Oxlo.ai
Scale across AI models without scaling your bill
1.3K followers
Scale across AI models without scaling your bill
1.3K 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.
Interactive
Free Options
Launch Team / Built With
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!
Oxlo.ai
@jared_salois Thanks Jared, That is true.
Most teams eventually standardize on a smaller set of models for their core production workflows.
The value of having 35+ models is strongest during evaluation, but it continues after that because the model landscape moves so quickly. New releases can suddenly be better at coding, reasoning, tool use, speed, or a specific domain.
We keep adding new models as they become available, while keeping existing major versions available separately. So teams can stay on the models they have optimized for, then test newer options without rebuilding integrations or moving to another provider.
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.
Oxlo.ai
@sabber_ahamed We don't route user prompts, users decide which model they want to use for every prompt.
Savings come from self hosting and efficient management of resources and of course keeping our margins very thin to acquire customers :)
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?
Oxlo.ai
@arjayyy Switching is very easy since all providers use the Open AI API format, Users can switch by just changing a couple of API fields and it takes under 5 minutes.
Cost savings are also instant as you pay for a full month upfront.
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?
Oxlo.ai
@xiaosong001 Thanks! Oxlo.ai is purely the backend API layer, so authentication, retries, and error handling remain under the developer’s control.
Our focus is to provide a reliable model access, and predictable pricing. Since the API surface is standardized across providers, integrating and switching models is much simpler.
Out of curiosity, what are you building? Are you working on an AI product or an agent framework?
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?
Oxlo.ai
@jennifer_lyu Thank you Jennifer!!
At the moment, calibration is more manual. You can compare model outputs side by side, adjust parameters, and evaluate which model best fits your use case.
Since you’re on a fixed subscription, we don’t emphasize per-call cost comparisons. Instead, the focus is on response quality, latency, and finding the right model for your workload without worrying about the cost of trying different models.
Out of curiosity, what kind of applications are you building today?
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?
Oxlo.ai
@ansari_adin That’s a fair question.
Our focus as an early-stage company is user adoption rather than maximizing margins. We’ve designed our plans with fair usage limits so they remain sustainable, while keeping pricing as affordable as possible for developers.
As we grow, higher infrastructure volumes and better purchasing power will naturally improve our economics. Our goal is to pass a meaningful portion of those efficiencies back to customers rather than maximizing markups.
Out of curiosity, what kind of AI product are you building today?
@barath_kanna_bk Fair answer, though "fair usage limits" is the part that tends to quietly tighten once a company has paying customers locked in and needs the margin to survive. Would you be open to being specific now about what those limits actually are, or is that still being figured out as you see real usage data come in?
Oxlo.ai
@ansari_adin Absolutely. Our limits are already defined and are not something we’re planning to quietly tighten.
Today we offer two plans:
1,000 calls/day on the Pro plan
5,000 calls/day on the Premium plan
For larger workloads, we create custom fixed-price plans based on a customer’s historical usage. We typically commit to a fixed monthly price that’s at least 15% lower than their current AI spend, while providing around 1.5× headroom over their committed usage.
The idea isn’t to lock customers in and change the rules later. It’s to give teams predictable pricing with enough room to grow while keeping the service sustainable for everyone.
@barath_kanna_bk Makes sense, prioritizing adoption early and revisiting pricing as you scale is a reasonable sequence. Working on a few side projects in the AI tools space right now, nothing live yet worth mentioning.
Oxlo.ai
@ansari_adin Thanks Ansari, I appreciate that!
Best of luck with your side projects. If they end up using multiple models or AI agents, I’d genuinely love to hear how Oxlo fits into your workflow and what we could do better.
Feedback from builders like you is exactly what helps us improve.
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.
Oxlo.ai
@yibo_wang3 Thank you Yibo, really glad that resonated!
Today, users set the rules by explicitly choosing the model in each API request. We don’t automatically switch to a cheaper model or route requests behind the scenes, as we think developers should stay in control of quality and behavior in production.
That said, optional cost- or latency-aware routing is something we’d love to explore in the future for teams that want it. Out of curiosity, what kind of AI product are you building?