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.
In our team's experience, the product of agents is generally The Homer (1). It does work, but it's vastly overengineered.
When I personally want tight code, I have to spend a considerable amount of time adjusting it manually:
- It needs to be trimmed down. In my experience, at least one agent I use struggles to produce minimalist designs, and it's very frustrating
- I need to consider whether there are solutions based on higher-level assumptions, that AIs typically miss
- I need to check whether there are off-the-shelf solutions - AIs like to reinvent the wheel
IMO, software production has become a mass-produced commodity in every sense - it's much more expensive to produce software manually, but the quality is not the same.
IME this works until it does not. This approach works well at the beginning of a greenfield project, but at the same time because it is so easy to add features, you will likely ship something that is way too over engineered. And that complexity will not amortize over next increments and will more likely lead to the entire project being a black box only fully understood by AI. However a more careful use of AI for targeted surgical changes is far more ”productive” in the long term IMO.
Do you have issues with performance at the moment? Right now I tend to find that it produces absolutely terrible design patterns and especially performance. I mean maybe I don't know exactly what area you're looking at but yeah for us we tend to find it's terrible wrt dB/caching/scaling and often any performance improvements it proposes end up actually shooting itself in the foot and being worse than before but it's not very good at testing in an organized way to even notice it made it worse despite repeated prompts to do so I mean if I prompt it to test performance in a handheld structured way (it is very bad at finding out what performance to test and why) before making changes I can usually figure it out but it usually takes insistence on the specifics to really ensure a good solution that will actually fix the problem
Performance is better than ever. It's never been more practical to set up wildly complex synthetic test environments and measure perf wins. Plus the models will find every possible algorithmic/design improvement.
It actually gives me quite an uncanny feeling, bulldozing over years of human optimization work with a newer, "perfect" design. Like bringing an AK-47 back to the middle ages.
Keep the decision-making and execution separate. Use the high IQ models to chat about the design and make them drive subagents to do the actual work. "Chat" style threads are actually quite cheap. Where it gets expensive is having Fable 5 output thousands of lines of implementation where 95% of it was already overdetermined and there were only a few important judgement calls.
I actually have no doubt that I could replace my Opus 5 Low/Medium subagent profiles with Grok 4.5/GLM 5.2/Deepseek v4 Flash and perf would probably be pretty similar.
On top of that - highly recommend adding accurate cost counters to your statusline. You can't improve what you don't measure! (Or even have any intuition about).
Are you at least conversational in the subject matter? You're gonna have a good time just by paying attention and adjusting your workflow. If you're getting a lot of back and forth with it, its asking a lot of planning type questions, stop, step back, rethink the whole feature, and start again from the beginning with everything more fleshed out.
If you are in a brand new field, there's no way to bridge that divide. The issue is you don't know what is good or bad, or whether what you have learned is good or bad. You're in a sports car and you don't know how to drive much less what's track and what's field.
You can spend a lot of effort getting good at prompting towards writing tests and E2E tests to at least verify your app does what you expect it to, regardless of experience.
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.
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.
... where in the article did they say they were using subscriptions? I'm fairly certain enterprises can't access subscription pricing in any case, they're all API costs (Anthropic doesn't support more than 150 on subscription pricing [0][1]).
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 ;)
> Rapidly adopting newer, more efficient models delivers the largest cost wins of any technique.
I think the more interesting lever is the fourth they mention: token efficiency.
> By the time costly LLM inference occurs, the user's initial statement accounts for only a negligible fraction of the data fed into the AI system, meaning costs are dominated by context the user did not explicitly include.
I think there’s still lots of low hanging fruit in regards to monitoring and improving agent work. Look at your sessions. Look at how much time and context is being spent on, say, a web search returning dozens of results when one good single-pager doc would’ve been better.
These seem like the obvious tweaks akin to "using a cheaper hosting platform". I think the real savings come from careful context control for programmatic agents, careful tool awareness and usage to reduce thrashing, distilling workflows into deterministic processes and, moat importantly, adding friction and boundaries for non-technical users who tend to burn tokens making insane asks like "analyze all documents and give me a summary".
I've tested Omnigent superficially, attracted to its thinking around policy, governance, sandboxing, and ui. But it's still alpha at present. I forked its Polly model and got working a somewhat more complex multiagent workflow that I've also modeled in Sandcastle and Gas City but the agent broke after the next update which I would have needed to patch to maintain functionality. Subjectively I also noticed individual models seemed to be performing somewhat worse when wrapped in the platform's framework, presumably due to the extra context introduced (token use was measurably higher). Promising project that I'll revisit when it's further along and I do not doubt the outcomes Databricks claims in committedly dogfooding it.
Really? Because removing it from my company has saved us over 2 million a year and we were able to speed up processing. The chargeback model for databricks is predatory at best.
I think you’ve misunderstood the article. It’s about how Databricks reduced their own costs, not about how adopting Databricks will reduce anyone else’s costs.
- 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.
When I personally want tight code, I have to spend a considerable amount of time adjusting it manually:
- It needs to be trimmed down. In my experience, at least one agent I use struggles to produce minimalist designs, and it's very frustrating
- I need to consider whether there are solutions based on higher-level assumptions, that AIs typically miss
- I need to check whether there are off-the-shelf solutions - AIs like to reinvent the wheel
IMO, software production has become a mass-produced commodity in every sense - it's much more expensive to produce software manually, but the quality is not the same.
(1) https://simpsons.fandom.com/wiki/The_Homer
- Suggest a better approach that makes the AI say, “That’s much simpler. And you’re right. My original plan was over-engineered.”
It actually gives me quite an uncanny feeling, bulldozing over years of human optimization work with a newer, "perfect" design. Like bringing an AK-47 back to the middle ages.
I actually have no doubt that I could replace my Opus 5 Low/Medium subagent profiles with Grok 4.5/GLM 5.2/Deepseek v4 Flash and perf would probably be pretty similar.
On top of that - highly recommend adding accurate cost counters to your statusline. You can't improve what you don't measure! (Or even have any intuition about).
Are you at least conversational in the subject matter? You're gonna have a good time just by paying attention and adjusting your workflow. If you're getting a lot of back and forth with it, its asking a lot of planning type questions, stop, step back, rethink the whole feature, and start again from the beginning with everything more fleshed out.
If you are in a brand new field, there's no way to bridge that divide. The issue is you don't know what is good or bad, or whether what you have learned is good or bad. You're in a sports car and you don't know how to drive much less what's track and what's field.
You can spend a lot of effort getting good at prompting towards writing tests and E2E tests to at least verify your app does what you expect it to, regardless of experience.
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.
This is how AWS made its fortune.
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.
[0]: https://support.claude.com/en/articles/9797531-what-is-the-e...
[1]: https://support.claude.com/en/articles/9266767-what-is-the-t...
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 ;)
I think the more interesting lever is the fourth they mention: token efficiency.
> By the time costly LLM inference occurs, the user's initial statement accounts for only a negligible fraction of the data fed into the AI system, meaning costs are dominated by context the user did not explicitly include.
I think there’s still lots of low hanging fruit in regards to monitoring and improving agent work. Look at your sessions. Look at how much time and context is being spent on, say, a web search returning dozens of results when one good single-pager doc would’ve been better.
https://www.databricks.com/blog/introducing-omnigent-meta-ha...
https://github.com/omnigent-ai/omnigent