Oxlo.ai

Scale across AI models without scaling your bill

API

Developer Tools

Artificial Intelligence

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the routing-decision latency is the part i'd watch — are you classifying prompt complexity at inference time, or learning per-workload patterns over time? curious which one keeps the overhead from eating the savings.

Congrats on the top spot! Cost scaling across models is such a real pain point — I deal with a version of this myself running an AI image generator, where margin really depends on picking the right model for the right job. Curious how you're handling routing logic: is it mostly cost-based, or does latency/quality play into the decision too?

Teams say they want model flexibility, but most eventually standardize on one model and optimize around it. Curious what you've seen in practice. Does access to 35+ models stay valuable over time, or is it mainly useful during evaluation and testing?

Congrats on the launch!

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.

I've been using Groq for API testing and experimentation, so I was curious to try Oxlo.ai. My first impression is very positive, the platform feels polished, and the playground is especially interesting to explore.

I'll be putting it through more extensive testing, but so far the experience has been smooth. One feature I'd love to see is the ability to cancel a response while it's being generated (playground).

Congrats on the launch!
How does Oxlo.ai help teams compare model performance and cost before choosing which model to use in production?

This is a real problem for anyone running agents in production. With 35+ models on a fixed subscription, models get updated and deprecated over time, and a silent point update can change a production agent's behaviour in ways that are hard to debug. Do you pin exact model versions, so teams can reproduce results and upgrade on their own schedule?

👋 Congrats on the launch, do you plan to support Kimi 2.7 in the near future?

Congrats on the launch! The fixed monthly bill is the part that I like most here. We run an agent that fires a few hundred model calls per task and we know its the variance that wrecks budgeting, never the average

Clean positioning for developers. Feels like something built after actually dealing with real infrastructure pain, not just a surface level idea.

Big congrats 🙌 Oxlo.ai solves a real pain point. predictable costs while switching between models is a game-changer.

Feels like a good fit for people building agents or internal tools where you don’t want to constantly track token consumption. Congrats @megha_varshini and other makers! looks solid.

The tagline mentions scaling across AI models without scaling the bill, which sounds very useful for developer and AI workflow teams. How does Oxlo.ai approach model selection or routing in practice—does it focus more on cost optimization, reliability, performance, or giving teams a unified way to work across different AI providers?

No token math + no surprise bills is really smart! Just noticed one small thing: the top banner says "upto" instead of "up to" that's all. Congrats on the launch!! 💪

This is really wonderful. But how can you have predictable pricing? Do you limit the user upon reaching a certain threshold?

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.

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!