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.
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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.
Swytchcode
This is really wonderful. But how can you have predictable pricing? Do you limit the user upon reaching a certain threshold?
Oxlo.ai
@chilarai Thanks for the comment, Chilarai!
We offer two subscription plans for different stages of builders: one includes up to 1k calls / day, and the other includes up to 5k calls / day. These plans are designed for developers, solo builders, and early-stage teams.
For customers who need higher limits, we offer custom fixed price plans based on their historical usage. We commit to a fixed monthly price that is at least 15% lower than their current AI spend, with a usage ceiling of up to 1.5× their committed usage.
This gives teams room to grow while keeping their AI infrastructure costs predictable.
👋 Congrats on the launch, do you plan to support Kimi 2.7 in the near future?
Oxlo.ai
@wojtekszkutnik Thanks Wojtek.
Kimi K2.7 is coming up ver soon on Oxlo.ai.
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.
Oxlo.ai
@kiran_kadekoppa Thank you Kiran!
Today, we primarily rely on the safeguards and moderation capabilities provided by the underlying models. Our focus has been on providing a reliable, API layer with predictable pricing.
That said, as we onboard more enterprise customers, additional governance features such as centralized policies, usage controls, and organization-level guardrails are definitely on our roadmap.