Massive Avoids Cloud Exit Fees with DataPacket’s Bare Metal

Massive has turned an infrastructure decision into a business advantage. The company, specializing in real-time web access for AI agents, scrapers, and data pipelines, operates part of its platform on dedicated bare metal servers from DataPacket instead of relying on traditional hyperscalers. The standout result in the case study is remarkable: an average latency of 2 milliseconds to major cloud providers, peak traffic absorption of 20 Gbps at no additional cost, and an estimated annual savings of $2.9 million compared to equivalent outgoing traffic charges on big clouds.

This decision isn’t just technical. It’s a way to protect the business model. Massive sells access to live websites, HTML-rendered content, and structured search results for clients that feed AI agents and automated systems. Each request enters their infrastructure, goes out to the web, and returns with content that must then be delivered to the client. In a cloud billed per gigabyte of egress, this pattern turns growth into a penalty: the more the service is used, the higher the egress costs.

The case published by DataPacket summarizes the paradox well. AI workloads often live in AWS, Google Cloud, or Azure. However, the web access layer that feeds them can be hosted elsewhere. If that layer is far away, each call pays in latency. Plus, if it’s inside a provider that charges for outgoing traffic per gigabyte, growth also hits the bill. Massive has tried to address both issues by placing its infrastructure close to major clouds, but outside their egress pricing models.

Why Web Access for AI Penalizes Outgoing Traffic So Much

Massive’s business is inherently network-intensive by design. Clients direct requests to network.joinmassive.com, and the platform routes them through a global network of millions of voluntary, consented devices across more than 195 countries. The goal is to obtain real-time pages, rendered content, and search results from actual locations.

In this type of service, bandwidth isn’t just a secondary detail—it’s the product in motion. An AI agent that needs to query a website, extract data, read a rendered page, or analyze search results doesn’t just consume CPU. It consumes network, latency, stability, and responsiveness.

DataPacket outlines three reasons why a large cloud-based architecture could be problematic for Massive. First, egress rates: each byte served out incurs a cost. Second, target websites are often hosted on big clouds as well, creating cloud-to-cloud routes with penalties and unnecessary hops. Third, predictability: a web access platform thrives or fails based on its p99 latency, and shared virtualized environments can introduce variability.

The key difference lies in the cost model. In a traditional cloud, an intensive data transfer platform can see its bill scale directly with traffic. With dedicated bare metal servers and more predictable bandwidth plans, the company adds servers and capacity but doesn’t pay a variable rate per gigabyte in the same way. For a business growing 4 to 5 times annually, this difference can determine whether margins improve or erode.

Bare Metal, Direct Peering, and Less Distance to the Cloud

Massive runs its web access infrastructure on dedicated bare metal servers in four DataPacket locations chosen for their direct connectivity to major cloud providers. The logic is straightforward: if many of Massive’s clients run their AI workloads within AWS, GCP, or Azure, and many web destinations reside in those environments too, being just a few milliseconds away reduces friction for each request.

The infrastructure described by DataPacket combines recent AMD EPYC servers, NVMe storage, DDR5 memory, non-shared 2x25GE links per server, and traffic aggregation by region to handle bursts more efficiently. The bandwidth model is based on the 95th percentile, discarding the top 5% of traffic samples, a common approach in high-volume network services.

Direct private peering with major clouds is a core element. Instead of routing all traffic via the public internet, some paths are kept on private, shorter, and more predictable circuits. For a service that promises fast responses for AI agents, an average of 2 milliseconds latency to large clouds is more than just marketing—it’s part of the actual experience.

Control is another consideration. Deploying on bare metal requires more initial effort than simply clicking “deploy” on a hyperscaler. Planning capacity, regions, networking, monitoring, automation, and operations are involved. But in return, you get dedicated hardware, known routes, more stable costs, and less exposure to unexpected traffic surges.

Three APIs on a Shared Physical Infrastructure

The case study identifies three main products from Massive that share this infrastructure base. The Web Access API offers proxy access to any website, supporting HTTPS, HTTP, and SOCKS5, leveraging the network of real devices across over 195 countries. The Web Render API adds full rendering with JavaScript execution and capabilities to deliver rendered HTML or Markdown optimized for language models. The Web Search API provides structured Google SERP results, including organic listings, AI overviews, FAQs, and sitelinks, geolocated to real locations.

While infrastructure isn’t the visible product, it determines whether the product is viable. If each client request incurs high variable costs, scaling becomes more difficult. If each route adds latency, agents depending on that data perform worse. And if traffic spikes lead to unpredictable costs, financial planning becomes more fragile.

Jason Grad, CEO and co-founder of Massive, sums it up directly: scaling web access loads with egress pricing can become expensive quickly. With DataPacket, he says, the company gets the throughput it needs and knows what it will pay each month. He also notes that if Massive were on a hyperscaler, their infrastructure bill would grow at the same rate as the business.

That statement explains why this case matters beyond Massive. Many AI companies find that public cloud is excellent for starting, testing, deploying, and scaling quickly, but not always the most efficient for highly network-intensive workloads. When the product involves moving data, data movement costs become a strategic factor.

A Lesson for AI Infrastructure

The Massive-DataPacket case doesn’t mean bare metal is better for everything. Hyperscalers remain a powerful option for managed services, elasticity, databases, analytics, training, global deployments, and teams that prioritize operational speed. But it does offer an important lesson: in AI, not all layers need to live within the same cloud.

The web access layer, proxy, scraping, rendering, or search can benefit from dedicated infrastructure with predictable, low-latency bandwidth to the clouds where agents operate. It’s a hybrid pattern: models and applications may reside in AWS, GCP, or Azure, while a specialized network layer runs on bare metal nearby.

For companies building agents, crawlers, data pipelines, or RAG systems with frequent access to external content, this architecture warrants attention. It’s not just about how many GPUs are needed—it’s about how much traffic will exit, what it will cost, the latency to cloud providers, how to handle peaks, and the margin left as usage grows.

Massive has opted for a clear approach: pay for dedicated infrastructure and predictable bandwidth rather than a variable rate per gigabyte. In a real-time web content business for AI, that isn’t a minor optimization—it’s part of the core product.

Frequently Asked Questions

What problem did Massive solve with DataPacket?
Massive needed an infrastructure capable of moving large volumes of web traffic for AI agents without the egress costs of hyperscalers hindering growth.

What advantages did dedicated bare metal provide?
According to the case study, it delivered an average latency of 2 ms to large clouds, capacity to handle peaks of 20 Gbps without extra cost, and estimated annual savings of $2.9 million relative to cloud egress prices.

Why is egress so critical in AI services?
Because many agents and pipelines need to read, render, and send back large volumes of content. When each gigabyte of outgoing traffic is variable charged, growth can erode margins.

Does this mean bare metal is better than public cloud?
Not always. Public clouds remain very useful for many workloads. The case shows that for high-traffic, low-latency services, a dedicated bare metal architecture with good peering can be more predictable and cost-efficient.