Everyone can build it. Almost no one can afford to run it at scale. And the companies selling the picks and shovels are about to get undercut by the same forces they unleashed.
by VEKTOR Memory — 20 min read
How This Article Started: 20 Forums, 40 Headlines, and a Growing Sense That Everyone Was Confused
I woke up to clear skies and the sun finally shining, and I set out to understand this idea, the truth behind it, and the nagging suspicion that the narrative around AI and software costs had become so loud, so uniform, and so confidently confusing that someone needed to sit down and actually go through it.
No tweets, or are they now X's? No LinkedIn thought leader infomercials, no Substack hype, just actual research and deep thoughts.
So I spent time reading, collating data. Forums, whitepapers, LinkedIn posts, Hacker News threads, VC essays, Reddit arguments. I went looking for the real signal underneath the noise. What I found instead was the full spectrum of human overconfidence, lots of moat real estate.
On one end: the hype machine at full throttle. “Software is going to zero.” “A solo dev can now build what a 50-person team built in 2021.” “The era of the $500/month SaaS subscription is over.” “Vibe coding will replace your entire engineering org.” These headlines were everywhere. Breathless. Confident.
Shared tens of thousands of times, this angle gets views, of course, the algorithm loves being fed claps, shares, comments, and reposts.
Most were written by people who had a very good Tuesday with Codex, Windsurf, Claude and Cursor and decided that instant dev, open source to Github and getting oodles of stars, maybe even roping in a celebrity, was now the permanent condition of software development.
“We are now famous on GitHub!"
Very hipster, very vibes, see you on the playa..
On the other end: the backlash. Experienced engineer, people with 15 to 25 years in production systems are pushing back hard. “Show me the vibe-coded app that survived its first real enterprise security audit.” Reddit threads in r/ExperiencedDevs filled with the quiet exhaustion of people who had been here before. Who remembered when COBOL was going to be replaced overnight. When no-code was going to eliminate developers. When offshore outsourcing was going to make senior engineers irrelevant. The cynicism was warranted. But it was also, in its own way, too simple.
This is the final conclusion in our hybrid viewpoint. It has to be after doing all the research.
Both sides are right. And both sides are missing the point: Another Paradox
The hype crowd was correct that something fundamental has changed about the cost of making software. A feature that took a team of six engineers six weeks to build in 2022 now takes one engineer with AI assistance one week. That compression is real. It is documented. It is not going away. The indie developer in a bedroom in India with Deepseek or Kimi can now ship a product that would have required a funded startup two years ago. That is a genuine, irreversible shift.
The skeptics were correct that production software is not a code problem. It never was. The hard parts of software, the parts that break in the night, the parts that regulators audit, the parts that require your system to integrate with seventeen other systems that were not designed to talk to each other, the parts that require your data to be in the right place at the right time with the right access control—none of that got easier. Some of it got harder.
Both things are simultaneously true. And the tension between them is not a contradiction to be resolved. It is the paradox you have to learn to live inside if you want to understand what is actually happening to the software industry in 2026.
The Story Nobody Was Telling
After reading through those twenty forums and forty headlines, I landed on an idea that I have not seen stated cleanly anywhere.
Here it is:
The cost of writing software is approaching zero. The cost of running software at scale is going up. And the cost of moving your data — in, out, between systems, across borders, in compliance with regulations that did not exist three years ago — is becoming the defining competitive variable of the decade.
That is the real story. Not “software is free now.” Not “nothing has changed, the hype is fake.” Something in between, and more interesting than either.
The moats that protected enterprise software companies for twenty years with complex features, large engineering teams, deep workflow integration, and proprietary APIs, they are being raided. Tiny marauders with AI tools and a clear problem to solve are breaching the walls faster than the incumbents can repair them. Bain & Company found in 2025 that 35% of enterprises had already replaced at least one SaaS tool with a custom-built alternative. Retool’s 2026 Build vs. Buy survey of 817 enterprise builders found 78% planning to build more internally this year.
The castle walls are coming down. But the moat, the actual, literal moat — is the data. And the data is not going anywhere. It is getting harder to move, not easier. Egress fees, GDPR obligations, data sovereignty requirements, security compliance, and audit trails. Every year, the cost and complexity of relocating enterprise data increases. The moat is not technological. It is gravitational.
