Oxlo.ai
Scale across AI models without scaling your bill
880 followers
Scale across AI models without scaling your bill
880 followers
Most AI teams pick a model first and discover the bill later. We built Oxlo.ai to change that. Access 35+ frontier AI models including DeepSeek V4 Pro, Kimi K2.6, GLM 5, Qwen, Llama, and Mistral through a single API. Compare models, calibrate responses, and choose the right model for each use case. Scale across AI models with predictable monthly subscriptions, benchmark-grade performance, generous usage limits, and we never train on your data.
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Render Sandboxes: a computer for every agentIsolated compute, persistent state and a real filesystem
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The agent spend forecasting problem is what gets teams in trouble - you ship something that works, it starts getting real usage, and suddenly your AI infrastructure bill looks like a ransomware demand. We went through exactly this building agentic workflows - prototype costs look fine, then the agent starts doing multi-step reasoning chains at scale and the bill triples.
Quick question on the mechanics: when my agent makes a call, do I explicitly pick the model per request, or does Oxlo do any routing/optimization automatically? I'm guessing explicit control is better for quality guarantees, but curious whether you have any plans for cost-aware routing as an optional layer - like "use the cheapest model that meets this quality threshold."
Congrats on the launch - the fixed pricing angle is smart positioning for teams trying to get finance sign-off on AI infra.
The core claim here is cost reduction across multiple models, but the interesting engineering question is where the savings actually come from. Routing calls to cheaper models based on task complexity is one approach, caching repeated or near-identical completions is another, and they have pretty different tradeoffs in terms of output consistency and latency. Curious which of those Oxlo is doing, and whether you have any control over the routing logic or whether it's fully automatic. Also wondering how this behaves when you're mixing models with different context window sizes or tool-calling implementations, since a lot of multi-model setups quietly break at that layer.
One thing I've noticed with AI copilots is that the challenge isn't generating suggestions, it's earning enough trust for people to rely on them in their daily workflow. I like that Oxlo AI seems to focus on becoming part of the workflow instead of just another chat interface. That's a much harder problem to solve.
How do you know when users have started trusting Oxlo enough to rely on it every day?
Congrats on the launch! I played with your calculator on the landing page for a while from my iPhone — good stuff but it is incredibly laggy. Worth fixing ASAP 🙌
What’s the secret in achieving the fixed price? It sounds unbelievable and there must be a ceiling.
In hardware, we never pick a component without optimizing the BOM (Bill of Materials) first, so the 'discover the bill later' problem in AI is a massive pain point we can completely relate to. I love the concept of routing through a single API to keep costs predictable.
I’m curious about the calibration and switching latency—when swapping between models like DeepSeek V4 Pro or a Llama model for different use cases under a single subscription, how do you handle response time consistency? Speed-to-action is everything for real-time interfaces. Massive congrats on the launch!
Congrats on the launch, @barath_kanna_bk Predictable pricing for AI infrastructure is a massive pain point solved.
qq. on the fixed subscriptions: Is there a hard cap where requests throttle, or do you have a soft limit that triggers an upgrade prompt? Def checking this out today.