The Best AI is Built Not Bought
Applied Compute is the cloud for model training, inference, and continuous improvement. Our researchers work with you to transform your data and expertise into frontier intelligence no one can buy.
"The training recipe was precisely tailored to our product and use case. Applied Compute's approach to dataset, environment, and harness construction gave us confidence that the model would perform well in the production setting with real users."
Moritz Stephan
Engineering Lead, Cognition
"AI has long been core to how DoorDash helps merchants succeed. By encoding our quality standards directly into model training, we scaled our internal expertise and raised the bar on menu accuracy on the platform."
Andy Fang
CTO at DoorDash
"Our support team's judgment is the hardest thing to encode: subtle differences between failures, when to escalate, what's safe to surface. Applied Compute built an agent that learns that judgment from our SMEs in production, so our expertise compounds with every ticket instead of living in a few people's heads"
Zach Abrams
Co-Founder, CEO at Bridge
"Applied Compute achieving frontier capabilities on APEX Agents with just 2,000 tasks is incredible."
Brendan Foody
Co-Founder, Co-CEO at Mercor
"The training recipe was precisely tailored to our product and use case. Applied Compute's approach to dataset, environment, and harness construction gave us confidence that the model would perform well in the production setting with real users."
Moritz Stephan
Engineering Lead, Cognition
"AI has long been core to how DoorDash helps merchants succeed. By encoding our quality standards directly into model training, we scaled our internal expertise and raised the bar on menu accuracy on the platform."
Andy Fang
CTO at DoorDash
"The training recipe was precisely tailored to our product and use case. Applied Compute's approach to dataset, environment, and harness construction gave us confidence that the model would perform well in the production setting with real users."
Moritz Stephan
Engineering Lead, Cognition
Build the AI that no one can buy
Frontier performance, scalable economics
Train models on your data to improve quality, reduce latency, and lower token spend without giving up control.
Train it. Serve it.
Improve it.
Our team of frontier lab veterans and applied research engineers help you train custom models on your data, deploy them at scale, and improve them continuously.
Scalable infrastructure for better, faster, cheaper models.
Post-train tool-using, long-horizon agents.Train across text, images, code, and structured data on a state-of-the-art stack using your own harness and graders.
Optimize your models towards your evals.Calibrate rewards to your product KPIs and domain expertise. Inspect what your model is learning with rollout-level observability.
Stay model-flexible.Train from the best available base models, then upgrade as stronger ones ship without changing your harness, data pipeline, or deployment stack.
Flexible, production grade inference in seconds
Serve in the harness you trained in.Move from training to production with zero train-to-deployment mismatch.
Optimize for any production workload.Configure deployments around the throughput, latency, and concurrency your product needs, from asynchronous agents to real-time user experiences.
Promote checkpoints to production instantly.Deploy your latest model as soon as it is ready, with replicas that spin up in seconds.
Models that get better the more they are used
Transform real usage into training data.Mine training data and reward signals directly from production traces to continually improve your model.
Improve deployments with real-time training.Attach your production inference endpoints to a training runtime. Update deployments online as real user feedback comes in using online RL and self-distillation.
Fearlessly roll out deployments with monitoring and observability.Directly run A/B tests with release candidates, and roll out production model updates frequently and with confidence.
One platform to train, run, and improve the model you own
Developer SDK and toolkit
Training, inference, sandboxes, evals, memory, and agent serving through one SDK. Compatible with any agent framework or custom harness.
Developer SDK and toolkit
Training, inference, sandboxes, evals, memory, and agent serving through one SDK. Compatible with any agent framework or custom harness.
Observability to shape model improvement
Every rollout is traced and analyzed. Automated detection of reward hacking, regressions, and edge cases.
Inference, in our cloud or yours
High-throughput, low-latency serving via dedicated, single-tenant deployments in any region. Optionally record production traffic as signal for training future iterations of the model.
Flexible deployment, one control plane
Run serverless on our cloud or fully in your own VPC, all from a single control plane.
Elastic compute on demand
Scheduling, queuing, and fault tolerance are handled automatically. Scale from prototype to frontier runs without managing a cluster or GPU uptime.
Any model, any scale
Open-weight models from 1B to 1T+ parameters. Swap models without rebuilding your stack.
Deployed with you from day one
Our team has pioneered RL post-training, long-horizon agents, and continual learning at frontier labs.
Embedded
We sit with your engineers, from co-designing the first eval, to deploying the model and beyond.
Managed
Your team designs and runs experiments. We manage the training, serving, and infrastructure load.
Your data. Your model. Your edge.
Built for the most security-conscious customers in AI, enterprise, and the public sector.
SOC 2 CertifiedData never leaves your perimeter
Choose serverless or a VPC deployment based on the control you need.
Full access and audit
Role-based access control, audit logs on every dispatch, lifecycle automation for checkpoints, datasets, and artifacts.
You own the intelligence
The model is yours. You decide what trains, what deploys, and what memory is retained. Opt out of training any time.
News and Research
Continued Training with Entropy Preserving RL
Training an Agentic Router for Optimal Cost-Performance on SWE Tasks
Bringing Capabilities in Distribution via Relevance-Masked Self-Distillation