superbased - Single Control Plane For AI Agents

Local / Open Source / No Telemetry
Claude, Codex, Cursor & 20+ tools
(Nvidia Inception)
Link:

[superbased - Single Control Plane For AI Agents @superbased_app]

Your agent hits a question while you are out. It waits, and the afternoon is gone.

Or you answer it from your phone and it carries on. Same session, same machine, nothing moved anywhere.

npm install -g @superbased/observer

pip install superbased-observer

https://t.co/yukztvccPr

https://t.co/zTWQx6hbO6

[superbased - Single Control Plane For AI Agents @superbased_app]

@MatthewBerman @bot @SpaceXAI Ai models pricing tracker which tracks model inference API prices from various providers.

[superbased - Single Control Plane For AI Agents @superbased_app]

@MengTo I think it would be better if you made this into a turn-based combat system. Helps reduce real-time control mechanics requirement

[Sanjay Bhadra 🇮🇳 @sanjaybhadra]

5/ As agents and harnesses multiply, enterprises need to see what each is doing; which models, tokens, tools and permissions it uses; and where budgets, stop conditions or human intervention apply.
@superbased_app @Santosh74038967 @rahulrajsahay

[Sanjay Bhadra 🇮🇳 @sanjaybhadra]

3/ How does an enterprise see which agent and harness is driving a workload, which models and tools it uses, the tokens and infrastructure it consumes; and where to change the mix while maintaining reliability and control?

😀 Enter @superbased_app

[Sanjay Bhadra 🇮🇳 @sanjaybhadra]

Superbased is working on this governance gap in enterprise AI. Its control plane makes the activity, token use and cost of AI coding agents visible on the user’s machine, with controls for routing, guardrails and live intervention.
The broader direction is clear: organizations will need to move from agent inventory to attribution, control and, ultimately, governed use at scale.

@rahulrajsahay @Santosh74038967

[Santosh Kathira @Santosh74038967]

29% employees using unsanctioned tools basically means that folks want to use their preferred agent harnesses.

You can let your employees use the tools of their choice and still manage them.

[superbased - Single Control Plane For AI Agents @superbased_app]

The simplest way to do track session limits usage would be to look at API token consumption between each interval and compare it against the percentage points used.

You can look up the session logs for these details or you can use us (https://t.co/zTWQx6hJDE) to get a better picture.

[superbased - Single Control Plane For AI Agents @superbased_app]

I think it allows for ~$45 of API token usage during the 5 hour session and you get ~4 such 5 hour session limit resets per week.

If you contrast that against a Claude code I believe you get 2x i.e. $ 90-100 per 5 hour session limit and roughly similar number of 5 hour session limit resets.

For the $20 plans Codex is pretty underpowered compared to Claude Code.

[superbased - Single Control Plane For AI Agents @superbased_app]

@tensoracc You can use @superbased_app to check what happened i.e. look back in history to check token usage/cost by session/message.

[superbased - Single Control Plane For AI Agents @superbased_app]

@HoffmanRon I believe they do not charge anything for cloud agents beyond tokens consumed.

Did you get any update on this one because it is startling if true?

[superbased - Single Control Plane For AI Agents @superbased_app]

@angelozhangai @OpenAI @thsottiaux Maybe some cache breaking change.

[superbased - Single Control Plane For AI Agents @superbased_app]

@Joshuathinks1 Well....
https://t.co/0WCL2lGxCa

[superbased - Single Control Plane For AI Agents @superbased_app]

@kalashvasaniya Ouch...
You need better tracking of your usage...

[Santosh Kathira @Santosh74038967]

@TonyXavier_ Do try @superbased_app .

We're not a desktop app though - the agents are managed from your browser.

Let us know what you think.

https://t.co/Tl7qohyO0S

[Sanjay Bhadra 🇮🇳 @sanjaybhadra]

5/ How does an enterprise see which model, provider or harness is driving the workload (is suited to for that work) and change the mix, while maintaining reliability or control?

That is one of the problems being addressed by @superbased_app. Going beyond the model price list telling you just what tokens cost to determining what reliable execution of the workload costs.

[Sanjay Bhadra 🇮🇳 @sanjaybhadra]

The interesting bit about the screenshot below is: different harness + model combinations, working on different parts of the same codebase, at the same time and in 'One' view.
We may be moving fairly quickly from “which model do you use?” to “which combination (stack) do you use for which part of the job?”
@Santosh74038967 is meanwhile doing a nice bit of dogfooding here: using Superbased to demonstrate why a control plane across agents starts to matter.
The future probably isn’t one agent doing it all, it’s tending towards managing the 'agent portfolio'! @rahulrajsahay

[Santosh Kathira @Santosh74038967]

Ox-Alpha has been running at 19 toks/s on average.

Speed has become an issue but not complaining at all.

It is damn good and I just hope it is reasonably priced.

[Santosh Kathira @Santosh74038967]

Codex + GPT 5.6 Sol (xhigh) on the left

OpenCode + Ox Alpha (high) on the right

Both for very different parts of the codebase and it is working wonderfully well

Both on ▞ https://t.co/twAR4tZZQw

[Santosh Kathira @Santosh74038967]

Ox Alpha is behaving like Fable at spinning up and managing sub-agents - it is fantastic.

Along with @opencode , it is absolute fire !

Tok/s range from single digits to 50 toks/s but guess what, it doesn't matter - because there are no long network queue wait times like Claude Code.

Net result is that it is faster than Fable.

I'm thinking I might switch.

This seems criminally good right now.

[Santosh Kathira @Santosh74038967]

Demand for AI outstrips supply even now!

That's a signal that we have yet to hit the peak of the AI cycle yet.

Cursor gave a "Heavy load" message for the 1st time for me just now.

Claude Code has frequent 529 errors or slow serving.

Kimi chat hits limits frequently due to user load.

ZCode was supposed to give GLM 5.3 tokens for free for the weekend but stopped literally within an hour after they hit their budgeted limit.

Deepseek increased the price of Deepseek V4 Flash and Pro models because they could and had to.

OpenAI limit resets are far less frequent - once in 2 weeks now.

Compute availability is the main bottleneck - AI is still majorly supply side constrained.

▞ superbased allows you to start & switch between any AI agent directly from the dashboard so that you will never be agent/model constrained.