Hey HN! We’re Abhi, Venkat, Tom, and Nick and we are building Datafruit (https://datafruit.dev/), an AI DevOps agent. We’re like Devin for DevOps. You can ask Datafruit to check your cloud spend, look for loose security policies, make changes to your IaC, and it can reason across your deployment standards, design docs, and DevOps practices.

Demo video: https://www.youtube.com/watch?v=2FitSggI7tg.

Right now, we have two main methods to interact with Datafruit:

(1) automated infrastructure audits— agents periodically scan your environment to find cost optimization opportunities, detect infrastructure drift, and validate your infra against compliance requirements.

(2) chat interface (available as a web UI and through slack) — ask the agent questions for real-time insights, or assign tasks directly, such as investigating spend anomalies, reviewing security posture, or applying changes to IaC resources.

Working at FAANG and various high-growth startups, we realized that infra work requires an enormous amount of context, often more than traditional software engineering. The business decisions, codebase, and cloud itself are all extremely important in any task that has been assigned. To maximize the success of the agents, we do a fair amount of context engineering. Not hallucinating is super important!

One thing which has worked incredibly well for us is a multi-agent system where we have specialized sub-agents with access to specific tool calls and documentation for their specialty. Agents choose to “handoff” to each other when they feel like another agent would be more specialized for the task. However, all agents share the same context (https://cognition.ai/blog/dont-build-multi-agents). We’re pretty happy with this approach, and believe it could work in other disciplines which require high amounts of specialized expertise.

Infrastructure is probably the most mission-critical part of any software organization, and needs extremely heavy guardrails to keep it safe. Language models are not yet at the point where they can be trusted to make changes (we’ve talked to a couple of startups where the Claude Code + AWS CLI combo has taken their infra down). Right now, Datafruit receives read-only access to your infrastructure and can only make changes through pull requests to your IaC repositories. The agent also operates in a sandboxed virtual environment so that it could not write cloud CLI commands if it wanted to!

Where LLMs can add significant value is in reducing the constant operational inefficiencies that eat up cloud spend and delay deadlines—the small-but-urgent ops work. Once Datafruit indexes your environment, you can ask it to do things like:

  "Grant @User write access to analytics S3 bucket for 24 hours"
    -> Creates temporary IAM role, sends least-privilege credentials, auto-revokes tomorrow

  "Find where this secret is used so I can rotate it without downtime"
    -> Discovers all instances of your secret, including old cron-jobs you might not know about, so you can safely rotate your keys


  "Why did database costs spike yesterday?"
    -> Identifies expensive queries, shows optimization options, implements fixes

We would love to hear your thoughts! If you work with cloud infra, we are especially interested in learning about what kinds of work you do which you wish could be offloaded onto an agent.

As someone who's been doing Infra stuff for two decades, this is very exciting. There is a lot of mindless BS we have to deal with due to shitty tools and services, and AI could save us a lot of time that we'd rather use to create meaningful value.

There is still benefit for non-Infra people. But non-Infra people don't understand system design, so the benefits are limited. Imagine a "mechanic AI". Yes, you could ask it all sorts of mechanic questions, and maybe it could even do some work on the car. But if you wanted to, say, replace the entire engine with a different one, that is a systemic change and has farther reaching implications than an AI will explain, much less perform competently. You need a mechanic to stop you and say, uh, no, please don't change the engine; explain to me what you're trying to do and I'll help you find a better solution. Then you need a real mechanic to manage changing the tires on the moving bus so it doesn't crash into the school. But having an AI could make the mechanic do all of that smoother.

Another thing I'd love to see more AI use of, is people asking the AI for advice. Most devs seem to avoid asking Infra people for architectural/design advice. This leads to them putting together a system using their limited knowledge, and it turns out to be an inferior design to what an Infra person would have suggested. Hopefully they will ask AI for advice in the future.

I can see the value, but to do the things you're describing, the AI needs to be given fairly highly-privileged credentials.

> Right now, Datafruit receives read-only access to your infrastructure

> "Grant @User write access to analytics S3 bucket for 24 hours" > -> Creates temporary IAM role, sends least-privilege credentials, auto-revokes tomorrow

These statements directly conflict with one another.

So it needs "iam:CreateRole," "iam:AttachPolicy," and other similar permissions. Those are not "read-only." And, they make it effectively admin in the account.

What safeguards are in place to make sure it doesn't delete other roles, or make production-impacting changes?

IMO it is a smart decision to implement this as a self-hosted system, and have the AI make PRs against the IaC configuration - for devops matters, human-in-the-loop is a high priority. I'm curious how well this would work if I'm using Pulumi or the AWS CDK (both are well-known to LLMs).

I consulted for an early stage company that was trying to do this during the GPT-3 era. Despite the founders' stellar reputation and impressive startup pedigree, it was exceedingly difficult to get customers to provide meaningful read access to their AWS infrastructure, let alone the ability to make changes.

Congrats on the launch. As a former CI build engineer, I’m very curious about this and look forward to watching your progress. One question

> we’ve talked to a couple of startups where the Claude Code + AWS CLI combo has taken their infra down

Do you care to share what language model(s) you use?

I think you are under estimating the nuances you have in non faang infrastructure. Also, based on my previous experience you will meet with developer resistance (may be AI can help you beat that). By being broad you also competing with purpose built solution like finops, devsecops etc. Who also seems to have agents now.

It is workflow automation in the end of the day. I would rather pick SOAR or AI-SOC where automation like this is very common. For eg blinkops or torq.

> (1) automated infrastructure audits— agents periodically scan your environment to find cost optimization opportunities, detect infrastructure drift, and validate your infra against compliance requirements.

Why does that need an AI? I’m pretty sure many tools for those things exist, and they predate LLMs.

Sounds good.

