Geodd now supports ByteDance’s Seed, Seedream, and Seedance model families for language and agent workloads, image generation, and AI video generation. The models are available through Geodd’s production inference platform.
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Geodd is a GDPR-ready AI inference provider focused on data minimization and secure processing. It does not store standard API prompts, outputs, request/response bodies, or customer datasets, and API data is not used for training or human review.
Geodd is partnering with Opper AI to make Geodd’s inference infrastructure available through the Opper AI gateway.
Geodd EU serverless inference is now live, starting with GPU infrastructure hosted in Norway. EU customers can now run supported models through Geodd with inference traffic served within the EU region, helping teams meet data residency, regional
Gemma 4 31B IT is available on Geodd for teams evaluating 31B-class open-weight inference. It is a fit when the workload needs stronger reasoning, coding support, long-context handling, multimodal capability, or instruction-following quality that
DeepSeek-V4-Flash is now available on Geodd, based on Geodd-provided product information. It is relevant for teams evaluating DeepSeek-V4-Flash for production inference where workload fit, latency, throughput, cost behavior, context length, and
Inference infrastructure with direct engineering support means the provider does more than supply GPUs, model endpoints, or a support queue. Engineers are involved in deployment, monitoring, scaling, incident response, workload tuning, and
A dedicated GPU gives your team reserved GPU compute. Dedicated AI inference gives your team a dedicated or isolated inference environment that may include serving runtime, monitoring, scaling, optimization, and support, depending on provider scope.
The total cost of self-hosted inference is not only the hourly GPU price. It includes GPU capacity, supporting compute, storage, networking, orchestration, observability, engineering time, runtime tuning, scaling, incident response, and the cost of
Serverless inference is usually the better fit for variable, early-stage, or unpredictable workloads where teams want managed API access without provisioning inference infrastructure. Dedicated inference is usually the better fit when workloads are