Eventual
Eventual builds data infrastructure for AI and Physical AI workloads through its open-source multimodal DataFrame engine Daft and the commercial MultiBase product, enabling enterprise teams to process petabyte-scale video, sensor, and unstructured data for machine learning pipelines.
- Company typePrivate
- Founded2022
- HeadquartersSan Francisco, United States
- Headcount11–50
- GTM typeB2B
- OfferingSoftware
What Eventual does
Eventual is a data infrastructure company that builds tools for processing multimodal data — including video, images, audio, lidar, and high-frequency sensor streams — at scale for AI and Physical AI workloads. Founded in 2022 by ex-Lyft engineers Sammy Sidhu and Jay Chia, the company was created in response to data infrastructure bottlenecks encountered during self-driving development, where researchers reportedly spent 80% of their time on data workflows rather than model training. The company is headquartered in San Francisco with 11-50 employees and has raised $30M in total funding.
Eventual firmographics
Firmographics- Name
- Eventual
- Legal name
- EVENTUAL COMPUTING, INC.
- Website
- https://eventual.ai
- Company type
- Private
- Founded year
- 2022
- Operating status
- Operating
- Headcount range
- 11–50 employees
- Short description
- Eventual builds data infrastructure for AI and Physical AI workloads through its open-source multimodal DataFrame engine Daft and the commercial MultiBase product, enabling enterprise teams to process petabyte-scale video, sensor, and unstructured data for machine learning pipelines.
- Ownership category
- akta.pro rank
Eventual industry classification
Industry- Product category
- AI Data Infrastructure
- NAICS
- Computing Infrastructure Providers, Data Processing, Web Hosting, and Related Services (51821), Computing Infrastructure Providers, Data Processing, Web Hosting, and Related Services (518)
- SIC
- Services-Prepackaged Software (7372), Services-Computer Programming, Data Processing, Etc. (7370)
- akta.pro primary industry
- Enterprise AI Data & Knowledge Platforms (Vector Databases, Knowledge Graphs) (HDAEANAH)
- akta.pro secondary industries
- Data & Analytics Platforms (Data Warehousing, Lakes, Streaming) (HDABAAAF), Data Pipelines for GenAI (Curation, Filtering, Deduplication, Copyright) (HDAAACAJ), Real-Time / Streaming Data Warehousing (HDAEABAF), AI Integration & Orchestration Platforms (Connectors, Workflow, iPaaS for AI) (HDAEANAI)
Keywords
Where Eventual is headquartered
LocationHeadquarters
- HQ city
- San Francisco
- HQ country
- United States
- HQ region
- North America
Offices1 record
Markets served
Eventual business model
Business model- GTM type
- B2B
- Offering type
- Software
- Cost components
- Personnel, Technology or R&D, Marketing or Sales, Operations
Revenue model
- Open-Source Daft (Community/Free): Daft open-source engine available for free download on GitHub. Revenue comes from community adoption leading to enterprise adoption and potential commercial licensing or support contracts.
- MultiBase Commercial Product: MultiBase is a commercial data infrastructure product for Physical AI teams. Contact sales process indicates enterprise pricing. Targets organizations building robotics and autonomous systems with petabyte-scale video and sensor data needs.
Pricing tiers
| Model | Billing | Price |
|---|---|---|
| Subscription | Annual | MultiBase Enterprise - Contact sales for pricing |
Go-to-market motion2 records
Distribution channels2 records
Marketing channels8 records
Eventual product offering
Product offeringCore offering
Eventual builds data infrastructure for AI and Physical AI applications. Its open-source engine Daft processes multimodal data (images, video, audio, lidar, and sensor streams) in distributed DataFrame pipelines at exabyte scale. On top of Daft, the commercial product MultiBase delivers Physical AI data infrastructure with deep semantic indexing and GPU-optimized data loading for robotics and autonomous vehicle teams.
