Oxlo.ai
Scale across AI models without scaling your bill
936 followers
Scale across AI models without scaling your bill
936 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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Congrats on the launch, @barath_kanna_bk Predictable pricing for AI infrastructure is a massive pain point solved.
qq. on the fixed subscriptions: Is there a hard cap where requests throttle, or do you have a soft limit that triggers an upgrade prompt? Def checking this out today.
@vikramp7470 Thanks for the comment Vikram.
We have a soft limit which sends warnings and upgrade prompts in advance in case the request limits are reached so users can promptly upgrade their plans to stay up always.
Nice, A soft limit definitely makes for a better user experience.🙌🙌
@vikramp7470 Thanks Vikram, Please try out the portal and let us know how it goes!!
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?
@angelika_dev Good question.
Users control model routing directly through the model field in the API request. We do not automatically switch, update, or route models on their behalf, so teams retain full control over the model they use in production.
@barath_kanna_bk Thanks Barath. I think I muddled it. I get that I pick the model and you don't reroute. What I meant is version pinning within a single model. On OpenAI or Anthropic you can point at a dated snapshot, so a silent update doesn't shift your outputs. With the open models you serve, if the provider ships a new build of say Qwen or GLM, does my pinned choice stay on the exact version I tested, or do I move to the new one automatically?
@angelika_dev Ah, understood. You mean version pinning within the same model family.
We do not silently replace a model with a newer generation under the same model ID. When a new release becomes available, we add it as a separate option, so users can keep using the version they have already tested and move to the newer one on their own schedule.
For example, Kimi K2.5 and Kimi K2.6 are available as separate models on Oxlo. The same approach applies to other major model releases.
That gives production teams control over when they upgrade, rather than changing their agent’s behavior without notice.
Congrats on the launch!
How does Oxlo.ai help teams compare model performance and cost before choosing which model to use in production?
@pt_tango_ag Hi Przemek,
We have a dedicated section in the Oxlo.ai playground to test, compare and calibrate each model that we have before deploying it in your agent / app. This also helps to evaluate how tokens consumed by different models for the same prompt for very accurate calibration and fine tuning!!
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?
@martin_mo Thank you Martin!
Since you’re running an AI image generator, you probably know this pain very well.
Today, routing is controlled by the developer. Users explicitly choose the model through the API request, so we don’t silently switch models based on cost.
We think quality, latency, and cost all matter differently depending on the workflow, so we want teams to keep that control. Over time, optional routing based on cost, latency, or quality is definitely something we want to explore.
Curious what models you’re using for image generation today?
@barath_kanna_bk "Makes sense — developer-controlled routing gives more predictability, especially when quality/latency/cost trade-offs vary so much by use case. On my end I'm running a tiered setup with a mix of fast open-weight models, picked mainly to balance speed and cost on an indie budget. I imagine once you explore optional cost-based routing, the hardest part will be setting defaults that don't surprise users — curious if you're leaning toward per-project rules, or something more global at the account level?"
@martin_mo We’re still exploring that space, but we’re leaning towards per-project rules rather than account-wide defaults. Different applications often have very different requirements, so giving developers control at the project level feels more flexible and less surprising.
That said, it’s still early and we’re speaking with developers to understand what would actually be most useful before building it.
Which open-weight models have worked best for you so far?
@barath_kanna_bk yeah per-project makes more sense tbh, way less surprising than some global toggle silently deciding for you. for me it's mostly fast open-weight diffusion models, sub-5s gen time — people just bail if a consumer tool takes too long. still tuning the speed/quality balance honestly. what are you hearing more from devs you talk to, speed or quality being the bigger complaint?
@barath_kanna_bk Per-project rules makes a lot of sense — totally agree that one-size-fits-all defaults would be messy given how different the requirements are per app. On the model side, I've had good results with a mix of open-weight diffusion models, leaning toward the faster ones (sub-5-second generation) since speed-to-output really matters for a consumer-facing tool where people bail if they wait too long. Still tuning the balance between speed and fidelity depending on the tier. What's been your experience — do most teams you talk to lean more toward speed or quality as the dominant constraint?
@martin_mo That’s interesting, and I completely agree. For consumer-facing applications, latency often matters just as much as quality.
From the conversations we’ve had, it really depends on the use case. Coding assistants and reasoning-heavy agents usually prioritize quality, while chatbots, automation, and consumer apps tend to favor speed and responsiveness.
It’s one of the reasons we expose multiple models rather than trying to pick one “best” model for everyone.
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).
@matheusdsantosr_dev Thanks for the feedback Matheus.
We will definitely add a request cancellation feature soon in the playground!!
Happy to explore synergies together!!
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!
@jared_salois Thanks Jared, That is true.
Most teams eventually standardize on a smaller set of models for their core production workflows.
The value of having 35+ models is strongest during evaluation, but it continues after that because the model landscape moves so quickly. New releases can suddenly be better at coding, reasoning, tool use, speed, or a specific domain.
We keep adding new models as they become available, while keeping existing major versions available separately. So teams can stay on the models they have optimized for, then test newer options without rebuilding integrations or moving to another provider.
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.
@sabber_ahamed We don't route user prompts, users decide which model they want to use for every prompt.
Savings come from self hosting and efficient management of resources and of course keeping our margins very thin to acquire customers :)