One OpenAI-compatible API for the best open models. Switch in a single line, pay per token, and keep your data private. Start free with $5 in credits.
# one endpoint, your key, any model curl https://api.cloud.baysn.ai/v1/chat/completions \ -H "Authorization: Bearer $BAYSN_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "MiniMax-M2.7", "messages": [{"role":"user", "content":"Summarize this support ticket"}] }'
from openai import OpenAI client = OpenAI( base_url="https://api.cloud.baysn.ai/v1", api_key=os.environ["BAYSN_API_KEY"], ) resp = client.chat.completions.create( model="MiniMax-M2.7", messages=[{"role":"user", "content":"Summarize this support ticket"}], ) print(resp.choices[0].message.content)
import OpenAI from "openai"; const client = new OpenAI({ baseURL: "https://api.cloud.baysn.ai/v1", apiKey: process.env.BAYSN_API_KEY, }); const resp = await client.chat.completions.create({ model: "MiniMax-M2.7", messages: [{ role: "user", content: "Summarize this support ticket" }], }); console.log(resp.choices[0].message.content);
Get started
If your code already talks to OpenAI, it already talks to Baysn. Here's the whole flow
Register with a few details. We set up your account and email your key, usually within 24 hours
Point base_url at api.cloud.baysn.ai/v1 and drop in your key, the rest of your OpenAI code is unchanged
Use any model by name, pay per token, scale to zero, add dedicated capacity when traffic spikes
Model library
A curated set of frontier open models, quantized without quality loss and priced per million tokens. More added regularly
Why Baysn
Closed APIs lock you in and learn from your prompts. Self-hosting eats your quarter. Baysn gives you the best open models, served fast, kept private, and dropped in with one line of code.
OpenAI-compatible across chat, vision, embeddings, and tool calls. Keep your SDK, your prompts, and your evals. Point the base URL at Baysn and your bill drops. No rewrite, no lock-in.
A curated set of frontier open models on a tuned serving stack. Low time-to-first-token, high throughput, and no 200-model junk drawer to dig through.
Private by default. Start on isolated serverless, then move to dedicated or fully air-gapped capacity that is yours alone. Your traffic is never used to train any model.
Dedicated, isolated capacity trusted for compliance-restricted, private, and air-gapped deployments. Inference you can put in front of a regulator, not just on a roadmap.
Without Baysn vs with Baysn
What it takes to ship AI the old way, and what it takes with Baysn
Deployment modes
Begin per-token in minutes, then move to dedicated capacity or batch when your workload settles, same models, same API
Integrations
The API is OpenAI-compatible, so Baysn works out of the box with the frameworks, editors, and gateways your team already runs, no glue code
Pricing
Priced per million tokens, input and output billed separately. New accounts start with $5 in free credits
Need a custom fine-tune, a private model, or a committed-volume rate? Dedicated inference capacity is quoted per GPU-hour with short commitments and private deployment options
Get started
Tell us where to send it. We set up your account and email your API key and console access, usually within 24 hours. New accounts start with $5 in credits.
Thanks. A Baysn engineer will set up your account and email your API key and console access, usually within 24 hours.
Want to talk through dedicated or private capacity first? Reach us any time at inference@baysn.ai.
Inference runs on our GPU cloud. If you'd rather rent the GPUs and run your own stack, training or custom serving, start one layer down with Baysn GPU Cloud
Questions
Yes. Point the OpenAI SDK's base_url at api.cloud.baysn.ai/v1, drop in your Baysn key, and call any model by name. Chat, vision, embeddings, streaming, and tool calls all work without code changes
On serverless your traffic is isolated and never used to train any model. For stricter needs, run on dedicated inference capacity that's yours alone, your own region, on-prem, or fully air-gapped, and your data stays inside it for the entire pipeline: request, processing, response, and logs
A tuned serving stack, speculative decoding, FP8 quantization, and continuous batching on high-performance accelerators, with models served close to your region. We optimize for the metrics that matter to you: low time-to-first-token and high throughput per dollar
Roughly past ~10,000 sustained requests per day, dedicated capacity on a per-GPU-hour rate usually beats per-token pricing. You can start serverless to validate, then move to dedicated with the same models and API when your volume settles
Yes. Deploy a fine-tune or a custom container on dedicated inference capacity with autoscaling and observability. Talk to us and we'll have you running on your timeline
GPU Cloud rents you the machines to run whatever you want. Inference is the managed product on top, you call a model through an API and we handle the serving, scaling, and optimization. Same company, two ways to buy
Inference 101
Clear guides from our own team, start here, then come build. No fluff, no sign-up wall
Training vs inference, and why inference is what powers every real-time AI app you ship
How a managed inference API works under the hood, and when to use it over self-hosting
When to use per-token serverless, reserved dedicated capacity, or a fully private deployment
The compute layer underneath inference, what GPU as a service is and how to choose
Register for access today, or talk to us about dedicated and private capacity