Product overview
Eventual offers a dual go-to-market strategy combining an open-source engine with a commercial product. The core is Daft, an open-source multimodal data engine that processes images, video, audio, and unstructured data in distributed DataFrame pipelines at exabyte scale. Built on top of Daft is MultiBase, a commercial data infrastructure product for Physical AI applications that provides semantic indexing of video and sensor data with GPU-optimized data loading. Daft serves as the underlying execution engine while MultiBase delivers the enterprise-grade workflow for robotics and autonomous vehicle data teams.
Differentiator
Problem solved
Functional benefit
Brands
- Daft: Open-source data engine for processing multimodal data including images, video, audio, and sensor data at exabyte scale. Used by companies like Amazon, Mobileye, and Together AI.
- MultiBase
Products and services
- Daft Open-source multimodal data processing engine that handles images, video, audio, and unstructured data in distributed DataFrame pipelines. Runs in production at exabyte scale inside Amazon, Mobileye, and Together AI. Supports lake formats (Iceberg, Delta Lake, Hudi, Paimon), native extensions via C ABI, ASOF joins, multimodal embeddings, and AI/ML workloads including video decoding and vector search. Available for free via GitHub and PyPI for data engineers, ML engineers, and AI/ML teams.
- MultiBase Commercial data infrastructure product for teams building Physical AI, built natively for video and high-frequency sensor data. Provides deep semantic indexing of perception data beyond embeddings, enabling complex temporal and action-oriented queries on petabytes of fleet data in plain English. Streams curated data to GPUs at line rate during training to maximize Model FLOPs Utilization (MFU). Runs directly on open formats (mp4, jpeg) in user-owned storage. Targets enterprise Physical AI teams building robotics and autonomous systems.
Quantifiable outcome
- Reclaims 20-40% of training time lost to data loading
- +5 more outcomes
Companies that use Eventual
Customer profileNamed customers5 records
Segments3 records
Ideal customer profiles2 records
Eventual technology and API
TechnologyTechnology focussed Yes
API detail
- Has API
- No
- API docs
- API detail
Core technology
AI maturity
App detail
Integration9 records
AI capability12 records
Feature8 records
Eventual partnerships and signals
Strategic signalPartnerships
Five partnerships are on record, tiered flagship and core.
- AmazonflagshipAmazon runs Daft in production at exabyte scale. Their AWS team documented the migration from Apache Spark to Ray with Daft on Amazon EC2 in an AWS blog post.
- MobileyecoreMobileye uses Daft in production for processing autonomous vehicle sensor and video data at scale.
- Together AIcoreTogether AI, an AI infrastructure company, uses Daft for multimodal data processing in their platform.
- CloudKitchenscoreCloudKitchens uses Daft for processing multimodal data from their autonomous kitchen operations.
- Essential AI LabscoreEssential AI Labs uses Daft for processing unstructured data in their AI development pipeline.
Scale indicators5 records
Recent moves6 records
Expansion highlights5 records
Eventual competitors and assessment
Company assessmentBroad incumbents
- Databricks: Unified data and AI platform built around Delta Lake that competes broadly with Daft's lakehouse DataFrame engine for multimodal workloads. Databricks has deeper enterprise distribution and Photon as an accelerator, but does not specialize in video/lidar-first multimodal pipelines the way Eventual does.
- Snowflake: Cloud data warehouse and increasingly AI workload platform. Competes with Daft on structured and semi-structured data lakehouse workloads and is extending into AI/ML, making it a broad incumbent competing for the same enterprise data infrastructure budget.
- Scale AI: Data infrastructure and labeling platform serving autonomous vehicles, robotics, and AI labs. Comparable because Scale targets the same Physical AI customer segment (e.g., Mobileye) and is building adjacent data tooling that overlaps with MultiBase's perception-data indexing mission.
