Oxlo.ai
Scale across AI models without scaling your bill
1.1K followers
Scale across AI models without scaling your bill
1.1K 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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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?
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.
AgentKey
Oxlo.ai
@luki_notlowkey Thank you Luki!!
The biggest reason has been cost predictability. Most gateways still bill per token or per provider, so as usage grows, the bill grows too.
With Oxlo, developers get access to frontier models and a fixed monthly subscription with defined usage limits. That makes it much easier for startups and small teams to budget their AI infrastructure while still having the flexibility to choose the right model for each task.
We’re still early, but that’s the feedback we’ve consistently heard from users.
Congrats on the launch. Request-based pricing is a really interesting way to charge. But what if someone requests a large batch of work? How do you prevent someone from submitting a single request with a large batch of work to game the flat rate?
Oxlo.ai
@xinrui1 Thanks Xinrui!
We prevent that by using request weighting. Each request has input and output token limits, and if those limits are exceeded, it’s counted as multiple requests rather than one. That keeps usage fair while still giving developers predictable pricing without unexpected bills
@barath_kanna_bk Thank you for the reply! This is a smart method!
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!
Oxlo.ai
@lana_wang Thank you for trying it out and for the honest feedback!
There is a small overhead from our gateway, but we’ve worked hard to keep it minimal. We’ll continue optimizing the serving stack as we scale.
On privacy, that’s one of our core priorities. We never train on customer data, and we’re continuing to strengthen the platform with more privacy and enterprise-focused capabilities over time.
Really appreciate you taking the time to test Oxlo.ai, and we’d love to hear any other feedback as you continue using it.
Mailwarm
Do you normalize responses across providers, or do developers still have to handle each model?
Oxlo.ai
@thamibenjelloun Developers interact with a single OpenAI-compatible API, so the request and response format is normalized across all supported models. That means switching between providers typically only requires changing the model field rather than rewriting application logic.
Of course, each model still has its own strengths, latency, context window, and capabilities, so those differences remain for developers to choose based on their use case.