OctoAI (now NVIDIA)

https://octo.ai/

Inference cloud spun out of OctoML — acquired by NVIDIA in 2024. Originally TVM-compiler heritage; offered API for OSS LLM/diffusion serving.

At a glance

Type
GenAI inference (acquired)
Tier
T2
Created
2019 (as OctoML); rebrand 2023
Latest release
searched not found
License
searched not found
Pricing
searched not found
Funding
Acquired by NVIDIA in 2024-10 (terms undisclosed; reportedly ~$250M); previously raised $135M total through Series C 2023-12 (Tiger Global led).

Taxonomy

storage
n/a
retrieval
n/a
persistence
n/a
update
n/a
unit
n/a
governance
n/a
conflict
n/a

When to use

Optimised for: searched not found

Anti-fit: searched not found

Pros & cons

Pros

searched not found

Cons

searched not found

Claims & capabilities

Acquired by NVIDIA 2024-10.

Technical surface

API surface
searched not found
Backend storage
not applicable — not a memory product
Deployment
searched not found
Embedding model
not applicable — not a memory product
Multi-tenancy
searched not found
MCP
searched not found
A2A
searched not found
OpenTelemetry
searched not found

Similar systems

Other inference platforms & gateways in the catalog, ranked by inbound references.

  • LiteLLM T1

    BerriAI's open-source LLM gateway — unified OpenAI-format API for 100+ providers; LiteLLM Proxy adds budgets, fallbacks, observability, rate-limiting.

  • vLLM T1

    Open-source LLM inference engine with PagedAttention — high-throughput batching, paged KV-cache. UC Berkeley origin; de-facto OSS inference stack.

  • Anyscale T1

    Commercial platform built on Ray — distributed training, fine-tuning, serving. Anyscale Endpoints provides hosted OSS inference; ray cluster is the substrate underneath OpenAI's training stack.

  • Baseten T1

    Inference platform for ML models — Truss package format, Chains for multi-model workflows, autoscaling GPU/CPU serving. Targeted at production teams shipping LLM/diffusion endpoints.

  • Fireworks AI T1

    Fast inference platform for OSS LLMs — custom Cuda kernels, speculative decoding, multi-LoRA serving. Targets latency-sensitive production use cases.

  • Modal T1

    Serverless cloud for AI/ML — Python-decorator workflow defines GPU/CPU functions deployed to managed infra; widely used for training jobs, batch inference, and agent backends.

Row last verified 2026-05-14. Catalog data is CC-BY-4.0 — see how to read this.