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Cloudflare, Inc.

NET

9 stories

$109B Market cap · 2026-10-29

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Convequity on X

A company gets an API key from a model provider (OpenAI, Anthropic, Google) and links it in OpenCode Console. OpenCode, the coding agent, uses that key to run models at the provider, billed to the company. It is a bearer credential: anyone with a copy can spend on the account. OpenCode disclosed unauthorized access to a legacy system being decommissioned. Keys for about 1.4% of Console users who linked external providers may have been exposed. OpenCode calls it a distillation attack. Users report charges for runs they never made. Provider terms generally make the key owner responsible, so the bill can land on the customer. A copied key works at the provider until revoked. OpenCode already runs on Cloudflare Workers, and Console is a gateway with budgets. Direct calls with a stolen key bypass Console and its budgets. Whether that legacy host ran on Cloudflare is undisclosed. In our view, customers need a control layer they own that keeps the raw provider key away from the agent and its vendor: a vault-held key, or Cloudflare ($NET) AI Gateway prepaid billing with no customer provider key, plus scoped keys, spend caps, per-user limits, alerts and full logs. F5 ($FFIV), Kong and LiteLLM offer related controls. We cannot say these would have stopped this breach. They can limit blast radius, spend and detection time: metrics we think a CFO will ask for before the next rollout.
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Convequity on X

Software multiples have fallen further since our January report. As of 7 Oct 2026, 238 companies in our all-software watchlist have EV/Sales (NTM) data on Koyfin. The median is 3.1x, down from 3.7x in January. The average is 5.4x, down from 6.2x. The 25th percentile is 1.8x, down from 1.9x. The 75th percentile is 5.5x, down from 6.1x. 191 of the 238 (80%) trade below our 6.7x private equity floor. 113 trade below 3x. The Bessemer Cloud Index shows the same pattern. Its median is 4.7x, down from 5.1x in January. 46 of its 68 companies (68%) trade below 6.7x. The cheapest are $CXM at 0.9x, $SPT at 1.1x, $MNDY at 1.7x and $WIX at 1.8x. In our watchlist, only 21 companies trade above 10x. The most expensive are $PLTR at 44.5x, $CRWD at 42.4x, $NET at 38.6x, $PANW at 24.2x, $DDOG at 19.5x and $SNOW at 16.3x. Growth still earns a higher multiple. The 44 names expected to grow revenue 20%+ a year over 3 years have a median of 5.2x. The 85 names expected to grow under 10% have a median of 2.6x. Even for the faster growers, the 5.2x median is below the floor. The market is paying up for very few names. We think that is right. Many software companies have slowing growth, high costs and no clear AI strategy. We would not buy the sector, or a cloud index, on valuation alone. In software, we focus on the few companies rebuilding their products around AI, and our SaaS Survival Scorecard tests key SaaS names against the pressure from AI agents.
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Convequity on X

$NET's AI hosting is limited by the physics of its edge footprint and by the role of its products. Carrier-hotel sites built for fast network traffic are a different proposition from purpose-built AI datacenters. Cloudflare’s own engineering disclosures acknowledge power constraints, and management distinguishes its strategy from the hyperscalers’ GPU spending race. Our assessment starts with a two-H100-per-server edge configuration. H100 PCIe and NVL-class cards operate around 350–400 watts per GPU, versus up to 700 watts for H100 SXM. Hyperscalers commonly pack eight SXM GPUs into a machine and connect those machines into dense clusters. The distinction is power, cooling, memory bandwidth and GPU interconnects — not simply whether a model can run. The existing edge footprint cannot acquire hyperscaler-class cooling and power density through server upgrades. Dense, liquid-cooled Blackwell and Vera Rubin systems require a different infrastructure commitment. We see little chance that NET’s existing edge footprint becomes a competitive home for those dense clusters. For sustained, large-model inference, our assessment is that sparse edge GPUs cost more per token than dense AI clusters. Geographic proximity saves network transit time, but it does not guarantee a faster first token or a cheaper response. Greater compute capacity, faster interconnects and efficient batching can outweigh the edge’s network advantage. We expect heavy training, frontier-scale hosting and the most demanding next-generation inference to remain concentrated with hyperscalers. Workers AI’s progress with larger open models is real. Hosting those models does not establish an economic advantage over dense clusters. AI Gateway also lets customers route requests to external providers, matching the model and infrastructure to the task. Cloudflare can still win substantially. More agents mean more requests to secure, more Workers executions, more orchestration and more demand for fine-grained access controls. Those workloads build on infrastructure and capabilities Cloudflare already has. On the SaaS Survival Scorecard, we score $NET at 2.5 for Model-Layer Leverage. That reflects its substantial work building an agent harness across inference, orchestration and execution, while stopping short of model post-training and reinforcement learning.
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Convequity on X

A bit of surpsied to see OpenAI Dot's VM spec is so low: CPU: 9 visible logical CPUs; host reports AMD EPYC 9V74 at ~2.6 GHz RAM: 9.7 GiB total, ~8.7 GiB available; no swap Storage: 32 GiB workspace filesystem, ~30 GiB free Also uses Cloudflare $NET network as egress like Grok Bot does but the computer spec is so much lower than Grok Bot. Maybe OpenAI is just more resource constrained vs. SpaceX given a much bigger user base. The demand for traditional compute, networking and storage will grow crazy as more people start to adopt this cloud agent architecture.
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Convequity on X

Agentic AI is changing the question for every SaaS name. The old one was “how big is the market?” The new one: when agents do the work, does the vendor get bypassed, survive as plumbing, or own the stack? We built a scorecard for that split — and ran the first three deep dives: • $PLTR — Route 3 ownership bet (ontology / multi−domain ops). Structure elite; valuation still has to earn it • $DDOG — Route 2 plumbing agents need. Host telemetry is hard to absorb casually; coding agents + first-party collect is the watch item. Multiple roughly fair • $NET — purest agent-volume rails. Unabsorbable network footprint; physics caps the model layer; ~40x prices in a lot Condensed public version below. Full Part 2 + series for Convequity subscribers.

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