Meanwhile, the customer, the enterprise paying $200,000 a year for software that a developer could now replicate in a month, is now experiencing a specific kind of cognitive whiplash. They read the same headlines you read. They see the demos. They understand, intellectually, that they are being overcharged for something a competent team with AI assistance could rebuild. They want to leave. Then they open a ticket with their data team and ask what it would take to migrate, and the answer comes back: eighteen months, $1.5 million, and a compliance re-certification that the board will not approve before Q3 next year. So they renew. Furiously. But they renew with disdain and a conference call back-and-forth haggle, saying the support service is not as good as it was now that it's outsourced.
The best we can do is an 8% discount for a 24-month renewal.
And the indie developer who just shipped a product that competes with a $200M ARR SaaS company? They are six months away from their first enterprise prospect sending them a 47-page security questionnaire and asking for their SOC 2 Type II certification. Which they do not have. Which will cost $60,000 and nine months to obtain. At which point the economics of “I built this in a weekend” run directly into the economics of “enterprise requires you to prove you are trustworthy, and proof costs money.”
This is the paradox in full.
The barriers to building have never been lower. The barriers to enterprise adoption have never been higher. The cost of the AI infrastructure powering everything is going up, not down. The open-source models undercutting the proprietary providers are getting better faster than anyone predicted. The customers are simultaneously delighted by what AI makes possible and infuriated by what their data gravity well makes impossible: freedom outside the moated castle, NO ESCAPE!
There will be a slow death by 1000 cuts for the horizontal SaaS companies whose only moat was “we were here first and you are too trapped to leave.” There will be margin erosion — at the feature layer, at the application support layer, and eventually at the proprietary model layer. There will be new winners — but they will not win on code. They will win on data portability, compliance infrastructure, and the ability to operate in a world where the model you use today is not the model you use in eighteen months. And there will be a lot of very confused corporations, sitting on mountains of legacy software subscriptions, wondering why they spent $3 trillion on digital transformation and their productivity numbers barely moved.
That last sentence is not a projection. It is already documented. McKinsey published the data.
What This Article Actually Is
This is not a hot take. I am not going to tell you software is dead, or that engineers are obsolete, or that AI will replace your entire stack by Q4.
What I am going to do is walk through the evidence, from McKinsey, from Gartner, from production engineers, and from enterprise data, show you what the actual shape of this transition looks like. Where value is genuinely disappearing. Where new costs are silently accumulating. Where the next defensible positions are forming. And why the companies that win the next decade will not be the ones who built the most features, but the ones who solved the unglamorous problem of how to move data safely, cheaply, and portably in a world where every vendor is trying to trap it.
I will also be honest about the uncertainty. Some of this is projection. Some of it is pattern recognition from a builder who has been deep in this infrastructure problem. None of it is certain. The future of software is not a straight line. But the forces at work are real, the costs are documented, and the paradox is not going away.
Let’s go through it properly.
Marc Andreessen said software was eating the world.
He was right. But he didn’t finish the sentence.
Software ate the world. Then AI ate the cost of making software. And now we’re living inside the paradox: the easier it becomes to build, the harder it becomes to win. Not because of competition. Because of infrastructure. Because of data. Because of the invisible tax that compounds every time you try to move something, scale something, or certify something for a real enterprise customer.
This is the story of democratised software. The part they don’t put in the YC application.
The Chart That Should Terrify Every CTO
McKinsey published two charts in May 2025 that, placed side by side, tell the whole story.
Chart one: US enterprise IT expenditure grew at 8.0% CAGR from 2020–2024. That’s $157 on the index, up from 100 in 2014. Chart two: US labor productivity grew at 1.7% CAGR over the same period. It reached 119.
Spend up 57%. Productivity up 19%.
You’re spending three times more money to get one-third the proportional return.
And the spend profile changed dramatically underneath that headline number. Software as a share of total IT spend doubled — from 15% in 2014 to 30% in 2024. Internal labor collapsed from 26% to 18%. External services held relatively flat.
Read that again. You doubled your software spend, cut your internal headcount, and productivity barely moved.
The productivity gap is not a technology problem. It is a data problem.
The software got cheaper. The intelligence got better. The part that stayed expensive and the part that consumed the productivity gains: Ingress/Egress was getting data in, getting data out, making systems talk to each other, and keeping it all compliant.
If you extrapolate these curves to 2030, the gap becomes a chasm.
