EinsteinDB is a Hybrid memory system consisting of Memristors (DRAM and Non-Volatile Memory) aimed at persisting data fast!

domain-agnostic schema-freeLearn More

EinsteinDB is a Neuromorphic Hybrid HTAP that allows you to write and read data without locking, and without worrying about what other clients are doing. It is fast, and does not need to use a network to work

EinsteinDB and MilevaDB use different types of index to make it faster to find things

EinsteinDB is a distributed database that is schema-free, and offers the strictest concurrency control for Repeatable Reads and Merge-Commit Leaderless transactions.

BerolinaSQL is an open source SQL dialect for NoSQL databases like Apache Cassandra, MongoDB, HBase and others. It was created by WHTCORPS Inc., which provides development tools for building scalable web services in Java, Scala and Clojure using its technology stack of Apache Spark on top of Mesosphere DC/OS or Kubernetes on top of Google Container Engine (GKE).

A database that is distributed without time zones, can be scaled to a global scale, and has a synchronous replication. The EinsteinDB team at Whtcorps claims to have accomplished all of this using Petri nets.

To support rich key-value operations efficiently, EinsteinDB uses a special kind of index. It builds the index in memory and it is very fast to use.

Distributed SQL databases like MilevaDB aim to combine the best features of both Relational Database Management Systems (RDBMSs) and NoSQL databases to create a truly cloud-native database via EinsteinDB's persistence layer.

Kubernetes is a system that makes sure MilevaDB and EinsteinDB keep running, even if something bad happens.

Fidel allows you to use the same codebase across all of your applications by using a single application. Fidel can auto scale and auto failover your applications to a different host, allowing you to reduce the time between redeployments and increase uptime.

EinsteinDB provides for a production-grade, real-time situation awareness, decision making on live transactional data aimed at eliminating friction between IT and business goals with stochastic foraging load balancing automata.

automatically shard your data so you don’t have to do it manually. You can simply add new nodes to scale horizontally and elastically.


$0.001 GBPS without EinstA.I powered by GPT3


para="$0.01 GBPS with EinstA.I powered by GPT3"
  • EinsteinDB's Californium offers a self-managed 1024 GiB Arm-based Arm-based CPU which is 5% to 18% faster than the usual x86-based CPU offers out there. EinsteinDB's Moscovium base model does not include OpenAI's GPT3-powered EinstAI's Automata.
  • Californium with EinstAI is $0.01 Gbps via EKS with Graviton2 (Arm) offering and also on the Intel Xeon Platinum 8000 series (x86) across all Google Cloud, Azure, and Alibaba
  • Moscovium is a base offering, without EinstAI, of $0.001 Gbps with both transactional (Txn) and non-transactional (Tuplestore) APIs.
  • An embedded columnar in-memory analytics layer designed for large scale data processing sitting on a key-value store designed for high performance, small footprint applications. EinsteinDB and MilevaDB together reduce workload latency up to 86.6%, and for multi-threaded performance, EinsteinDB increases the throughput by up to 6.4 times under YCSB workloads


  • Features a novel compare-and-swap (CAS) atomic instruction to achieve synchronization between multiple threads.
  • MilevaDB is an open-source distributed Prolog-inspired AllegroSQL database. MilevaDB's storage layer is EinteinDB. BerolinaSQL is a MySQL compatible module that can be used with Postgres, MariaDB, and Oracle Tables with added Secondary Indices.
  • $0.001 Moscovium Cloud-Native offering
  • $0.01 is Californium, Next-Gen HTAP EinsteinDB offering with OpenAI's General Intelligence Automaton GPT3, GPT4, CLIP, and BERT.


  • MilevaDB, through FIDel and EinsteinDB's offering of gRPC compatible protobuffers, uses cost as feedback to train the model and then switches to latency memristors (context switches) for fine-tuning throughput guarantees in read heavy workloads.
  • Instead of clustering the whole database, EinsteinDB aims to cluster only the "hot" portions of the database by relying on the admission policy of the existing database cache. Second. We've developed a novel self-tuning locality-sensitive hash (LSH) function, namely, Tunable-LSH to decide in constant-time where in the storage system to place a record. Tunable
  • Instead of putting everything in one big pile, EinsteinDB is going to put the hot stuff in a special pile. First, we have a special self-tuning magic box that can make decisions about where to put things in the storage system. MilevaDB and EinsteinDB can store semi-structured data using JSON-LD. We first use cost as feedback to train the model and then switch to latency as feedback for fine-tuning.
  • EinsteinDB's Materialized views (MVs) can significantly optimize the query processing in MilevaDB. However, it is hard to generate MVs for ordinary users because it relies on background knowledge, and existing methods rely on DBAs to generate and maintain MVs. However, DBAs cannot handle large-scale databases, especially cloud databases that have millions of database instances and support millions of users. Thus it calls for an autonomous MV management system. EinstAI is an autonomous materialized view management system which analyzes query workloads, estimates the costs and benefits of materializing queries as views, and selects MVs to maximize the benefit within a space budget.

EinsteinDB is faster than MonetDB and LogicBlox. EinsteinDB is faster because it uses deep neural networks to learn how to make the database faster.

EinsteinDB learns how to make the database faster by looking at how people use it. And it gets better over time, because EinsteinDB learns from the data it sees.