Improbable vs OctoAI

Neither company has publicly disclosed a valuation, making a definitive size comparison difficult. Improbable employs 500-1000 people, while OctoAI has 100-500 employees.

Improbable has raised more in total funding at $700M, compared to OctoAI's $132M — a gap of $568M. Combined, the two companies have completed 3 known funding rounds.

Improbable leads with an Awaira Score of 68/100, while OctoAI sits at 65/100. That 3-point gap reflects real differences in funding, scale, and traction — it's not a vanity metric.

Improbable was founded by Herman Narula in 2012. OctoAI was founded by Luis Ceze in 2019. Visit each company's profile on Awaira for a full founder biography.

Improbable: Improbable develops large-scale simulation technology and virtual world infrastructure, originally focused on cloud-distributed game server simulation and subsequently pivoting to AI-powered synthetic environment generation and metaverse platform development. The London company built SpatialOS, a cloud platform for running distributed simulations of large, persistent virtual worlds, and has applied simulation capabilities to defence, urban planning, and entertainment applications.\n\nThe company raised approximately $700 million including a landmark $502 million SoftBank round in 2017 that was one of the largest venture rounds in European technology history. Improbable has undergone multiple strategic pivots, moving from gaming infrastructure to defence simulation contracts with clients including the UK Ministry of Defence, which uses simulation environments for training and wargaming. The company also built MSquared, a metaverse interoperability network, before refocusing on defence and AI simulation work.\n\nImprobable operates in an AI simulation market where demand from defence agencies, autonomous vehicle companies, and robotics firms has grown substantially. The company competes with simulation platforms from Epic Games (Unreal Engine), NVIDIA (Omniverse), and specialist defence simulation vendors. Its SoftBank backing and large cash reserves have enabled it to survive multiple market pivot cycles that would have ended less well-funded companies, and its current focus on AI-powered synthetic data generation for defence represents a growing government budget category. OctoAI: OctoAI delivers a machine learning acceleration and model serving platform that enables teams to deploy, scale, and optimize AI models in production with significantly lower inference latency and cost compared to standard cloud ML services. The platform includes automated model optimization, hardware-aware compilation, and a managed serving layer that handles traffic routing and autoscaling.\n\nThe company raised approximately 132 million USD and counts enterprise customers in sectors including e-commerce, media, and financial services that operate high-throughput model serving pipelines at scale. OctoAI emerged from foundational research at the University of Washington in ML systems optimization, giving the technical team a deep background in compiler-level model performance.\n\nAs inference costs become the dominant expense in production AI deployments, infrastructure that reduces cost-per-query by meaningful percentages translates directly to enterprise budget savings. OctoAI competes with Anyscale, BentoML, and cloud-native MLOps offerings, but its focus on hardware-level optimization and the depth of its model acceleration capability distinguishes it from platforms that primarily address deployment workflow rather than raw performance.

Improbable got there first, launching in 2012 — that's 7 years of extra runway. OctoAI didn't arrive until 2019. In AI, that kind of head start means more training data, deeper customer relationships, and a bigger talent moat.

Improbable has about 500-1000 employees; OctoAI has about 100-500. A bigger team usually means more revenue or heavier VC backing, but in AI, small teams can build at massive scale.

Yes — they're direct rivals. Both Improbable and OctoAI compete in ML Platform, targeting many of the same buyers. If you're evaluating one, you should be looking at the other.