The Short Answer
Revolut says self-hosting open GLM models cut its AI coding costs to roughly a tenth of frontier-lab API prices, about $0.30 per million tokens. The saving comes from choosing the model per task. On ibl.ai you own all the code and the data and run agents model-agnostic, so that choice stays yours.
The account comes from a talk by David Abitbol, who leads AI operations at Revolut, published by the AI Engineer channel on October 29, 2026, and a StartupHub write-up the same day.
What did Revolut actually change in its AI coding setup?
Revolut moved its AI coding work off frontier-lab APIs, which StartupHub names as Anthropic and OpenAI, onto open GLM models it serves itself. Per that write-up, it runs them on 56 Nvidia B200 GPUs and began self-hosting in April.
The talk's title states the headline: AI coding costs cut 90%. StartupHub reports GLM 4.5 served at about 30 cents per million tokens, roughly a tenth of what Revolut had been paying.
Revolut also reported about 60 to 70% faster delivery, and says tuning made the same GPUs 88% faster. The delivery figures come from a sample of about 2,000 engineers.
What did the viral version of the Revolut story get wrong?
The viral version got three details wrong, and none of them change the conclusion. First, the model: posts said GLM-5.3, but the written account names GLM 4.5. Z.ai released GLM-5.3 in August 2026, after Revolut started.
Second, the "$3 per million tokens before" figure. We found it in no source; the talk supports "roughly a tenth", not a specific prior price.
Third, the valuation. Revolut's most recent reported mark is about $115 billion, from a July 2026 secondary share sale. $200 billion is a target, not a valuation.
Is Revolut the only company moving production AI to open models?
Revolut is not alone. Decagon CEO Jesse Zhang has said the company runs about 90% of its workloads on open-source models rather than OpenAI's or Anthropic's, as 36Kr reported in July 2026.
The pattern is the same in both: most production tasks, code completion, classification, extraction, routine drafting, run well on models that cost a fraction of the frontier.
Even hosted list prices show the spread. Z.ai's pricing page lists GLM-4.5 at $0.60 per million input tokens and $2.20 output, and GLM-5.3 at $1.40 and $4.40.
Does self-hosting open models make sense without a GPU cluster?
Self-hosting at Revolut's scale needs a GPU cluster and a team to tune it. Most organizations have neither, and should not pretend to.
The transferable part is not the hardware. It is the right to pick the model per task: a cheap open model for the bulk of the work, a frontier model where the task earns it, and the ability to move either when prices change.
For the serving side, our comparison of vLLM, Ollama, TGI and LocalAI covers which runtime fits which job and what hardware each one needs.
Why does model-agnostic matter more than the model you pick?
Model-agnostic matters more because the cheapest good model changes every few months. GLM went from 4.5 to 5.3 in about a year, and pricing shifted each time.
An agent wired to one vendor's API cannot follow those prices. An agent that names its model as a setting can. On ibl.ai, each agent's LLM is chosen per agent, and Costs shows what each one spends.
That is also the security half of the story. A model you host keeps prompts and code inside your network, as Revolut's does.
Who should own the AI stack that makes this saving possible?
The organization paying for the tokens should own the AI stack. Revolut could switch because it controlled where inference ran. Most companies renting AI per seat cannot.
On ibl.ai you own all the code and the data, the platform runs model-agnostic, there is no per-seat pricing, and you can deploy anywhere, from your own cloud to a fully air-gapped network. That is the same choice Revolut made, without building the platform yourself.
Revolut's earlier move, its own foundation model, PRAGMA, came from the same instinct. ibl.ai is family-owned and operated from New York, NY — a U.S.-headquartered, domestically-owned long-term partner, not a vendor that sells licenses and moves on.
Sources: the talk "How Revolut Cut AI Coding Costs 90% With Self-Hosted Open Models" by David Abitbol on the AI Engineer channel, October 29, 2026; model, price, GPU count, start month and delivery figures from StartupHub's account; GLM-5.3 release from TechNode; list prices from Z.ai; valuation from Tech.eu; Decagon from 36Kr. The briefs said GLM-5.3, a $3 prior price, a $200 billion valuation and an AI Engineering NYC venue; the sources support GLM 4.5, no prior price, about $115 billion, and an AI Engineer talk with the venue unconfirmed.