Oxlo.ai
Scale across AI models without scaling your bill
880 followers
Scale across AI models without scaling your bill
880 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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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?
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?
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?
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!
Do you normalize responses across providers, or do developers still have to handle each model?
This is really wonderful. But how can you have predictable pricing? Do you limit the user upon reaching a certain threshold?