BTW, your website is heavy, for a basic set of components it shouldn't be taking 100% CPU.

Congrats on the launch! Excited to see you guys adopt a BYOC distribution model

Really great stuff! Congrats on the launch!

there have been a lot of attempts to make products like these but this kinda product almost always only one problem. nobody really is sure about the access privileges it requires to operate and what it does on its backend with such privileges

My 2 cents (DevOps Engineer with a decade of experience, MBA, optimistic about tools like Claude Code and pessimistic about what I'm seeing here):

You need to be very clear about the persona who you're building for, what their pain point is, and why they're willing to spend money to solve it. So far it seems like you took an emerging technology (agentic workflows), applied it to a novel area (DevOps), built a UX around it, and tried to immediately start selling. This is the product trap of a solution in search of a problem.

Are you trying to sell to large companies? The problem that large companies have is cultural/organizational, not tooling. For any change, you need to get about a dozen people to review, understand, wait for people to come back from vacation, ping people because it fell off their desk, sign off, get them to prioritize, answer questions again from the engineer the task was assigned to, wait for another round of reviews and approvals, and maybe finally somebody will get the fix applied in production. DevOps is (or at least, it originally used to be) focused on finding and alleviating the bottlenecks; the actual process of finding data or applying changes is not the bottleneck in large companies and so therefore it is not a solution to the pain point that different folk in large companies have. If your value proposition is that large company executives could replace Infrastructure employee salaries with a cheaper agentic workflow, you need to re-read my prior point - if large companies have all this process and approvals for human beings making changes, why would they ever let an agentic workflow YOLO the changes without approval? And yes, I know, your agent proposes Terraform PRs for making changes to keep a human in the loop - but now you slayed one of the Hydra's heads and three more have popped up in its place: the customer needs the Terraform PR to be reviewed by a human committee, some of whose members are on vacation, some of whose members missed the PR request because they had other priorities and it fell off their desk, etc. etc. Doesn't really sound like you solved anything. The fundamental difference between what you built and something like Claude Code is that Claude Code doesn't need a human committee to review on every iteration it executes on an engineer's laptop, only the review of the One Benevolent Laptop User who is incentivized to get good output from Claude Code and provide human review as quickly as (literally) humanly possible.

Are you trying to sell to small companies that don't have DevOps Engineers? What's the competitive space here? The options usually look something like, (a) pay a premium for a PaaS, (b) spend on the salary for your first DevOps Engineer in the hopes that they will save more on low-level infra bills compared to their salary, so you're posing now (c) some kind of DevOps agentic workflow that is cheaper than a DevOps Engineer salary but will provide similar infra cost savings? So your agentic workflow will actually lift and shift to better/cheaper infra primitives and own day-to-day maintenance, responding to infra issues which your customers - who aren't DevOps Engineers, and don't know anything about infra, and are trying to outsource these concerns to you - which your customers don't know how to handle? I would argue that if you really did achieve that, then you should be building an agentic-workflow-maintained PaaS that, by virtue of using agents instead of humans, can undercut traditional PaaS on cost while offering a maybe better UX somehow. If you're asking your customers to review infra changes that they don't understand, then they need to hire a DevOps Engineer for the expertise to review it, and then you have a much less interesting value proposition.

As SRE/Ops person, sigh checks the founder list and starts internally screaming

YC, you want founders of this companies to have 10 years working at Ford Motor Company. It's all reasons I want to write my blog article of "FAANG, please STFU. I wish I could be focused on 100k Requests per Second but instead I'm dealing with engineers who has no idea why their ORM is creating terrible query. Please stop telling them about GraphQL."

"Grant @User write access to analytics S3 bucket for 24 hours" Can the user even have access to this? Do they need write access or can't understand why they are getting errors on read? What happens when they forget in 30 days they asked your LLM for access and now their application does not work because they decided to borrow this S3 bucket instead of asking for one of their own. Yes this happened.

"Find where this secret is used so I can rotate it without downtime" Well, unless you are scanning all our Github repos, Kubernetes secret and containers, you are going to miss the fact this secret was manually loaded into Kubernetes/loaded into flat file in Docker container or stored in some random secret manager none of us are even aware of.

""Why did database costs spike yesterday?" -> Identifies expensive queries, shows optimization options, implements fixes

How? Likely it's because bad schema or lack of understanding with ORMs. Fix is going to be some PR somewhere to Dev who probably does not understand what they are reviewing.

Most of our headaches is the fact that Devs almost never give a shit about Ops, their bosses don't give a shit about Ops and Ops is trying desperately to keep this train which is on fire from derailing. We don't need AI YOLOing more stuff into Prod, we need AI to tell their bosses what downtime they are causing is costing our company so maybe, just maybe, they will actually care.

Translation: The AWS interface is so horrendously complicated that we now need an AI to navigate it.

Also, as a daily AI user (claude code / codex subs), I'm not sure I want YOLO AIs anywhere near my infra.

I've always heard the theory that if you're not ashamed of your launch announcement then you've launched too late, but a page with just "Book a Call" is stretching the plausibility for who could possibly be in the target demographic

I know dang is going to shake his finger at me for this, but come on.

Also:

> AWS emulator

isn't doing you any favors. I, too, have tried localstack and I can tell you first hand it is not an AWS emulator. That doesn't even get into the fact that AWS is not DevOps so what's up: is it AWS only or does it have GCP Emulation, too?

That's my whole point about the leading observation: without proper expectation management, how could anyone who spots this Launch HN possibly know if they should spend the time to book a call with you?

https://www.adafruit.com/trademarks

https://www.uspto.gov/trademarks/search/likelihood-confusion

> Trademarks don’t have to be identical to be confusingly similar. Instead, they could just be similar in sound, appearance, or meaning, or could create a similar commercial impression.