MAROKO133 Hot ai: Railway secures $100 million to challenge AWS with AI-native cloud infra

📌 MAROKO133 Eksklusif ai: Railway secures $100 million to challenge AWS with AI-na

Railway, a San Francisco-based cloud platform that has quietly amassed two million developers without spending a dollar on marketing, announced Thursday that it raised $100 million in a Series B funding round, as surging demand for artificial intelligence applications exposes the limitations of legacy cloud infrastructure.

TQ Ventures led the round, with participation from FPV Ventures, Redpoint, and Unusual Ventures. The investment values Railway as one of the most significant infrastructure startups to emerge during the AI boom, capitalizing on developer frustration with the complexity and cost of traditional platforms like Amazon Web Services and Google Cloud.

"As AI models get better at writing code, more and more people are asking the age-old question: where, and how, do I run my applications?" said Jake Cooper, Railway's 28-year-old founder and chief executive, in an exclusive interview with VentureBeat. "The last generation of cloud primitives were slow and outdated, and now with AI moving everything faster, teams simply can't keep up."

The funding is a dramatic acceleration for a company that has charted an unconventional path through the cloud computing industry. Railway raised just $24 million in total before this round, including a $20 million Series A from Redpoint in 2022. The company now processes more than 10 million deployments monthly and handles over one trillion requests through its edge network — metrics that rival far larger and better-funded competitors.

Why three-minute deploy times have become unacceptable in the age of AI coding assistants

Railway's pitch rests on a simple observation: the tools developers use to deploy and manage software were designed for a slower era. A standard build-and-deploy cycle using Terraform, the industry-standard infrastructure tool, takes two to three minutes. That delay, once tolerable, has become a critical bottleneck as AI coding assistants like Claude, ChatGPT, and Cursor can generate working code in seconds.

"When godly intelligence is on tap and can solve any problem in three seconds, those amalgamations of systems become bottlenecks," Cooper told VentureBeat. "What was really cool for humans to deploy in 10 seconds or less is now table stakes for agents."

The company claims its platform delivers deployments in under one second — fast enough to keep pace with AI-generated code. Customers report a tenfold increase in developer velocity and up to 65 percent cost savings compared to traditional cloud providers.

These numbers come directly from enterprise clients, not internal benchmarks. Daniel Lobaton, chief technology officer at G2X, a platform serving 100,000 federal contractors, measured deployment speed improvements of seven times faster and an 87 percent cost reduction after migrating to Railway. His infrastructure bill dropped from $15,000 per month to approximately $1,000.

"The work that used to take me a week on our previous infrastructure, I can do in Railway in like a day," Lobaton said. "If I want to spin up a new service and test different architectures, it would take so long on our old setup. In Railway I can launch six services in two minutes."

Inside the controversial decision to abandon Google Cloud and build data centers from scratch

What distinguishes Railway from competitors like Render and Fly.io is the depth of its vertical integration. In 2024, the company made the unusual decision to abandon Google Cloud entirely and build its own data centers, a move that echoes the famous Alan Kay maxim: "People who are really serious about software should make their own hardware."

"We wanted to design hardware in a way where we could build a differentiated experience," Cooper said. "Having full control over the network, compute, and storage layers lets us do really fast build and deploy loops, the kind that allows us to move at 'agentic speed' while staying 100 percent the smoothest ride in town."

The approach paid dividends during recent widespread outages that affected major cloud providers — Railway remained online throughout.

This soup-to-nuts control enables pricing that undercuts the hyperscalers by roughly 50 percent and newer cloud startups by three to four times. Railway charges by the second for actual compute usage: $0.00000386 per gigabyte-second of memory, $0.00000772 per vCPU-second, and $0.00000006 per gigabyte-second of storage. There are no charges for idle virtual machines — a stark contrast to the traditional cloud model where customers pay for provisioned capacity whether they use it or not.

"The conventional wisdom is that the big guys have economies of scale to offer better pricing," Cooper noted. "But when they're charging for VMs that usually sit idle in the cloud, and we've purpose-built everything to fit much more density on these machines, you have a big opportunity."

