth of the AI era: the ethereal intelligence of algorithms rests on a very physical, heavy, and power-hungry foundation. The future is being built not just in code, but in concrete, copper, and kilowatts.

marsbit18m ago

The Hottest 00s Generation on Wall Street

marsbit18m ago

Review of Cathie Wood's Masterstroke Operation on Circle

A Recap of Cathie Wood's Masterful Trading in Circle's IPO This article analyzes the strategic moves made by ARK Invest's Cathie Wood around the IPO of Circle (CRCL). Despite her typical long-term, narrative-driven investment style, Wood executed a textbook "buy low, sell high" trade. Wood secured a core position of approximately 4.49 million shares at the $31 IPO price. The stock debuted at $69, surged to a high of $299 in June 2025 fueled by stablecoin regulatory news (the GENIUS Act), and then entered a prolonged decline. During this rally, ARK systematically sold around 1.7 million shares at an average price near $210, driven partly by internal fund rebalancing rules triggered by the stock's soaring weight. This move locked in substantial profits. As the stock later fell due to lockup expirations, new share issuance, and interest rate concerns—even dipping below $50—Wood began repurchasing shares. Starting in November 2025 around $86, she continued buying on the way down, eventually rebuilding her position to roughly the original size by Q1 2026. Key takeaways include: 1) Having a strong, independent long-term thesis (viewing Circle as critical digital dollar infrastructure). 2) Trading in tranches instead of trying to time exact tops or bottoms. 3) Maintaining strict position-sizing discipline, using rules to force profit-taking and preserve buying power. For most retail investors, chasing the dramatic "pop" at open is dangerous, as the subsequent 83% drawdown showed. Wood's success hinged on pre-IPO access, a clear investment thesis, and disciplined execution.

marsbit1h ago

Review of Cathie Wood's Masterstroke Operation on Circle

marsbit1h ago

Sharplink CEO: Ethereum's Future is Unfolding Now

In an article titled "Sharplink CEO: Ethereum's Future is Unfolding," Joseph Chalom, a former BlackRock executive and current Sharplink CEO, argues that the current debates surrounding the Ethereum Foundation (EF) and ETH price miss the bigger picture. He asserts that Ethereum's long-term institutional adoption is secured by its foundational strengths: trust, security, and liquidity. Chalom highlights Ethereum's dominance in settling stablecoin value, tokenizing real-world assets (RWA), and facilitating high-value DeFi transactions as evidence of its winning position. He defends the Ethereum Foundation's focus on rigorous protocol development and a decade-long track record of major upgrades (The Merge, EIP-1559, Dencun, etc.), viewing its upcoming technical roadmap as the most ambitious in the industry. Contrary to critics, Chalom posits that Ethereum's decentralization and reliable neutrality are core strengths for institutional adoption, not weaknesses, as they prevent control by any single entity. Drawing a parallel to Amazon's early days, he suggests that ETH's intrinsic value is tied to the expansion of its network, which is poised for a step-change in transaction volume across stablecoins, RWAs, DeFi, and agentic finance. Chalom advocates for a "be greedy when others are fearful" approach, citing historical examples from Warren Buffett and his own experience at BlackRock during the crypto winter. He concludes that while the EF should remain focused on core protocol attributes (CROPS: Censorship Resistance, Capture Resistance, Open Source, Privacy, Security), there is a leadership gap in market outreach. Chalom calls for ecosystem participants, including Sharplink and other key players, to become more vocal advocates to support the coming institutional adoption supercycle, asserting that "Ethereum's future is unfolding now."

marsbit1h ago

Sharplink CEO: Ethereum's Future is Unfolding Now

marsbit1h ago

6 Questions to Understand the Business Trends of AI

The AI industry has entered its "summer" phase, according to a six-dimensional scoring framework assessing its development cycle. Each dimension—narrative vs. delivery, system connectivity, delivery capability, ROI rationalization, common industry trends, and capital environment—scores 1 point, totaling 6 points. This places the industry firmly in summer (5-7 points), characterized by a coexistence of grand promises and tangible deliverables, with increasing pressure to demonstrate value and profitability. Key signals mark this shift. ByteDance's Doubao launched paid subscriptions, while OpenAI introduced an advertising platform. These moves are driven by dual forces: immense cost pressures from scaling user bases and massive compute requirements, and the maturation of commercial opportunities. Major players like Anthropic report explosive growth, highlighting AI's transition into core productivity infrastructure. For businesses, the path forward involves three strategic steps. First, identify a small, high-impact use case to quickly demonstrate a closed-loop value proposition, such as automating customer service or content generation. Second, systematically replicate successful pilots across the organization by standardizing processes, building shared AI capabilities, and aligning talent, incentives, and leadership. Finally, move beyond simply adding AI to existing workflows and undertake systemic reconstruction—redesigning processes for parallel AI-human collaboration, implementing real-time dashboards, and establishing automated trigger chains. The era where storytelling alone secured funding is over. The focus has shifted to delivering measurable efficiency gains, cost savings, and new revenue streams, as evidenced by real-world implementations in companies like Semir, Anta, and Midea. Success now depends on starting with a focused proof point, scaling it organization-wide, and ultimately allowing AI to redefine operational paradigms.

marsbit7h ago

6 Questions to Understand the Business Trends of AI

marsbit7h ago

Trading

Spot
Futures

Hot Articles

How to Buy MOVE

Welcome to HTX.com! We've made purchasing Movement (MOVE) simple and convenient. Follow our step-by-step guide to embark on your crypto journey.Step 1: Create Your HTX AccountUse your email or phone number to sign up for a free account on HTX. Experience a hassle-free registration journey and unlock all features.Get My AccountStep 2: Go to Buy Crypto and Choose Your Payment MethodCredit/Debit Card: Use your Visa or Mastercard to buy Movement (MOVE) instantly.Balance: Use funds from your HTX account balance to trade seamlessly.Third Parties: We've added popular payment methods such as Google Pay and Apple Pay to enhance convenience.P2P: Trade directly with other users on HTX.Over-the-Counter (OTC): We offer tailor-made services and competitive exchange rates for traders.Step 3: Store Your Movement (MOVE)After purchasing your Movement (MOVE), store it in your HTX account. Alternatively, you can send it elsewhere via blockchain transfer or use it to trade other cryptocurrencies.Step 4: Trade Movement (MOVE)Easily trade Movement (MOVE) on HTX's spot market. Simply access your account, select your trading pair, execute your trades, and monitor in real-time. We offer a user-friendly experience for both beginners and seasoned traders.

4.3k Total ViewsPublished 2024.12.10Updated 2025.03.21

How to Buy MOVE

Discussions

Welcome to the HTX Community. Here, you can stay informed about the latest platform developments and gain access to professional market insights. Users' opinions on the price of MOVE (MOVE) are presented below.

活动图片