LLMPods provides regional GPU infrastructure for open-model inference. Choose deployment capacity around your model, workload and data requirements.
Explore models, deployment options and pricing
Latency, data location and invoice currency are infrastructure decisions. Make them part of your deployment, not an afterthought.
Shorter network paths can help a chat response start sooner. Türkiye-based compute keeps local workloads close; model size, queueing and prompt length still shape first-token time.
Measure first token, P50 and P95 from your application.
Know where prompts, outputs, logs and support data travel. Processing model workloads in Türkiye can help limit cross-border transfers, while lawful processing, retention and access controls remain essential.
Local hosting alone does not establish KVKK compliance.
If usage is priced in a foreign currency, the same workload can cost more in TRY when exchange rates move. Ask for the billing currency and conversion rule before you commit.
TRY billing is available where applicable; USD-indexed rates may still carry FX exposure.
Compute is hosted in Türkiye today. Frankfurt and Canada are planned locations, not currently available deployment regions.
Current compute location · TR-IST-01
Planned · no launch date announced
Planned · no launch date announced
Current compute is in Türkiye. Frankfurt and Canada are planned; requests from other markets can be served from Türkiye without moving the GPUs.
A shorter network route can reduce transport delay, but first-token time also depends on the model, queue and input size. Compare the same model and prompt from your actual users' location.
No. You still need to assess processing grounds, notices, retention, access and any transfers through integrations or support. Regional compute is one part of that assessment.
Not if the agreed price is indexed to USD or another currency. Confirm whether rates are fixed in TRY, which conversion date applies and whether taxes are included.
Every request crosses the same seven layers. Each layer reports what it did, so the path is never a black box.
Illustrative telemetry · demonstration data
LLMPods speaks the OpenAI schema. Point your existing SDK at a new base URL and keep the code around it.
Four deployment topologies on one runtime. The difference between them is how much of the machine is yours.
For elastic inference.
For predictable production workloads.
For isolated workloads.
For strategic infrastructure requirements.
Scale capacity around demand instead of provisioning infrastructure for peak traffic.
