Databricks cuts AI coding spend by 70%
Databricks drove down AI coding spend 70%
Databricks reveals how it and other digital-native companies like Stripe, Coinbase, Uber, and Ramp manage AI coding costs at scale. By adopting open-source models, dynamic routing, spend gates, and token optimization, they achieve a 'dual mandate': broad access to AI tools with minimal friction while keeping costs within a fixed envelope per user. Key levers include moving to efficient models, using meta-harnesses, and giving developers visibility and tripwires. Databricks reports a 70% reduction in AI coding spend.
That curve is unsustainable - left unchecked it will eventually overtake revenue.
- extr
I would be really curious to hear from devs at Databricks what the experience of development is like internally. I work at a small startup with essentially unlimited AI spend budget - the entire point is that I should be turning to it at every opportunity since our human labor is so expensive relative to tokens. So generally it's like:
- Spend most time prioritizing/discussing what to do.
- Once that's agreed, use Fable 5 High + 5.6 Sol XHigh come up with a design + plan. Agree on the high level plan. (Usually this just comes down to choosing where the change belongs on the spectrum between minimal patch <-> full redesign)
- Use Opus 5 or Sol Med to execute
- Auto-fix bugs and CI until green + thermonuclear review skill x3.
- Manual interrogation of change/nits
- Come up with QA plan and have Codex Computer Use execute on it
- Manually spot check the final result (usually a sizable diff, thousands of lines, complete feature E2E, etc)
I probably spend like $80 a day at least but I produce the output of 3 or 4 2022 engineers and probably at better quality. So it's easily worth it. Would I save money by switching to GLM 5.2 and such...perhaps? IDK. At our scale it's not worth the time spent building the eval harness to actually understand the performance tradeoff.
- lbriner
There are a surprising number of articles like this along the lines of, "we started using AI tools and ended up spending millions per year".
On what planet do people start paying for things without keeping an eye on the costs and no-one notices until you have spent a crazy amount? I don't understand. You are either paying a fixed amount which you are happy about in-advance or you are PAYG in which case you would ballpark how much it costs.
Otherwise it reads a bit like a fake problem, because it didn't really happen, you just foresaw it (as you should) and added a few guide rails.
- sashank_1509
I suspect that when it comes to hard complex software products, you’re better off ignoring agents and doing “trad coding”. What you lose in short term speed you gain in manageable complex codebases.
If you have a 500k line codebase and even > 50% is written by agents, you are in a world of pain that won’t justify the costs longer term.
Now of course, there are products that just involve lots of code but are not actually complex. This is generally the project with like hundreds or thousands of features but most of the features are separate and don’t actually interact in complex ways. Think a task management app with hundreds of features like calendar, email integration etc. there I think agents gives you more bang for the buck. Just my thought, using agents at work.
- platinumrad
Careful. If you admit to using models that weren't trained by OpenAI or Anthropic then you might hauled in front of Congress: https://www.scmp.com/news/china/diplomacy/article/3362616/us...
- sandeepkd
I find this funny and interesting at some levels
1. Codex, Claude and others try to switch models being used at their level itself to manage the cost and outcomes
2. Now company like data bricks develops one more layer on the top of it to do the same task, of finding the base harness and applicable model
Companies like Codex and Claude are focussing/investing heavily on to ensure that people are using their harness directly or instead use APIs. Unless Databricks has some agreement in place they are violating the TOS and openly publishing an article about it. Would be interesting if openAi or Anthropic come back and claim for the API usage prices and all the savings go away.
- dgellow
What I take from this is that models are already commoditized, and it’s pretty clear nobody has a moat: routing for the models, they can be swapped whenever new models are released, AI labs will have to continue to run on the treadmill non stop or be replaced. Long term I cannot imagine that business will be high margin. Routing for the harness, so anything that differentiate a provider vs another isn’t exposed to the user and isn’t too relevant.
One more datapoint for the thesis that OpenAI and anthropic aren’t viable, sustainable businesses, and cannot justify their $1T valuation and the level of compute commitment (reminder that OpenAI committed to >$750B in infra spending for 2030)
- bisonbear
This approach seems fundamentally predicated on being able to evaluate coding agents on your own code by having domain specific evals. With that knowledge, you can trust the routing logic is actually improving/maintaining perf while reducing costs.
Without the insight into agent performance, any changes like this feel like a gamble to save $$ at the cost of developer productivity
I'm actually working on building generic repo-specific benchmarks at https://stet.sh ;)
- nichochar
Surprisingly pragmatic and info packed article..
Kudos to databricks, I also find it interesting that such different companies (Stripe, Ramp, Databricks) are all building the exact same internal tools.
I think building companies is going to look more generic in the future because intelligence is an API now.