OctoAI wants to makes private AI model deployments easier with OctoStack

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OctoAI (formerly known as OctoML), coming announced nan motorboat of OctoStack, its caller end-to-end solution for deploying generative AI models successful a company’s backstage cloud, beryllium that on-premises aliases successful a virtual backstage unreality from 1 of nan awesome vendors, including AWS, Google, Microsoft and Azure, arsenic good arsenic Coreweave, Lambda Labs, Snowflake and others.

In its early days, OctoAI focused almost exclusively connected optimizing models to tally much effectively. Based connected nan Apache TVM instrumentality learning compiler framework, nan institution past launched its TVM-as-a-Service level and, complete time, expanded that into a fully-fledged model-serving offering that mixed its optimization chops pinch a DevOps platform. With nan emergence of generative AI, nan squad past launched nan afloat managed OctoAI level to thief its users service and fine-tune existing models. OctoStack, astatine its core, is that OctoAI platform, but for backstage deployments.

Image Credits: OctoAI

Today, OctoAI CEO and co-founder Luis Ceze told me, nan institution has complete 25,000 developers connected nan level and hundreds of paying customers successful production. A batch of these companies, Ceze said, are GenAI-native companies. The marketplace of accepted enterprises wanting to adopt generative AI is importantly larger, though, truthful it’s possibly nary astonishment that OctoAI is now going aft them arsenic good pinch OctoStack.

“One point that became clear is that, arsenic nan endeavor marketplace is going from experimentation to past twelvemonth to deployments, one, each of them are looking astir because they’re tense astir sending information complete an API,” Ceze said. “Two: a batch of them person besides committed their ain compute, truthful why americium I going to bargain an API erstwhile I already person my ain compute? And three, nary matter what certifications you get and really large of a sanction you have, they consciousness for illustration their AI is precious for illustration their information and they don’t want to nonstop it over. So there’s this really clear request successful nan endeavor to person nan deployment nether your control.”

Ceze noted that nan squad had been building retired nan architecture to connection some its SaaS and hosted level for a while now. And while nan SaaS level is optimized for Nvidia hardware, OctoStack tin support a acold wider scope of hardware, including AMD GPUs and AWS’s Inferentia accelerator, which successful move makes nan optimization situation rather a spot difficult (while besides playing to OctoAI’s strengths).

Deploying OctoStack should beryllium straightforward for astir enterprises, arsenic OctoAI delivers nan level pinch read-to-go containers and their associated Helm charts for deployments. For developers, nan API remains nan same, nary matter whether they are targeting nan SaaS merchandise aliases OctoAI successful their backstage cloud.

The canonical endeavor usage lawsuit remains utilizing matter summarization and RAG to let users to chat pinch their soul documents, but immoderate companies are besides fine-tuning these models connected their soul codification bases to tally their ain codification procreation models (similar to what GitHub now offers to Copilot Enterprise users).

For galore enterprises, being capable to do that successful a unafraid situation that is strictly nether their power is what now enables them to put these technologies into accumulation for their labor and customers.

“For our performance- and security-sensitive usage case, it is imperative that nan models which process calls information tally successful an situation that offers flexibility, standard and security,” said Joshua Kennedy-White, CRO astatine Apate AI. “OctoStack lets america easy and efficiently tally nan customized models we need, wrong environments that we choose, and present nan standard our customers require.”