Projection: US IT Spend vs Labor Productivity (Index, 2014 = 100)
Year IT Spend Index Productivity Index Gap
2024 157 119 38 pts
2027 195 128 67 pts
2030 245 135 110 pts
By 2030, enterprises will have spent roughly $3 trillion on software and AI tooling since 2020, with productivity gains equivalent to getting an extra 0.5 junior engineers per $100M of investment. The gap does not close. It compounds. Because the problem was never the software.
What Actually Costs Money (The Iceberg No One Shows You)
There is a famous iceberg diagram floating around enterprise circles. Above the waterline: build cost, software licenses, pilot compute. Things that get approved in a business case.
Below the waterline: data prep, production API costs, retraining, evals, drift monitoring, human-in-the-loop oversight, compliance trails, change management, vendor lock-in, shadow AI cleanup.
The research is brutal on what happens next. According to CloudZero’s analysis, average monthly AI spending hit $85,521 in 2025 — up 36% year-on-year. The proportion of organisations planning to invest over $100,000 per month more than doubled from 2024 to 2025. A separate analysis found that 85% of organisations misestimate AI project costs by more than 10%, with nearly a quarter underestimating by 50% or more.
These overruns do not come from the model costs. They come from what no one budgeted for:
The plumbing. Supposedly we need more plumbers, but not that type..
The Backend
Gennaro Cuofano at FourWeekMBA coined the term the interoperability tax in 2025. The definition: the compounding cost of making systems work together. It consumes up to 40% of IT budgets. Healthcare spends $30 billion a year just making systems talk to each other. Financial services allocates 35% of IT budgets to integration. Manufacturing loses 20% of productivity to data silos.
The average large enterprise now runs 231 applications. They barely communicate. And each integration point adds latency, complexity, security surface, and cost. You connect your CRM to email: 50ms overhead. Add marketing automation: 100ms. Add analytics: 200ms more. What started as a simple data query now traverses six security checkpoints, three middleware layers, and two authentication systems before returning a result.
Every API is a tax. Every middleware layer is a compounding fee. And you don’t see the bill until you’re already paying it.
The Cost of Moving Your Own Data
Here is the line that separates the hype from the reality.
Egress fees are the new switching cost.
You built on AWS. Your analytics are in Redshift. Your CRM data is in Salesforce. Your customer records are in a managed PostgreSQL instance. You have five years of behavioral data in S3. Now a better option appears — a new system that’s cheaper, faster, more aligned with where AI is going.
The math:
50GB in Salesforce, 200GB in your warehouse, 500GB in S3 analytics
AWS egress: $0.09-$0.12 per GB outbound
Migration cost: $75,000-$90,000 just to pull the data out
Integration rebuild time: 6–12 months
Compliance re-certification (if healthcare, finance): $500,000-$2,000,000
Engineer cost at loaded rate: $200,000-$400,000
Total cost of switching: $800K to $2.5M for a mid-size enterprise.
You can’t vibe code your way through that; it is a real, technically complex redeployment.
This is not a coincidence. The cloud providers have known for years that the real lock-in is not the product quality. It is the egress tax. You can be furious at the service. You can hate the interface. You can be paying 40% above market for the feature set. And you still won’t leave, because leaving costs more than staying.
This is why the McKinsey charts look the way they do. The software got better. The AI got smarter. The productivity didn’t move. Because the data never moved either. It’s trapped. And the systems built on top of it are trapped with it.
Bain & Company put real numbers on the shift in 2025: 35% of enterprises had already replaced at least one SaaS tool with a custom-built alternative. Retool’s 2026 Build vs. Buy Report — surveying 817 enterprise software builders — found 78% plan to build more internal tools this year. The top categories: workflow automation, internal admin tools, BI. CRM and customer support ranked lower only because those replacements already happened in 2023 and 2024.
Companies are trying to escape. The data won’t let them move fast enough.
Everyone Can Build Software Now. Almost No One Can Ship It to Enterprise.
This is the second half of the paradox. And it is where most indie developers and early-stage startups learn the most expensive lesson.
Yes, you can build a SaaS feature in a day. The post on Reddit from the experienced dev who rebuilt a DocSend replacement in an afternoon? Real. The founder who replicated Canva’s core functionality over a weekend?
Also real.
What is not real is the claim that this makes enterprise software free.
The code is free. The certification is not.
