Ship the LLM feature, not an infra project
A product team adding an LLM feature does not want to become an MLOps team. Meshive gives you an OpenAI-compatible endpoint in minutes, on hardware you can see and a bill you can predict.
Your client code does not change
vLLM and SGLang both come up OpenAI-compatible. Point base_url at Meshive and the rest of your integration stays exactly as it was.
Traffic decides pod or Serverless
Steady traffic earns its own dedicated GPU. A feature still finding its audience runs on Serverless and costs nothing between calls.
No infra hire to keep it running
Health checks, restarts and the CUDA stack are Meshive's job. Your team owns the prompt and the product, not the fleet.
The technical side of this work
Product teams shipping LLM features runs on Meshive's Inference serving setup — templates, model packs and the exact workflow.
Serve a model, not a fleetCards that suit this work
Every pod gets a whole, non-virtualized GPU. Pick by the model you are serving and the traffic it actually sees.
- NVIDIA L40SThe default once the feature has steady daily traffic
- H100 SXM5When one mid-range card stops keeping up with requests
- RTX PRO 6000 BlackwellLong prompts and RAG context that will not fit elsewhere
- NVIDIA L4Embeddings and rerankers beside the main model
Three steps
- 1Pick the model and the card — an L4 for embeddings, an H100 for a 70B chat model.
- 2Launch the vLLM or SGLang template and point your client at the endpoint.
- 3Move to Serverless once the feature is live and traffic is unpredictable.
Other teams on Meshive
Pick a card and start the pod
Sign up, choose the GPU, and the pod is yours in under two minutes. It bills by the hour and stops when you stop it.
Already a user? Invite friends and earn 15% of their first top-up.