Inference without compromise

Deploy models on the infrastructure you choose, with predictable performance, reliable scaling, and full control.

Scale inference without limits

Start with hosted base models or move to dedicated endpoints for more control. Either way, Seekr ensures performance, scalability, and enterprise-ready reliability.

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Performance control

Tune throughput and latency by configuring endpoints to match your application’s needs.

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Instant access

Run hosted base models with auto-scaling, no manual setup required.

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Tailored for workloads

Configure inference to match domain-specific and application needs.

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Hardware freedom

Run on an array of hardware options including NVIDIA and AMD.

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Performance insight

Monitor latency, throughput, and reliability across endpoints.

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Enterprise-ready

Dedicated resources provide compliance and isolation at scale.

From selection to scale

Streamline the entire inference lifecycle, delivering consistent speed and resilient capacity for enterprise workloads.

Model selection

Choose the right starting point

Choose from our library of open-source models, including chat, instruct, vision, and domain-specialized options. Whether you need a compact model for cost-efficient tasks or a larger model for advanced reasoning, Seekr helps you align model choice with task complexity, domain requirements, and performance goals.

SeekrFlow AI platform model library
Model customization

Tailor models to your domain

Enhance base models with fine-tuning (Instruction, context-grounded, GRPO), extend capabilities with vector stores for Retrieval-Augmented Generation (RAG), or connect to SeekrFlow Agents for more complex workflows. This stage ensures the model is domain-specific, accurate, and enterprise-ready.

SeekrFlow model fine-tuning and customization options: context-grounded, GRPO, instruction
Dedicated deployment

Run on dedicated endpoints

Launch your customized model on a dedicated endpoint with reserved capacity. Choose from an array of hardware options—including NVIDIA A100/H100/H200 or AMD MI300X—to optimize for throughput, latency, and cost-performance balance. Dedicated endpoints deliver predictable speed, resilient capacity, and freedom from noisy-neighbor effects common in serverless setups.

SeekrFlow hardware selection for AI deployment
Monitoring & validation

Ensure ongoing reliability

Track inference performance with detailed metrics on latency, throughput, and reliability. Seekr gives you full visibility to debug issues, fine-tune parameters, and confirm models are production-ready.

SeekrFlow AI production monitoring metrics and data points

Secure by design

With Seekr, your data remains yours. We never use it to train other models and give you full control to install our platform wherever your data resides. Our SOC 2 Type II certification ensures best-in-class security, featuring fine-grained access controls and the flexibility to run on your preferred cloud or hardware.

Learn more

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Enterprise-grade accuracy. Faster value.

3x

more accurate model responses

6x

more relevance in responses

2.5x

faster data preparation vs others

90%

cheaper costs vs traditional approaches

30

minutes or less to build a production-grade LLM

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Top enterprise and government leaders trust Seekr

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“Seekr is setting a high bar for performant and efficient end-to-end AI development with its SeekrFlow platform, powered by AMD Instinct MI300X GPUs. We’re proud to work with Seekr as they showcase what’s possible using AMD Instinct GPUs on OCI’s AI enterprise infrastructure.”

Negin Oliver

CVP, Business Development – AI and Cloud at AMD

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FAQs

What is a dedicated endpoint?

A dedicated endpoint reserves hardware for your model, ensuring predictable latency and consistent throughput.

How are dedicated endpoints different from serverless inference?

Serverless inference shares resources; dedicated endpoints guarantee performance by isolating your workloads.

How do I choose model size?

Smaller models are faster and cheaper; larger models offer better reasoning. Match size to your task complexity.

What about memory requirements?

Larger models and batch sizes require more memory. Dedicated hardware ensures capacity without resource contention.

Can I monitor inference metrics?

Yes. Latency and throughput are tracked to validate model performance in production.

How do I handle spiky traffic?

Dedicated endpoints maintain reserved capacity, so sudden traffic spikes don’t affect performance or reliability.

Accelerate your path to AI impact

Book a consultation with an AI expert. We’re here to help you speed up your time to AI ROI.

Request a demo

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