Here is what it actually costs to take a product to enterprise readiness:
Enterprise Entry Tax (2026)
SOC 2 Type II audit: $50,000 - $150,000
ISO 27001 certification: $100,000 - $300,000
HIPAA compliance (healthcare): $200,000 - $500,000
GDPR/CCPA readiness: $100,000 - $200,000
Pen testing (annual): $50,000 - $150,000
Security monitoring tooling: $30,000 - $80,000/year
Kubernetes/DevOps Infrast: $50,000 - $200,000/year
Data pipeline engineering: 3-6 engineer-years to build
0.5 FTE/year to maintain
Minimum to touch enterprise: $500,000 - $2,000,000
This tax does not compress with AI. Regulations do not become cheaper because you used Cursor to write the code. An ISO auditor does not care how fast you shipped the feature. A CISO does not reduce their vendor questionnaire because your README is well-written.
The developer experience cost has collapsed. The enterprise compliance cost has stayed flat — and in many cases is rising, as GDPR enforcement intensifies, new AI regulations emerge in the EU and UK, and US states pile on with their own privacy frameworks.
And there is a third cost that is easy to miss: the cost of running the AI that powers your product.
At 10 billion tokens per day — a realistic figure for a B2B product with real enterprise customers — the numbers look like this:
Model Cost per 1M tokens Daily cost Annual cost GPT-4o (OpenAI) ~$2.50 $25,000 $9.1M Claude Sonnet ~$3.00 $30,000 $10.9M Self-hosted Llama 3.1 70B ~$0.003 $30 $11,000 Self-hosted Mistral 7B ~$0.001 $10 $3,650
The cost ratio between proprietary API and self-hosted open source is already between 800:1 and 3000:1. By 2028–2030, as open models close the remaining quality gap with proprietary systems, that ratio reaches 10,000:1.
You cannot build a sustainable enterprise AI product on $10M/year in inference costs while charging enterprise pricing. The margins collapse before you find product-market fit.
The Proprietary Model Squeeze
OpenAI and Anthropic face a specific version of the paradox that is worth naming directly.
They democratised software development. They put advanced AI capability in the hands of solo developers, indie builders, and small teams. That was the mission. What the mission created, as a side effect, was the demand signal for open-source models to catch up.
Every indie developer who learned to build with Claude or GPT-4 became a market participant who would eventually ask: could I run this myself for less?
The answer, as of 2026, is: yes, for a lot of use cases.
DeepSeek-R1 achieves near-parity with o1 on reasoning benchmarks. Llama 3.1 405B runs inference that was GPT-4-class capability two years ago. Mistral Small runs on consumer hardware. The quality gap between closed and open is real at the frontier, but it is closing at every tier below it.
OpenAI’s inference cost for GPT-4o has dropped significantly since launch. Anthropic’s API pricing has compressed. But there is a floor as investor returns require it. At 50% gross margin targets, the minimum viable price per token is set by the hardware cost, not by competitive pressure from the open ecosystem.
The open ecosystem has no such floor. A Llama model self-hosted on a $6,000 GPU cluster costs, in amortised compute terms, roughly $0.000001 per token. A million tokens for a fraction of a cent.
By 2030, for workloads that do not require frontier capability, which is most enterprise workloads — the business case for proprietary API is gone. The enterprise that committed to OpenAI at $10M/year in 2025 will be looking at a $10K/year self-hosted alternative in 2029.
The companies selling the picks and shovels are running out of miners who need to buy them.
Where Value Actually Accumulates (And Why It Is Not Code)
The a16z essay “The Empty Promise of Data Moats” made a controversial argument in 2019 that turned out to be only half right. The claim: data network effects are overhyped, and most data scale effects have limited defensive value.
The half that was wrong: proprietary, unique, operationally-generated data is still the most defensible asset in the stack. What they were correctly attacking was the idea that accumulated data volume alone creates a moat.
Volume is not a moat. Context is.
Veeva Systems in life sciences. Pave in compensation benchmarking. Epic in healthcare. Their moats are not that they have more data. It is that they have the only data — real, verified, continuously updated data from millions of operational interactions that no one else can legally or practically access.
What AI has done is collapse the distinction between feature moats and context moats. The feature moat is gone as anyone can build the UI, the workflow, the integration. The context moat has never been more valuable.
But it only exists if you designed your product to generate and capture it.
The Attainment Labs analysis of this shift put it cleanly: “The moat is not the software. The moat is what the software has been ingesting over years of operation, and what that data allows you to do that no competitor can replicate.”
