u/Accomplished_Ask3336

working on this for a while and wanted to share it with people who might actually find it useful.

the backstory is pretty simple. I kept building things with LLMs and running into the same two problems after shipping. costs that didn't match my estimates because users generate way more near-duplicate requests than you'd expect. and output quality that was fine in testing but inconsistent in production because real users don't write structured prompts, they write whatever comes to mind.

synvertas is a gateway that sits between your app and your model provider, works with OpenAI, Claude and Gemini. semantic caching catches the near-identical requests and serves the cached response so you're not paying twice for the same intent. a prompt optimizer intercepts the user's input and rewrites it into something cleaner before it hits the model, which makes a real difference for output consistency. and there's automatic provider fallback so if one of the three has issues your app doesn't go down with it.

the integration is one URL change. your existing SDK, your existing code, nothing else needs to touch.

every project I've worked on that used an LLM ran into the same three things. costs that were hard to predict. at least one outage from OpenAI or whoever that took the whole feature down. and users typing things into the app that the model couldn't do anything useful with.

I looked around for something that handled all three without having to self-host a bunch of infrastructure. couldn't really find it, so I built it.

synvertas.com sits between your app and your model provider, works with OpenAI, Claude and Gemini. it handles semantic caching so you're not paying full price for near-duplicate requests, automatic fallback between providers when one has issues, and a prompt optimizer that rewrites vague user inputs into something the model can actually work with before the request goes out.

integration is just a URL swap in your existing SDK setup. nothing else changes in your codebase.

it's early and I'm a solo founder so I'm not going to oversell it. would genuinely appreciate feedback from people who've dealt with these problems, what am I missing, what would you need to see before you'd consider using something like this.

every project I've worked on that used an LLM ran into the same three things. costs that were hard to predict. at least one outage from OpenAI or whoever that took the whole feature down. and users typing things into the app that the model couldn't do anything useful with.

I looked around for something that handled all three without having to self-host a bunch of infrastructure. couldn't really find it, so I built it.

synvertas.com sits between your app and your model provider, works with OpenAI, Claude and Gemini. it handles semantic caching so you're not paying full price for near-duplicate requests, automatic fallback between providers when one has issues, and a prompt optimizer that rewrites vague user inputs into something the model can actually work with before the request goes out.

integration is just a URL swap in your existing SDK setup. nothing else changes in your codebase.

it's early and I'm a solo founder so I'm not going to oversell it. would genuinely appreciate feedback from people who've dealt with these problems, what am I missing, what would you need to see before you'd consider using something like this.

Most of us running AI wrappers or SaaS tools eventually hit the exact same wall. Your API bill gets out of hand because users ask the same questions repeatedly, or OpenAI goes down for an hour and takes your entire application down with it. Building the custom infrastructure to handle semantic caching and model fallbacks from scratch takes your focus away from actually building your core product.

I wanted a much simpler way to manage this without rewriting my whole codebase every time so I built synvertas.com It is a dedicated AI gateway designed specifically for bootstrapped founders and solo developers.

The setup process is completely frictionless. You literally just swap out your standard OpenAI base URL with the Synvertas URL in your code or your no code workflow.

Once it is connected it automatically checks if a new prompt is semantically similar to a previous one. If it is a match it serves the response directly from the cache dropping your response time to milliseconds and costing you zero API credits. And if your primary provider happens to crash the gateway silently routes the request to a backup model like Anthropic so your users never even notice an outage.

To push the cost savings even further I also built a prompt optimizer directly into the routing layer. It automatically compresses and refines your incoming requests on the fly before they hit the LLM.

If you are tired of dealing with custom AI infrastructure and want to protect your margins you can check it out. I would love to hear your feedback on the dashboard and the setup process.

Most AI apps silently waste money on duplicate API calls. Same question, different wording — full price every time. Nobody really talks about it.

I got annoyed enough to build something: Synvertas (https://synvertas.com). It's a proxy that sits between your app and your AI provider — one URL change in your SDK, nothing else breaks.

Similar prompts get cached automatically. Messy user prompts get cleaned up before they hit the model. And if your provider goes down, it switches to the next one without your users noticing.

Would you actually use something like this — or is this a problem you've just accepted?

Most AI apps silently waste money on duplicate API calls. Same question, different wording — full price every time. Nobody really talks about it.

I got annoyed enough to build something: Synvertas (https://synvertas.com). It's a proxy that sits between your app and your AI provider — one URL change in your SDK, nothing else breaks.

Similar prompts get cached automatically. Messy user prompts get cleaned up before they hit the model. And if your provider goes down, it switches to the next one without your users noticing.

Would you actually use something like this - or is this a problem you've just accepted?

Noticed my AI costs were growing way faster than my actual usage warranted. Turned out a huge chunk of requests were users asking basically the same thing - just worded slightly differently.

So I added similarity matching before the request hits the API. Similar prompts return the cached response without touching OpenAI at all.

Threw in automatic provider fallback too while I was at it - if OpenAI goes down, it silently switches to Anthropic or gemini.

Users never notice.

One URL change in the SDK, nothing else breaks.

https://synvertas.com — still early, feedback welcome.