Tools for shipping AI systems
Decision guides for the layers around the model. These pages explain workflow fit and evaluation criteria; hands-on benchmarks will be added only after controlled testing.
RunPod
RunPod is one option for renting GPU capacity when a local workstation is unavailable, already occupied, or too small for a particular model. Mneme Labs has not published a controlled RunPod benchmark yet; this page explains where the service can fit and what to measure before committing a production workflow.
Read the decision guide →Voice generation and dubbingElevenLabs
ElevenLabs can provide the voice layer in a content pipeline: narration, multilingual dubbing, previews, and programmatic speech generation. The useful engineering work is not typing text into a voice box; it is controlling scripts, pronunciation, consent, revisions, file naming, and final review.
Read the decision guide →Cloud servers and application infrastructureVultr
Vultr can fit teams that want control of a conventional server, API, worker, or containerized AI application. That control comes with operational responsibility: patching, access, observability, backups, cost limits, and a clear recovery plan.
Read the decision guide →Approachable app and API deploymentDigitalOcean
DigitalOcean is a reasonable starting point for developers moving an AI application from a laptop to a public service. The platform does not remove architecture decisions, but its product surface can make the first deployment easier to understand than assembling every cloud primitive separately.
Read the decision guide →Compare the complete stack
See how local compute, rented GPUs, voice generation, servers, and app hosting fit together.
Open the AI infrastructure guide →