Launching today
Oxlo.ai
Scale across AI models without scaling your bill
749 followers
Scale across AI models without scaling your bill
749 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.
Interactive
Free Options
Launch Team / Built With
Oxlo.ai
Hey Product Hunt! 👋
Barath here, founder of Oxlo.ai.
🎉 Launch Day Offer
As 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 traction
Over 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 feedback
If 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! 🚀
@barath_kanna_bk Many congrats on the launch, Barath. I liked the idea when you presented to me... having one API for 35+ models with predictable pricing will surely help the AI teams. Good luck with the launch! :)
Oxlo.ai
@rohanrecommends Thanks, Rohan, for the kind words.
It’s really heartwarming to hear this from an expert hunter like you.
@barath_kanna_bk Running a couple of agents in production, the bill was never the scary part for me, latency was. When you route across 35+ models behind one API, is the model picked per key or per request? And if it's per request, how do you keep the routing layer from adding a noticeable hop?
Oxlo.ai
@david_marko Good point. Developers explicitly select the model through an API field , so there is no hidden routing decision or model swap behind the scenes.
Our gateway adds only a lightweight routing step before forwarding the request to the selected model. We focus on keeping that layer lean and reliable, while model latency itself naturally varies by model and workload.
We however can commit to latency benchmarks for enterprise customers for whom performance is prime.
Out of curiosity, which models are you using for your production agents today?
The agent spend forecasting problem is what gets teams in trouble - you ship something that works, it starts getting real usage, and suddenly your AI infrastructure bill looks like a ransomware demand. We went through exactly this building agentic workflows - prototype costs look fine, then the agent starts doing multi-step reasoning chains at scale and the bill triples.
Quick question on the mechanics: when my agent makes a call, do I explicitly pick the model per request, or does Oxlo do any routing/optimization automatically? I'm guessing explicit control is better for quality guarantees, but curious whether you have any plans for cost-aware routing as an optional layer - like "use the cheapest model that meets this quality threshold."
Congrats on the launch - the fixed pricing angle is smart positioning for teams trying to get finance sign-off on AI infra.
Oxlo.ai
@galdayan Thank you Gal, you captured the problem really well.
Agent workloads are exactly where the forecasting issue becomes painful because a single user action can turn into multiple reasoning steps, tool calls, retries, and model calls behind the scenes.
On the mechanics, today developers explicitly choose the model per request. We believe that control is important, especially for teams that care about quality, latency, and predictable behavior in production.
That said, cost-aware routing is definitely part of the direction we want to move toward. The idea is exactly what you described: give teams an optional layer where they can optimize for cost, latency, or quality depending on the task, while still keeping the final control with the developer.
Our current focus is to make access predictable and reliable first. From there, smarter routing and optimization can become a powerful layer on top.
Out of curiosity, what kind of agentic workflows are you building, and how are you currently managing model selection and spend as they scale?
Foyer
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.
Oxlo.ai
@fberrez1 Great question.
Today, Oxlo does not automatically route requests or switch models behind the scenes.
Users control model selection directly through the model field in the API request. That means teams decide which model fits each task, whether they are optimizing for cost, latency, context length, tool-calling behavior, or output quality.
Our savings come mainly from optimized infrastructure, and keeping margins lean, not from silently sending requests to cheaper models.
We also avoid hiding model differences. Context windows, tool-calling behavior, and latency vary by model, so developers keep explicit control rather than relying on automatic routing that could break production workflows.
Out of curiosity, what does your model stack look like at Foyer today? Are you using different models for different tasks, or mostly standardizing on one provider?
One thing I've noticed with AI copilots is that the challenge isn't generating suggestions, it's earning enough trust for people to rely on them in their daily workflow. I like that Oxlo AI seems to focus on becoming part of the workflow instead of just another chat interface. That's a much harder problem to solve.
How do you know when users have started trusting Oxlo enough to rely on it every day?
Oxlo.ai
@harini_mukesh Thanks for the question!!
From our perspective, we believe that trust is earned when users starting using our APIs in production environments from their initial testing clusters.
Reliability, cost predictability and privacy are the foundations of trust. If developers can confidently build, compare models, and scale without worrying about outages, unexpected bills, or their data being used for training, Oxlo.ai becomes infrastructure they can depend on every day.
We are still early, but that is the standard we are building toward.
Jinna.ai
Congrats on the launch! I played with your calculator on the landing page for a while from my iPhone — good stuff but it is incredibly laggy. Worth fixing ASAP 🙌
What’s the secret in achieving the fixed price? It sounds unbelievable and there must be a ceiling.
Oxlo.ai
@nikitaeverywhere Thanks for flagging the calculator, Nikita. Our team will improve its mobile responsiveness and get that fixed soon.
We self-host the models, and our subscription plans include usage ceilings appropriate to each plan. We are not claiming to offer unlimited access for a small fee.
Our approach is to keep margins as lean as possible to make AI model access more affordable and encourage adoption. We aim to remain among the most cost-effective API options while maintaining a sustainable service.
ZeroGPU
Oxlo.ai
@its_maddy_a We scale instances as per demand, our Datacenter partners give us that flexibility to instantly scale instances based on real time load dynamics.
We have an internal AI agent that monitors load in real time and promptly lets our DevOps team know about the potential scaling requirements well in advance.
todai
Oxlo.ai
@umar_saleem Our model stack is been battle tested for running agents in production. so far our users have been happy about our uptime and latency.
We also offer dedicated GPU deployments with SLAs for enterprise customers, so reliability is ensured.
Curious, what model are you currently using at Todai.
Dune
In hardware, we never pick a component without optimizing the BOM (Bill of Materials) first, so the 'discover the bill later' problem in AI is a massive pain point we can completely relate to. I love the concept of routing through a single API to keep costs predictable.
I’m curious about the calibration and switching latency—when swapping between models like DeepSeek V4 Pro or a Llama model for different use cases under a single subscription, how do you handle response time consistency? Speed-to-action is everything for real-time interfaces. Massive congrats on the launch!
Oxlo.ai
@dhanrajchoudhary Thank you Dhanraj, that comparison with BOM optimization is exactly the kind of problem we are trying to solve for AI teams.
On latency, each model has its own performance profile, so we do not promise identical response times across every model. Developers select the model based on the task and their latency, quality, and cost requirements.
Oxlo.ai does not silently switch models within an active request. The API routes the request to the model selected by the developer, while our platform focuses on keeping the serving layer reliable and reducing unnecessary infrastructure overhead.
We also make it easier to compare models and calibrate parameters before deployment, so teams can identify the right balance of quality and speed for each use case.
For real-time interfaces, faster models can be used for interactive flows, while larger reasoning models can be reserved for tasks where response quality matters more than latency.
Really appreciate the thoughtful question and curious what kind of models you're using at Dune.