Vercel CEO Guillermo Rauch and Coinbase CEO Brian Armstrong run very different companies, but they’re making the same architectural bet. Instead of building around a single AI provider, both are designing production systems that can route work across multiple models.

Rauch and Armstrong aren’t making this decision in a vacuum. Frontier models have become much closer in capability for everyday engineering work, open-weight alternatives have improved dramatically, and the price gap keeps widening. That makes it much easier to justify routing work across several models instead of committing to one. 

Trillion tokens, zero loyalty

In an interview with TechCrunch, Rauch said that Vercel now routes more than a trillion tokens a day across millions of deployments, and that the company is actively moving away from one-lab partnerships. Rauch’s point highlights that the model has become just one interchangeable component in a larger inference pipeline.

That’s a significant position from the CEO of a company that serves as deployment infrastructure for a huge share of the frontend ecosystem. Rauch is calling single-lab partnerships obsolete.

Rauch is calling single-lab partnerships obsolete.

Cheaper defaults, smarter routing

Armstrong is making the same bet, and the financial results state his case. Coinbase cut its internal AI spend by nearly half while overall token usage continued to grow, without imposing usage caps on engineers.

Their playbook basically runs on three core levers.

First, it’s an internal LLM gateway. Coinbase deliberately defaults its engineers to lower-cost open-weight models, specifically Z.ai’s GLM 5.2 and Moonshot AI’s Kimi 2.7. Engineers can still pull down a stronger model if a specific job absolutely demands it, but the pricing gap makes the default choice obvious. GLM 5.2 costs roughly $1.40 per million input tokens and $4.40 per million output tokens.

Compare that to Anthropic’s Opus 4.8, which sits around $5 for input and $25 for output. You are looking at a three- to six-times cost reduction per token. And it holds its own on major coding benchmarks, scoring 62.1 on SWE-bench Pro, compared to GPT-5.5’s 58.6. Plus, because Coinbase self-hosts these models, zero code or query data ever leaves their environment.

The second lever is task-based routing. Armstrong makes a highly practical point here, suggesting teams want a frontier model to do the heavy lifting for complex planning, but for pure execution tasks, where cheaper models perform just as well, there is zero reason to pay top dollar.

The third piece is aggressive caching. By keeping a conversation locked to the same model as long as the cached context is valid, Coinbase managed to push its cache hit rate from a measly 5% up to 60%. That 12x jump is a massive cost driver.

Gateways as control planes

If you want to understand Armstrong’s broader mindset, listen to his recent chat on the Sourcery podcast. He casually mentioned that Coinbase now operates with roughly 1,200 full-time AI agents, a number they calculate by normalizing compute hours to a standard 40- to 60-hour workweek. At that scale, he argues that human developers have absolutely no business manually choosing which model to use. The infrastructure has to automate that decision entirely.

Human developers have absolutely no business manually choosing which model to use.

Because foundation models are becoming so easy to swap in and out, the engineering focus is shifting to the surrounding infrastructure. Like a centralized control plane, a gateway intercepts every prompt and makes a dynamic, split-second decision about whether a workload actually requires the expensive reasoning capabilities of a frontier model or a cheaper, faster alternative can handle it. The infrastructure makes that call based on the cache state, the complexity of the task, and real-time pricing.

Teams need visibility into latency, uptime, token consumption, and cost across all providers because using multiple model providers changes observability requirements. Without that data, it’s difficult to know whether routing decisions are actually improving performance or reducing costs.

Test before you trust

Evaluation becomes just as important. Lower-cost models need to be continuously tested against the workloads that matter to an organization before they are deployed to production traffic. Public benchmarks are a useful starting point, but are no substitute for measuring how a model performs on your own code, data, and workflows.

Trying to pick the single best AI provider is a losing game.

What’s striking is that Vercel and Coinbase arrived at remarkably similar architectures despite solving different problems. Both assume that today’s best model probably won’t stay on top for long. If that’s true, the competitive advantage shifts away from the model itself and toward the infrastructure that decides which one to use. 

The post Coinbase runs 1,200 agents and just slashed its AI bill in half appeared first on The New Stack.

Your organization constantly needs more information about system performance, usage, and data while in production — or better yet, before it heads to prod. The challenge of telemetry increases with the complexity of your stack and agentic sprawl. Because “it works in the testing environment” becomes moot in the face of non-deterministic agents.

After all, AI agents span multiple environments, and that leaves traditional log-metric-trace models insufficient to handle the volume of the agentic AI era. The situation can lead companies to think that the best option is to throw everything into the locked box of proprietary tooling, but that creates another problem: Information is siloed within each layer, fragmenting data and taking you further from realizing real AI ROI.

Unified context across fragmented workflows

The OpenTelemetry framework and the OpenSearch distributed search and analytics engine make for a powerful, open-source pairing that gives organizations of all sizes unified context across their fragmented workflows. In fact, OTel has crossed the 95% adoption threshold for new cloud-native instrumentation projects and has already become the default choice for Greenfield projects.

OpenSearch, sponsored by Amazon Web Services, is gaining traction with AI engineers, as it recognizes that observability and AI must be united. This year’s OpenSearch roadmap specifically focuses on making it the primary retrieval interface for AI agents and an essential piece of any retrieval-augmented generation and agentic AI stack. 

Join us on July 22

Just because open source doesn’t have a direct cost doesn’t mean it’s free. That’s why Dotan Horovits and Rekha Thottan of AWS are going to perform a live troubleshooting simulation using correlated logs, metrics, and traces, followed by a demo of how agentic traces flow through Otel pipelines. Also learn how the open-source evaluation framework Agent Health can provide a structured pre-production benchmark to flag unpredictable agentic behavior before release. 

Join us live on July 22 to learn along and ask questions to learn how your organization can adopt these open-source standards in the second half of this year — across agentic workloads and traditional infrastructure, at scale.

Register for the webinar here

The post Watch AWS engineers troubleshoot agentic AI with OpenTelemetry and OpenSearch appeared first on The New Stack.