Launched this week
Synvolv
For AI teams who need growth without cost chaos
32 followers
For AI teams who need growth without cost chaos
32 followers
Synvolv gives companies the operational control needed to run AI products at scale. It combines usage visibility, spend oversight, tenant-level budget controls, intelligent routing, and live policy enforcement in one layer. This helps teams improve cost governance, protect margins, reduce operational inefficiencies, and scale AI services with greater reliability, accountability, and commercial control.
Curious about this, if a team is already juggling multiple providers and a lot of usage/cost tracking manually, what’s the first thing Synvolv helps bring under control?
@shikhar_werthar The first thing Synvolv helps bring under control is live cost behavior, especially budgets, tenant-level usage, and routing.
A lot of teams today are stitching together manual tracking across providers and only realizing what happened after the spend has already drifted. Synvolv puts control in the live request path, so teams can make tenant economics visible, set enforceable limits, and define routing/model rules that can be tested before production.
In practice, that means the first win is moving from “tracking cost after the damage” to “controlling spend as requests happen.”
I have definitely seen costs spike unexpectedly with AI APIs, but usually just depend on alerts or dashboards. How is Synvolv different in terms of actually preventing that, and how does it fit into an existing setup?
@con_builds That’s exactly the problem we’re going after.
Most alerts and dashboards are reactive, they help you see the spike, but only after it has started. Synvolv is about giving teams a live control layer, so they can shape what happens across requests, models, and providers before cost drift turns into a real business issue.
And from an integration point of view, the goal is to fit into an existing setup as a layer around your current traffic, not force a full rework.
@1yuvi9 interesting, the control layer framing makes sense.
How granular are the controls (e.g., per user or endpoint), and is the routing rule-based or adaptive over time?
@con_builds Appreciate that Conard.
Pretty granular, the core idea is that controls should map to how the business actually thinks about usage, not just sit at one global account level. So tenant-level is obvious, but you can also think in terms of project / workspace / feature, and over time even tighter scopes like endpoint or user where that matters.
On routing, we’re very intentional about not making it feel like a black box. The foundation is rule-based, because teams need predictable behavior in production. Then over time those rules can be tuned around live signals like cost, latency, provider health, or budget pressure.
So basically: structured enough to be reliable, flexible enough to get smarter with real traffic.
jared.so
When you set tenant level budgets, what happens to in flight requests once a tenant hits their limit, do they get queued, downgraded to a cheaper model, or just rejected? Really interesting approach to the cost problem.
@mcarmonas Love this question Martí.
That’s actually the whole point for us, Synvolv doesn’t decide what should happen when a tenant hits a limit, because that’s not really an infra decision, it’s a business decision.
What we do is give the business the control layer to define those patterns upfront. So as a tenant budget gets close, or a feature starts spiking cost, they can choose the action that makes sense for their product, downgrade, reroute, cap usage, pause non-critical flows, reject, whatever fits their margin and customer experience.
So the answer is: it’s not one fixed Synvolv behavior. We make the actions automatic, but the business decides the pattern.
This is interesting. For teams already feeling the pain of AI cost and provider complexity, what does adoption usually look like, is Synvolv something you can layer in gradually?
@kamal_jit_singh Yes, that’s exactly how we think adoption should happen.
Teams usually don’t rip out their stack and replace everything at once. Synvolv can be layered in gradually, starting with the parts of the product where cost drift and provider complexity are already painful. For example, a team might begin with one AI workflow or one high-usage feature, put budgets and tenant limits around it, add routing rules, and then expand from there.
The key difference is that Synvolv doesn’t just report spend after the fact, it helps control behavior in real time while traffic is live. So as usage grows, teams can enforce limits, test routing policies before launch, and trigger automatic actions before overspend turns into a margin problem.
How is this different from just setting usage limits or alerts in OpenAI / cloud dashboards?
@jose_codes Good question, usage limits and alerts only tell you when something has happened or is about to go wrong.
What we care about is control while the system is live.
So instead of just notifying a team that cost is rising, Synvolv is built to help shape what happens in that moment, across requests, models, and providers, before it turns into a bigger business problem.