Bundling Creates Inference Switching Costs
Groq
Bundling makes hyperscaler inference sticky because inference is rarely bought alone, it is wired into the rest of the production stack. A team already running apps on AWS, Azure, or Google Cloud can plug model serving into the same identity system, networking, logging, cost controls, and procurement process, so moving to Groq means changing not just chips but the surrounding operating model. That is why Groq has to win with an obvious speed or cost advantage, not just parity.
-
In practice, the lock in is operational. Bedrock ties inference into IAM, CloudTrail, PrivateLink, and cost allocation tags, so security, audit, and chargeback workflows stay inside existing AWS tooling. That matters for production teams more than raw model speed alone.
-
Enterprise buyers often split decisions this way already. One ML leader used AWS for inference because it provided reliable, secure, programmable infrastructure around the model, while cheaper NeoClouds were better for training clusters. That shows why the incumbent cloud can keep inference workloads even when it is not the lowest cost option.
-
Pure play inference vendors can still break through, but usually by creating a step change. Groq sells an OpenAI compatible API that can be adopted with minimal code changes and claims very high token speeds. Cerebras has followed a similar path, turning specialized hardware into a pay per token cloud product for developers who care enough about speed and margin to switch.
The next phase of inference competition will be decided less by access to models and more by who owns the surrounding workflow. Hyperscalers will keep folding inference into broader cloud bundles, while Groq and other specialists push deeper into latency sensitive and cost sensitive workloads where the performance gap is large enough to justify leaving the default stack.