State of AI Engineering 2026
What does it actually take to ship AI to production in 2026? We surveyed the engineers and founders doing it: the stack they run, the practices that hold up, and where builds break. Findings publishing soon.
Findings publishing soon. The 2026 survey is still being fielded and analysed. Every statistic below is a placeholder until the data is in; we will not publish numbers we have not measured. Register to get the report the day it lands.
What we measured
Three questions sit at the centre of the report. We are publishing the answers, not the hype; so here is what we asked before we tell you what we found.
The production stack
Which model providers, orchestration frameworks, vector stores, and eval tools teams actually run in production; and which they quietly dropped after the pilot.
Practices that ship
How teams handle evals, prompt versioning, guardrails, and human-in-the-loop review: the operational habits that separate a demo from a dependable system.
Where builds break
The failure modes founders and engineering leads report most: cost blowouts, hallucination in the wild, latency, and the long tail from prototype to reliable release.
How the data was collected
Methodology
We will publish the full methodology alongside the findings so you can weigh the numbers yourself. The sample details below are placeholders until fielding closes.
Respondents
Findings publishing soon
Roles surveyed
Findings publishing soon
Fielding window
Findings publishing soon
What the data shows
Key findings
The report is organised into four chapters. Each one answers a question with data, but the headline numbers stay sealed until publication.
01 · Adoption
How far past the prototype have AI features actually reached in production?
Findings publishing soon02 · Tooling
Which parts of the stack are consolidating, and which are still churning release to release?
Findings publishing soon03 · Reliability
How are teams measuring quality, and what do they do when an eval regresses?
Findings publishing soon04 · Cost & scale
What breaks first as usage grows: spend, latency, or the review loop?
Findings publishing soon
Why we are writing this
The gap between a working AI demo and a system you can put in front of real users is wide, and most teams cross it alone. Model launches get all the coverage; the unglamorous reality of evals, guardrails, prompt versioning, cost control, and the human review loop gets almost none. We build AI products in production for a living, so we kept hearing the same questions from founders and engineering leads: is our stack normal, are we spending too much, is everyone else's reliability this hard-won? This report exists to answer those questions with peer data instead of anecdote: an honest benchmark for the people actually shipping, published with its full methodology attached so you can trust the numbers. And when we do not have a number, we will say so rather than fill the gap with a guess.
Get the report on release
The 2026 report is free. Register and we will send you the PDF the day it publishes, along with a short summary of the headline findings.
No spam. We will only use your email to send the report and one follow-up summary.
Build AI in production?
Take part in the next wave
The survey is only as good as the people in it. If you ship AI features to real users and want your team's reality reflected in the next edition, tell us; a founder will loop you in.
State of AI Engineering 2026 FAQ
Not yet. The survey is being fielded and the findings are still being analysed. This page is the home for the report; register your email and we will send it to you the day it publishes.
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Register your email and we will send the 2026 State of AI Engineering report the moment it publishes: findings, methodology, and all.