· AI
Three Vendors Hold 88 Percent of Enterprise AI Spend. Almost None of Their Customers Ever Tested an Alternative.
A cheaper model will not fix your AI bill if you have no way to tell whether it still did the job.
Three vendors now account for roughly 88 percent of enterprise AI API spend, according to Menlo Ventures data cited by CIO.com, split roughly 40, 27, and 21 percent. That is not what a competitive market with real price differences is supposed to look like. I pulled the actual numbers, and the price differences are real: Anthropic's own pricing page lists Claude Sonnet 5.5 at $2 per million input tokens and $10 per million output tokens. DeepSeek's official pricing puts its V4 Pro model at $0.66 and $1.98 off-peak, and its Flash model at $0.15 and $0.60. That is a serious open-weight model running three to five times cheaper than a mainstream frontier one, and a cheap one running thirteen to seventeen times cheaper.
So why do three vendors still own almost nine in ten dollars of enterprise spend? My position: most buyers never built a way to find out if switching would cost them anything in quality. Without that, the safe move is to not switch, and inertia wins by default. That is not a market failure. It is a measurement failure, and only the buyer can fix it.
Here is the honest case for staying put anyway, because it is a real one. Enterprises that have tried switching AI vendors report real friction. In Zapier's survey of 500 U.S. enterprise executives, 66 percent had already attempted a platform migration, and 58 percent of those said it failed outright or took significantly more effort than expected, even though 89 percent believed going in that a switch would take four weeks or less. Nearly three in four said losing their primary AI vendor would disrupt daily operations or leave a core function unable to run. If you are already deep into one vendor's tooling, prompts, and support relationship, ripping that out to chase a lower sticker price is a real risk, not a hypothetical one.
That case weakens on inspection, though. The capability gap buyers are often protecting is small and not even stable in open models' favor. Stanford's 2026 AI Index puts the leading closed model 3.3 percentage points ahead of the leading open model on the Arena Leaderboard as of March 2026, up from a 0.5 point gap in August 2024. The gap did not keep closing. It widened again. For the routine 90 percent of enterprise AI work, a 3 percentage point difference on a leaderboard is noise next to a 3x to 17x per-token price difference. Paying the frontier premium can still be the right call for the hardest agentic and reasoning work, where small accuracy differences compound across long task chains. But that is a specific, narrow case, not a blanket reason to never price-check the other 90 percent of what you run through the same vendor.
The CIO.com piece makes the sharper point: list price is not cost per task, because a model that reasons longer or answers more verbosely can cost more per task even at an identical or lower per-token rate. That cuts against reflexively switching to whatever is cheapest on a pricing page just as hard as it cuts against never switching at all. Either mistake comes from skipping the same step.
The fix is not a vendor decision. It is a test you build once: a few hundred real queries pulled from your actual peak-load traffic, scored against criteria your own team writes down in advance, not a vendor's demo. Run your current vendor through it to get a baseline. Then run one cheaper open-weight option and one frontier option through the identical set before your next renewal, not after you are already committed. That is the only way to know whether the 3x price gap is free money or a quality cut you have not noticed yet.
This is close to what I do for clients through MojoAI, the fractional AI advisory side of the business: build the test set, run the real comparison, and make the vendor or model call on evidence instead of a sales deck or a leaderboard screenshot. If your last model decision was "we picked one two years ago and never looked again," that is worth a second look. Get in touch.
Sources
References used in this article. Links also appear alongside the relevant claims.
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