Reliable, fast inference for biology data workloads.
Inference that's 2x faster, 50-80% cheaper, and no refusals on biology workloads you need done.
5M+
Effective context
Every sample, log and paper in one run.
Biology is a data problem.
One analysis can mean thousands of samples, gigabytes of logs and hundreds of papers. Agents that run pipelines and read the results burn tokens at a scale few workloads match.
Pipeline runs and debugging
Run Nextflow and Snakemake workflows and fix them when a step fails.
Variant analysis
QC, calling and annotation across a whole cohort, with every filter recorded.
Single-cell analysis
Cluster, annotate and iterate on parameters until the cell types hold up.
Literature and database review
Read papers and query public databases with the hypothesis still in view.
Decisions made early carry all the way through.
Pipelines carry decisions forward: the reference build, the filters, the excluded samples. Lose them from context and the agent reruns the cohort with the wrong settings.
More results per day, and results that hold up.
Research leads care how fast results come back, whether they hold up, and where the data lives.
Same-day turnaround
Long analyses return sooner, so the next experiment starts while the question is fresh.
Accuracy on long runs
Less stale context gives the model a cleaner view across thousands of samples.
Nothing stored
The API keeps token counts for billing and nothing else. Your data is never stored.
Requests that don’t get refused
Closed models can flag or refuse pathogen and genomic sequence requests. Open-weight models like GLM and DeepSeek analyze them as-is.
Who builds this on Subconscious.
Biotech and pharma R&D
Running analysis agents on their own sequencing data.
Genomics cores and labs
Processing cohorts for many research groups at once.
Bioinformatics platforms
Adding agents that run pipelines inside their products.
Get started in minutes.
Every model is served in both the OpenAI and the Anthropic format. Point the agent you already have at Subconscious.
API format
Language
Endpoint
https://api.subconscious.dev/v1/chat/completions
Model
subconscious/glm-5.3-marathon
Works with
OpenAI SDK
Intact
Tool calls, streaming, structured output
from openai import OpenAI
client = OpenAI(
base_url="https://api.subconscious.dev/v1",
api_key="YOUR_API_KEY",
)
response = client.chat.completions.create(
model="subconscious/glm-5.3-marathon",
messages=[
{
"role": "user",
"content": "Refactor the billing module and update every affected test.",
}
],
)
print(response.choices[0].message.content)Questions
- Safety classifiers in closed models sometimes refuse or reroute legitimate research, like pathogen and genomic sequence analysis. The open-weight models we serve, GLM and DeepSeek, run this work without refusals.
- We do not store your prompts or completions. The API keeps only token counts for billing. Teams with stricter rules can also deploy on their own infrastructure.
- The API serves GLM-5.3 Marathon and DeepSeek V4.1 Flash Marathon in both the OpenAI and Anthropic formats. Dedicated deployments can serve virtually any open model, including your own fine-tunes.
- Yes. Start on the Subconscious API, then move to a dedicated deployment in our cloud or on your own GPUs. The runtime is a drop-in replacement for vLLM or SGLang, and our engineers can install and tune it inside your cloud.
Need it on your own GPUs?
The same runtime runs as a dedicated deployment in our cloud or on your own hardware, as a drop-in replacement for vLLM or SGLang.