How 30 employees built a platform generating tens of millions in annual revenue

Railway has achieved its scale with a team of just 30 employees generating tens of millions in annual revenue — a ratio of revenue per employee that would be exceptional even for established software companies. The company grew revenue 3.5 times last year and continues to expand at 15 percent month-over-month.

Cooper emphasized that the fundraise was strategic rather than necessary. "We're default alive; there's no reason for us to raise money," he said. "We raised because we see a massive opportunity to accelerate, not because we needed to survive."

The company hired its first salesperson only last year and employs just two solutions engineers. Nearly all of Railway's two million users discovered the platform through word of mouth — developers telling other developers about a tool that actually works.

"We basically did the standard engineering thing: if you build it, they will come," Cooper recalled. "And to some degree, they came."

From side projects to Fortune 500 deployments: Railway's unlikely corporate expansion

Despite its grassroots developer community, Railway has made significant inroads into large organizations. The company claims that 31 percent of Fortune 500 companies now use its platform, though deployments range from company-wide infrastructure to individual team projects.

Notable customers include Bilt, the loyalty program company; Intuit's GoCo subsidiary; TripAdvisor's Cruise Critic; and MGM Resorts. Kernel, a Y Combinator-backed startup providing AI infrastructure to over 1,000 companies, runs its entire customer-facing system on Railway for $444 per month.

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🔗 Sumber: venturebeat.com

📌 MAROKO133 Breaking ai: For the first time, NASA’s Perseverance rover completes M

NASA’s Perseverance rover has completed the first AI-planned drives on another world, marking a milestone in autonomous space exploration.

In early December, the six-wheeled robot followed routes generated by artificial intelligence rather than human planners, navigating Mars using machine-driven decision-making for the first time in mission history.

The demonstration took place on Dec. 8 and Dec. 10 and was led by NASA’s Jet Propulsion Laboratory in Southern California.

Engineers used generative AI to create waypoints for Perseverance, a task traditionally handled by experienced rover drivers on Earth.

The rover safely executed both drives without human-designed routes, proving that AI can plan complex surface navigation on another planet.

“This demonstration shows how far our capabilities have advanced and broadens how we will explore other worlds,” said NASA Administrator Jared Isaacman.

“Autonomous technologies like this can help missions to operate more efficiently, respond to challenging terrain, and increase science return as distance from Earth grows. It’s a strong example of teams applying new technology carefully and responsibly in real operations.”

The JPL team relied on vision-language models, a form of generative AI that can interpret images and text together.

The system analyzed the same surface data that human planners normally use.

This included rover imagery, terrain maps, and hazard information. Based on that data, the AI generated a continuous driving path with safe waypoints.

The project ran from JPL’s Rover Operations Center in collaboration with Anthropic, which provided its Claude AI models.

The AI assessed features such as exposed bedrock, sand ripples, steep slopes, and boulder fields. It then selected a route that avoided hazards while keeping the rover on course.

For Perseverance’s 1,707th Martian day, or sol, the rover drove 689 feet, or 210 meters. Two sols later, it completed a second AI-planned drive of 807 feet, or 246 meters.

Both traverses matched expectations and stayed within operational safety limits.

Testing before Mars commands

Despite the autonomy, engineers did not send AI commands directly to Mars. The team first verified every instruction using JPL’s digital twin of Perseverance.

This virtual replica checked more than 500,000 telemetry variables to confirm compatibility with the rover’s flight software. Only after passing those tests did engineers uplink the commands.

Mars sits about 140 million miles from Earth on average, creating long communication delays. Because real-time control is impossible, rover teams usually plan routes in advance, step by step.

AI-driven planning could reduce that workload and speed up daily operations.

“The fundamental elements of generative AI are showing a lot of promise in streamlining the pillars of autonomous navigation for off-planet driving,” said Vandi Verma, a space roboticist at JPL and a Perseverance engineer.

“We are moving towards a day where generative AI and other smart tools will help our surface rovers handle kilometer-scale drives while minimizing operator workload.”

NASA views this test as groundwork for future exploration. AI-assisted navigation could support longer rover drives and help identify scientifically interesting targets faster.

It may also play a role in future robotic and human missions beyond Mars.

🔗 Sumber: interestingengineering.com