The Steven Cen framework from February 2026 identified six non-functional moats that survive the AI commoditisation wave: SEO/GEO as a time barrier, brand as mindshare anchor, product taste as quality ceiling, team velocity as execution flywheel, data assets as self-reinforcing loop, and founder networks as trust license.
What all six share: they require time. They compound. They cannot be generated by a prompt.
Which creates a specific strategic window for builders who recognise it now.
The 2030 Market Structure
By 2030, the enterprise software market fractures into three structural segments.
Segment One: The Legacy Hostages (approximately 60% of enterprise spend)
Stuck in Salesforce, Workday, SAP, Oracle. The egress tax is too high, the compliance re-certification is too expensive, and the risk too visible to boards and auditors. They will spend 40–50% of their IT budget on SaaS subscriptions that underperform their needs, complain about it constantly, and renew anyway.
Their vendors know this. The lock-in was never about product quality. It was always about data gravity.
Segment Two: The DIY Scalers (approximately 25% of enterprise spend)
Teams that have figured out how to build internal tooling with AI assistance, are moving toward open-source model infrastructure, but are grinding against the compliance and data pipeline tax. They can ship fast. They cannot certify fast. They cannot move data fast.
This segment needs infrastructure that reduces the enterprise entry tax without rebuilding it from scratch for every product.
Segment Three: The Infrastructure Winners (approximately 15% of enterprise spend, but growing fastest)
Organisations that adopted composable, compliant, portable infrastructure early. They can run any model. They can move data without catastrophic egress costs. They can swap vendors when a better option appears. They are not hostage to any single provider.
This is the only segment where the productivity curve actually bends upward toward the spend curve. Because they did not just buy more software. They built the ability to change.
The Tools That Actually Solve This
This is where the VEKTOR Memory tools come in. As primitives for a different way of building, particularly aimed at memory database migration.
It would be nice if it worked on all types of data migration? That's super complex, as quickly realised, as there are no set tool frameworks… Similar to file conversion software.
The problem they are solving:
The transition from Segment One and Segment Two to Segment Three requires infrastructure that did not exist two years ago. You need:
A way to connect AI tools to any workflow without rebuilding the integration stack every time
A way to move agent memory and context across systems without losing continuity
A portable data interchange format that works regardless of which model, provider, or vendor you are using
Three open-source tools address these directly.
Via — a universal CLI integration layer for AI tools. The insight behind Via is that the bottleneck is not the AI. The bottleneck is the connection between AI capability and the specific workflow that needs it. Every company has a different stack, a different data structure, a different security requirement.
Via abstracts the integration layer so you can connect any AI tool to any workflow without writing the same connector code repeatedly.
Vek-Sync — a synchronisation layer for agent-generated data. As enterprises adopt more AI agents — across different providers, different model families, different tools, the problem of context fragmentation becomes acute. An agent in Claude does not know what an agent in Cursor did yesterday. Vek-Sync solves for persistent, portable context that travels with the workflow rather than being trapped in the tool.
Vex — a portable agent memory interchange format. The .vmig.jsonl format is the key primitive here. It is to agent memory what .csv was to tabular data: a lowest-common-denominator format that any system can read, write, or export.
The moat for Vex is not the tool. It is the format standard. If .vmig.jsonl becomes the default format for agent memory interchange, every system that adopts it creates a compatibility obligation for the next system.
The strategic positioning of all three is identical: they are free, open-source, and solve the data portability problem at the layer where the real cost accumulates. The business model is not charging for the tool. It is building the infrastructure that removes vendor lock-in — and then being the most capable layer on top of that infrastructure.
The Numbers That Prove the Timing
Why now? Because several things converged in 2025–2026 that did not exist together before.
Open model quality crossed the threshold for most enterprise workloads. Llama 3.1 and Mistral models now handle the vast majority of enterprise text processing, summarisation, classification, and extraction tasks at quality parity with GPT-3.5-class capability. For most internal tooling, that is sufficient.
The compliance tax became visible at scale. The first wave of indie-built enterprise tools hit the certification wall in 2024–2025. The founders who went through it documented the cost. The market now understands that “I built this in a weekend” and “I can sell this to a Fortune 500” are separated by a $500K-$2M gap.
