Oxlo.ai

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.

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?

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 core claim here is cost reduction across multiple models, but the interesting engineering question is where the savings actually come from. Routing calls to cheaper models based on task complexity is one approach, caching repeated or near-identical completions is another, and they have pretty different tradeoffs in terms of output consistency and latency. Curious which of those Oxlo is doing, and whether you have any control over the routing logic or whether it's fully automatic. Also wondering how this behaves when you're mixing models with different context window sizes or tool-calling implementations, since a lot of multi-model setups quietly break at that layer.

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).

Congrats on the launch! How does Oxlo.ai help teams compare model performance and cost before choosing which model to use in production?

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, do you plan to support Kimi 2.7 in the near future?

Congrats on the launch! The fixed monthly bill is the part that I like most here. We run an agent that fires a few hundred model calls per task and we know its the variance that wrecks budgeting, never the average

Clean positioning for developers. Feels like something built after actually dealing with real infrastructure pain, not just a surface level idea.

Big congrats 🙌 Oxlo.ai solves a real pain point. predictable costs while switching between models is a game-changer.

Feels like a good fit for people building agents or internal tools where you don’t want to constantly track token consumption. Congrats @megha_varshini and other makers! looks solid.

Hey Product Hunt! 👋Barath here, founder of Oxlo.ai.🎉 Launch Day OfferAs a thank you to the Product Hunt community, we’re offering an instant 10% discount on all subscriptions during launch day.Use code OXLOPH at checkout to claim it.We built Oxlo.ai because we saw a growing problem as AI agents moved from demos into production.When agents run continuously, usage becomes difficult to forecast. A successful agent does more than generate text. It reasons, calls tools, executes workflows, and serves real users. As adoption grows, infrastructure spend grows with it.We wanted teams to focus on building and scaling their agents, not worrying about whether next month’s AI bill would be 2x or 10x higher.🚀 What is Oxlo.ai?Oxlo.ai gives developers access to 35+ frontier AI models through a single OpenAI-compatible API and fixed monthly subscriptions.Built with a privacy-first approach, we never train on your prompts or access your data for model training. Developers can also compare models side by side and calibrate responses by adjusting model parameters before moving applications and agents into production.Instead of charging for every token consumed, we absorb usage variability and infrastructure complexity to give teams a stable monthly bill while running AI agents in production.💡 Who is it for?Teams building AI agents, copilots, AI employees, workflow automations, customer support agents, internal tools, and AI-powered products that need reliable model access at scale.⚡ Built for builders• OpenAI-compatible API• 35+ frontier AI models• Unlimited tool calls• Fixed monthly subscriptions• Privacy-first infrastructure• Compare models and calibrate responses before deploying• Built for production AI applications and agents🌍 Early tractionOver the past few months, Oxlo.ai has grown to more than 3,500 users across 100+ countries.Over the same period, we’ve continuously refined the platform through more than 20 product updates spanning onboarding, reliability, model access, and developer experience.🙏 We’d love your feedbackIf you’re building AI agents or deploying AI into production, we’d love to hear how you’re thinking about infrastructure, privacy, costs, and scaling.Me and the team will be around all day to answer questions.Happy hunting! 🚀