Three ways
to run.
Pay for usage, reserve compute, or build private infrastructure. Rates as published at llmnow.ai; plans and examples below.
Managed Inference
Usage-based model access.
Shared Pod · Performance Pod- APIs
- Variable demand
- Experimentation
GPU Pod
Dedicated or reserved compute.
Performance Pod · Dedicated Pod- Predictable workloads
- Custom models
- High throughput
Enterprise
Private infrastructure.
Enterprise Pod- Security
- Regional requirements
- Large-scale capacity
- Custom SLA
Compare the operating model.
Different tools solve different problems. Compare deployment control, data placement and operating responsibility before comparing a headline price.
| Capability | LLMPods | OpenAI API | Runpod | Self-hosted |
|---|---|---|---|---|
| Model access | Open-weight catalog; custom deployments by agreement | OpenAI API model catalog | Custom GPU workloads and public model endpoints | Your choice of weights and serving stack |
| Data placement | Türkiye today; Frankfurt and Canada planned | Project-level residency options; eligibility and scope apply | Depends on the selected deployment and product | Your infrastructure and connected services |
| Operating responsibility | Managed inference; isolation options by agreement | Provider-operated API serving | Managed endpoints or customer-operated GPU containers | You maintain capacity, updates and availability |
| Commercial model | Usage or reserved capacity; TRY invoicing where agreed | Usage-based API pricing | Credit-based billing; compute and storage charges | Hardware or cloud spend plus your operations cost |
Scroll sideways to compare providers →
Feature summary, not a performance benchmark. Options vary by plan and configuration. Sources checked 5 October 2026.
FX risk on every invoice.
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.
Illustration only: $100 of usage at hypothetical USD/TRY rates of 30, 40 and 50. Excludes taxes and bank fees; these are not current exchange rates.
When one Pod
isn't enough.
Reserve the infrastructure underneath your AI workloads.
One model, one GPU allocation.
Replicas that share routing.
Pods locked into one chassis.
Racks behind a private boundary.
- Dedicated GPU clusters
- Private networking
- Reserved capacity
- Regional architecture
- Custom model serving
- Technical account support
- Enterprise SLA
- Custom agreements
Operate Pods
like infrastructure.
A command-line view of the same system. Conceptual UI: commands shown are illustrative until the CLI ships.
You already know
the API.
https://api.llmpods.com/v1/chat/completions
{ "model": "llama-70b-instruct", "stream": true, "messages": [ { "role": "user", "content": "Hello, Pod." } ] }
{ "id": "chatcmpl-7f39a21", "object": "chat.completion", "model": "llama-70b-instruct", "choices": [ { "index": 0, "message": { "role": "assistant", "content": "Hello." }, "finish_reason": "stop" } ], "usage": { "total_tokens": 15 } }
data: {"delta": {"content": "Hel"}} data: {"delta": {"content": "lo"}} data: {"delta": {"content": ","}} data: {"delta": {"content": " Pod"}} data: [DONE]
Give your AI
somewhere better to run.
- OpenAI Compatible
- Regional Compute
- Dedicated GPU
- Managed Infrastructure