Data egress became politically visible. EU regulators, UK data protection authorities, and several US states began scrutinising vendor egress pricing as a form of anticompetitive lock-in. This is early — but the direction of travel is clear. Data portability mandates are coming. The infrastructure that already supports portable data will benefit.
The productivity gap became undeniable. The McKinsey charts we opened with are not projections. They are documented history. CIOs are carrying them into board meetings and asking why productivity has not moved. The answer they are getting — “we need to integrate better, move data more efficiently, reduce the interoperability tax”, this is the exact problem the open-source infrastructure stack solves.
The uncertainty we will all face
Let me close with the version of this argument that is honest about the uncertain future.
Software did not democratise equally. Code democratized. The ability to generate syntax, scaffold applications, prototype workflows, and ship basic product — that did democratize. A solo developer in 2026 can build what a five-person team built in 2022. That is real.
But enterprise software is not a code problem. It was never a code problem. It is a trust problem, a compliance problem, a data portability problem, and an infrastructure problem. Those problems did not democratise. In some ways they intensified, because the gap between “anyone can build this” and “this is enterprise-ready” is now more visible than ever.
The companies that will win the next decade are not the ones that build the most features. They are the ones that solve the unglamorous infrastructure problem: how do you move data safely, cheaply, and portably in a world where every vendor is trying to trap it?
The playbook, stated simply:
Code is free. Data is expensive. Integration is the tax. The moat is portability.
If you are building enterprise software in 2026, the question is not “can I build this feature?” You can. The question is: “when a better model appears next year, when a cheaper data store emerges, when a new compliance requirement lands, can I adapt without paying the switching cost twice?”
The companies building on portable, open-source infrastructure are positioning themselves to answer yes. The companies building on top of single-vendor, proprietary-API-dependent stacks are accumulating a debt they will not see until they try to leave.
Appendix: The 2030 Forecast
For anyone writing a business case, here are the projections:
2030 Enterprise Software Landscape
IT Spend CAGR (2024-2030): 6.5% (decelerating)
Labor Productivity CAGR: 2.8% (accelerating but still low)
SaaS % of IT Budget: ~42%
Data Egress Lock-In Cost (avg): ~$250K
Compliance Tax (DIY → Enterprise): $1M - $2M
Proprietary LLM Cost/1M tokens: ~$0.01
Self-Hosted Open Model Cost/1M: ~$0.000001
Cost ratio (Proprietary:Open): 10,000:1
The ratio is the story. At 10,000:1, the conversation inside every large enterprise AI project flips from “which provider should we use?” to “why are we paying a provider at all?”
The answer, for workloads that require frontier capability — medical reasoning, legal analysis, complex code generation — will still be proprietary providers. They will retain the frontier. But the frontier is 5–10% of enterprise AI workload by volume.
The other 90–95%? That is self-hosted open models, running on infrastructure that someone had to build first.
Resources
The VEKTOR Memory open-source infrastructure stack:
Via (universal CLI integration layer) — github.com/Vektor-Memory/Via
Vek-Sync (agent data synchronisation) — github.com/Vektor-Memory/Vek-Sync
Vex (portable .vmig.jsonl memory interchange format) — github.com/Vektor-Memory/Vex
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Primary sources cited:
McKinsey & Company, “The new economics of enterprise technology in an AI world,” May 2025
Gennaro Cuofano / FourWeekMBA, “Interoperability Tax: The $500B Hidden Cost Killing Digital Transformation,” August 2025
CloudZero, Enterprise AI Spending Report, 2025
Keyhole Software, “AI Software Development Costs 2026,” April 2026
Retool, “Build vs. Buy Report 2026”
Bain & Company, Enterprise SaaS Replacement Survey, 2025
Andreessen Horowitz (a16z), “The Empty Promise of Data Moats,” May 2019
Attainment Labs, “AI Is Eating Software,” February 2026
Lenovo Press, “On-Premise vs Cloud: Generative AI TCO (2026 Edition)”
Integrate.io, “Data Quality Improvement Stats from ETL — 50+ Key Facts,” 2026
DX / GetDX, “Total Cost of Ownership of AI Coding Tools,” 2025
VEKTOR Memory builds open-source AI infrastructure. Via, Vek-Sync, and Vex are free tools for portable, vendor-agnostic AI integration. The SDK and enterprise memory layer are available at vektormemory.com.
Data Sovereignty · AI Economics · Artificial Intelligence · SaaS · Software Architecture · Data Moats · Digital Transformation · Enterprise Software