Direct peers
- Anyscale: Commercial provider of the Ray distributed-compute platform on which Daft executes. Directly comparable because any Ray customer considering Eventual's Daft layer is also a candidate for Anyscale's managed Ray offering, and both companies sell AI infrastructure to the same buyer personas.
- Polars: Open-source DataFrame library built on Apache Arrow with strong performance characteristics. Competes directly with Daft for developer mindshare in Python data processing, and Daft specifically advertises zero-copy interop with Polars — making them simultaneously collaborators and substitutes.
- LanceDB: Open-source multimodal AI database built on the Lance columnar format for vector search, full-text retrieval, and SQL over images, video, and audio. Directly comparable to Eventual's Daft and MultiBase for multimodal data management and retrieval at AI scale.
Emerging players
- Modular (Mojo / MAX): AI infrastructure company building the Mojo language and MAX inference platform. Emerging competitor in the AI compute and data-processing stack targeting similar AI/ML engineering audiences, with overlapping positioning around developer-first performance tooling.
- Pinecone: Managed vector database focused on retrieval-augmented generation and similarity search. Comparable to the multimodal embeddings and vector search capabilities Eventual exposes through Daft and MultiBase, targeting similar AI/ML engineering buyers.
- Hugging Face: Open-source AI/ML platform with dataset and model hosting for multimodal data. Comparable as an open-source-first AI infrastructure company that has built developer communities and now layers commercial offerings on top, similar to Eventual's Daft-plus-MultiBase model.
- Weights & Biases: MLOps and experiment tracking platform with growing data and model management features. Comparable as part of the broader AI/ML infrastructure stack that includes data versioning and pipelines, targeting the same ML engineering buyers who adopt Daft.
Market position
Strengths5 records
Weaknesses5 records
Competitive moat6 records
Key risks6 records
Key highlights7 records
Customer concentration
Eventual social profiles
Digital presenceEventual compliance and trust
Trust signalCompliance1 record
Eventual financial estimates
Financial estimateRevenue estimate
Valuation estimate
Eventual leadership team
Management profileNumber of profiles
Profiles3 records
Eventual funding detail
Funding detailFunding overview
Funding rounds4 records
Investors11 records
Funding detail is available on the Subscription and Enterprise plan.Contact sales →
Eventual M&A and investment
M&A and investmentM&A
Investments
M&A and investment is available on the Subscription and Enterprise plan.Contact sales →
Frequently asked questions about Eventual
What does Eventual do?
Eventual builds data infrastructure for AI and Physical AI applications. Its open-source engine Daft processes multimodal data (images, video, audio, lidar, and sensor streams) in distributed DataFrame pipelines at exabyte scale. On top of Daft, the commercial product MultiBase delivers Physical AI data infrastructure with deep semantic indexing and GPU-optimized data loading for robotics and autonomous vehicle teams.
Is Eventual a public or private company?
Eventual is a private company. It is classified as venture growth investor backed and is currently operating.
When was Eventual founded?
Eventual was founded in 2022. It employs 11 to 50 people.
Where is Eventual based?
Eventual is headquartered in San Francisco, United States, in the North America region.
How does Eventual make money?
Two revenue lines are on record. Open-Source Daft (Community/Free) is the primary driver. The others are multiBase Commercial Product.
Who are Eventual's main competitors?
Broad incumbents on record are Databricks, Snowflake and Scale AI. Direct peers are Anyscale, Polars and LanceDB. Emerging players are Modular (Mojo / MAX), Pinecone, Hugging Face and Weights & Biases.
Does Eventual have an API?
No public API is recorded for Eventual.
What industry is Eventual in?
Eventual's product category is AI Data Infrastructure. Its primary akta.pro industry code is HDAEANAH, Enterprise AI Data & Knowledge Platforms (Vector Databases, Knowledge Graphs), with a secondary code of HDABAAAF, Data & Analytics Platforms (Data Warehousing, Lakes, Streaming). Its NAICS code is 51821 and its SIC code is 7372.