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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Render Sandboxes: a computer for every agentGive every agent its own machine. Starts in under a second
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This is a real problem for anyone running agents in production. With 35+ models on a fixed subscription, models get updated and deprecated over time, and a silent point update can change a production agent's behaviour in ways that are hard to debug. Do you pin exact model versions, so teams can reproduce results and upgrade on their own schedule?
Congrats on the launch!
How does Oxlo.ai help teams compare model performance and cost before choosing which model to use in production?
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?
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).
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 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.
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?