Forward-deployed inference
Ship more ambitious AI products, faster.
Our inference experts work alongside your team to evaluate models, build the serving system, and operate it in production.
What's included
An inference team for the work your product needs.
Forward-deployed inference combines the Boundless platform with hands-on product and engineering support. We take your workload from evaluation through production, then continue operating the serving environment after launch.
- Talk to an engineer01Model selection and evalsWe set the quality bar with you, evaluate viable open models, and choose the one that fits your product.
- Talk to an engineer02Serving-system designWe match the workload to the right hardware and engine, then tune precision, parallelism, batching, caching, and scheduling.
- Talk to an engineer03Benchmarking and deploymentWe establish the baseline, test viable configurations on the same workload, and deploy once the agreed threshold is met.
- Talk to an engineer04Operations and changesWe manage capacity, deployments, monitoring, and incident response, and make approved changes as the workload evolves.
Forward-deployed inference.
What Boundless manages
Inference engineered around your product.
Boundless benchmarks and configures models, runtimes, hardware, request handling, and capacity against your targets for quality, latency, throughput, reliability, and cost.
Model and quality
We turn your quality criteria into a repeatable evaluation, then compare viable models using representative inputs.
Our work touches:
- Model and version selection
- Evaluation design
- Quality thresholds
- Representative test sets
Compute and runtime
We match the workload to the serving system, then configure how the model runs across the available compute.
Our work touches:
- Serving-engine selection
- Hardware selection
- Quantization and precision
- Tensor and pipeline parallelism
Request path
We configure how requests move through the serving system under representative traffic.
Our work touches:
- Batch size and concurrency
- Scheduler policy
- Cache allocation
- Routing and overflow behavior
Production operations
We manage the serving environment after deployment and make approved changes as the workload evolves.
Our work touches:
- Capacity and scaling
- Canary deployments
- Monitoring and incident response
- Configuration and model changes