How to get picked for serverless compute by coding agents
AWS Lambda took 24% of 476 judged serverless compute sessions. What the numbers say a vendor in this category should do.
If you sell serverless platforms, this page is the part of the market no dashboard shows you: what a coding agent does when a developer asks for serverless compute and never compares vendors.
The numbers come from 476 judged sessions with Claude Code, Codex, Cursor, Grok Build CLI and Muse Code, spread across 8 realistic codebases, with every session read by a judge.
What coding agents choose for serverless compute
Across 476 judged sessions, AWS Lambda was chosen most often, in 24% of runs. Vercel Functions was second with 23%. In 18% of runs the agent wrote the code itself and adopted no product at all.
| # | Product | Runs won | Share |
|---|---|---|---|
| 1 | AWS Lambda | 114 | 24% |
| 2 | Vercel Functions | 110 | 23% |
| 3 | Built in-house (no product adopted) | 88 | 18% |
| 4 | Azure Functions | 59 | 12% |
| 5 | Cloudflare Workers | 30 | 6% |
| 6 | Google Cloud Run | 14 | 3% |
| 7 | Inngest | 8 | 2% |
| 8 | Fly.io | 8 | 2% |
| 9 | Trigger.dev | 8 | 2% |
| 10 | Render | 7 | 1% |
Full board, every run replayable: the serverless compute leaderboard.
What the shape of this category means
The leader takes only 24% of runs. This category is genuinely open and the ordering can be moved.
With the top product at 24%, serverless compute is decided in the moment, from what the agent reads and what it finds in the repository. Nothing is locked in, which is the best situation a vendor can be in and the one where the work pays fastest.
The order here is set by the quality of what an agent can read and by whether your product is already present in the codebase. Both are things you can change.
The agents do not agree with each other
In this category Claude Code, Codex and Cursor did not put the same product first.
Grok Build CLI (22 sessions) and Muse Code (71 sessions) join this table with fewer runs, so read their rows as indicative.
| Agent | Runs | Picked most often |
|---|---|---|
| Claude Code | 120 | Vercel Functions (30) |
| Codex | 144 | AWS Lambda (67) |
| Cursor | 119 | Vercel Functions (26) |
| Grok Build CLI | 22 | AWS Lambda (6) |
| Muse Code | 71 | Vercel Functions (18) |
That split decides where a vendor spends. In this category Codex ran a web search in 100% of its sessions and Claude Code in 11%, so what you publish reaches nearly all of Codex's serverless compute sessions and a minority of Claude Code's. Taking AWS Lambda's position with Codex is mostly a content problem. Taking Vercel Functions's with Claude Code leans more on the repository.
Who is asking changes the answer
Every request in this experiment was written as a specific kind of person. In this category the leader changes with the person.
| Who is asking | Runs | Picked most often |
|---|---|---|
| Vibe coder | 60 | Cloudflare Workers |
| Junior developer | 119 | Vercel Functions |
| Senior engineer | 177 | AWS Lambda |
| Enterprise team | 120 | Azure Functions |
That is 4 different products winning serverless compute for 4 kinds of buyer, out of the same 476 sessions. Nobody here is winning serverless compute. They are each winning one kind of buyer.
If you sell to more than one of them, you need pages for each. See how to win the enterprise persona.
What you are really competing against
In 18% of runs the agent wrote the code itself rather than adopting a product. That is low enough that your competition is other vendors, but high enough to be worth watching.
Considered, and never chosen
Because the judge records every product an agent raised and not only the one it picked, this board also shows who kept reaching the shortlist and losing. In serverless compute the clearest case is Azure Container Apps: on the table in 25 sessions, chosen in none.
| Product | Raised in | Chosen in |
|---|---|---|
| Azure Container Apps | 25 sessions | 0 |
Being rejected is a better position than being unknown, and a cheaper one to fix. The product is already in the agent's head and on the list. Whatever ended those 25 sessions is recorded in each transcript, one reason at a time.
What to do about it in serverless compute
- Treat the ordering as movable. The leader holds 24%, so agents are deliberating rather than defaulting, and the inputs they use can change the answer. This is the most winnable shape a category comes in.
- Pick which buyer you are for. The same serverless compute need written as a vibe coder landed on Cloudflare Workers, and written as an enterprise team landed on Azure Functions. Those are two markets, and the enterprise one needs pages containing the constraint words: audit log, data residency, retention, single sign-on. See how to win the enterprise persona.
- Measure per agent. Claude Code put Vercel Functions first, Codex put AWS Lambda first, Cursor put Vercel Functions first, Grok Build CLI put AWS Lambda first, Muse Code put Vercel Functions first. A blended number for serverless compute describes a market that does not exist.
The work that applies to every category rather than to this one is written up separately: audit your documentation, write a quickstart an agent can follow, and how to measure install share.
Every serverless platform on this board
One page per product, with its install share, the per-agent split, and how often it was raised without being chosen.
- Do coding agents recommend AWS Lambda? — chosen in 24% of sessions
- Do coding agents recommend Vercel Functions? — chosen in 23% of sessions
- Do coding agents recommend Azure Functions? — chosen in 12% of sessions
- Do coding agents recommend Cloudflare Workers? — chosen in 6% of sessions
- Do coding agents recommend Google Cloud Run? — chosen in 3% of sessions
- Do coding agents recommend Inngest? — chosen in 2% of sessions
- Do coding agents recommend Fly.io? — chosen in 2% of sessions
- Do coding agents recommend Trigger.dev? — chosen in 2% of sessions
- Do coding agents recommend Render? — chosen in 1% of sessions
Where these numbers come from
476 judged sessions in serverless compute across 8 codebases, part of a published set of 15,287. Real coding agents at pinned versions, in sandboxes, inside realistic codebases, with a simulated project owner in the loop and a blind judge on every session. The full method is on one page: how we measured this.
Every serverless compute run can be replayed on the board.
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Common questions
How many codebases is this based on?
476 judged sessions across 8 realistic codebases. A category only runs on repositories where its seam is open, so coverage differs: some categories ran on more than ten codebases and some on two.
What serverless platform do coding agents choose?
Across 476 judged sessions, AWS Lambda was chosen most often, in 24% of runs. Vercel Functions was second with 23%. The result changes by agent and by who is asking.
Do Claude Code and Codex pick the same serverless platform?
No. Claude Code picked Vercel Functions, Codex picked AWS Lambda, Cursor picked Vercel Functions. Measuring one agent tells you about part of the market only.
How often do agents build serverless compute themselves instead of installing something?
In 18% of runs the agent wrote the code itself rather than adopting a product.
How can a vendor improve its position here?
Make the quickstart run when pasted, state the current version on the documentation page, use one name across product, package and import, write pages for the symptoms users describe rather than only the category name, and get into the repository through templates and framework integrations.
Which serverless platforms do agents consider but never choose?
Azure Container Apps (raised in 25 sessions, chosen in none). Being considered and not chosen is a different problem from being unknown, and it is usually fixable.
Where this comes from
Armature ran 15,287 judged sessions with Claude Code, Codex, Cursor, Grok Build CLI and Muse Code inside 110 realistic codebases, and published every run. The numbers on this page come from that work.