Oxlo.ai

Scale across AI models without scaling your bill

API

Developer Tools

Artificial Intelligence

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Congrats on the launch! Predictable pricing is a refreshing approach. With OpenRouter, Together AI, and other model gateways already in the market, what has been the biggest reason customers choose Oxlo.ai instead of existing providers?

Routing across 35+ models to control cost is smart, the billing problem with AI is real and most teams find out too late.

One thing I'm curious about though, how does Oxlo handle output consistency when switching between models mid-workflow? Because the cost saving only works if the cheaper model returns outputs in the same structure the next step expects.
A subtle difference in how DeepSeek vs GPT formats a JSON response can silently break a pipeline downstream.

Is there any normalisation layer that makes model-switching invisible to the rest of the stack?

Honestly the framing of "scale across models without scaling your bill" is such a clean way to put it, that's the exact pain I feel every time we add a new provider to our stack. The cost creep is real. Curious how you handle routing under the hood, do you auto-pick the cheapest model that can handle a given task, or does the user set the rules? Either way, nice work team.

If the pitch is scaling across models without scaling the bill, the obvious question is what Oxlo's margin looks like on the heaviest usage tiers, are you negotiating better rates with the underlying model providers at volume, or is the "predictable" pricing actually subsidized early on and likely to change once usage patterns stabilize?

Pick a model first, discover the bill later" is painfully accurate, so one API across 35+ models with predictable monthly pricing is a real pitch. Having DeepSeek V4 Pro, Kimi K2.6 and GLM 5 side by side for comparison is genuinely useful for routing by use case. My question: when I compare models on the same prompt, do you surface latency and cost per call next to quality, or is calibration more manual right now?

The API angle is useful, but the boring hard part is usually auth edge cases and retries. Curious if Oxlo generates tests/error handling too, or starts with happy-path connectors first?

Do you normalize responses across providers, or do developers still have to handle each model?

Congrats on the launch! Scaling frontier AI models through a single API sounds incredibly useful, especially for keeping track of costs. I can definitely see how valuable this is for teams trying to build quickly without breaking the bank.

I'm curious about how the dashboard works for tracking bills: does it give you a real-time breakdown of which specific models are running up the highest costs?

Love what you guys are building here! 🙌

If you could convince a developer to switch to Oxlo AI in just one sentence, what would your pitch be? I'm curious what you consider your biggest competitive advantage.

Looks promising! Tested the Kimi integration—works as expected. My only concern is latency under throttling; it’s slightly higher than raw API calls, but the convenience of unified billing might be worth the trade-off for us. Curious to see how the privacy stack evolves. Good luck today!

For teams already locked into one provider, what does a typical migration to Oxlo.ai look like, and how long before they start seeing cost savings?

Congratulations on the launch Barath.

From a governance standpoint do you have any additional layer or just rely completely on what model provide out of the box.

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?