#18 in DatabasesUpdated 2026-08

Activeloop

by Activeloop · GPU-native database, shared agent memory and continual-learning tooling

56.1 — BenchRank score out of 100

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Overview

Activeloop builds infrastructure for continual learning across three products: Deeplake, a GPU-native database holding vector and tensor data behind a serverless Postgres interface; Hivemind, which turns agent traces into shared organisational memory; and Refinery, which feeds production feedback into database, kernel and policy optimisation.

Best for: Teams running AI agents that need a GPU-native, versioned store for vector and tensor data
Pricing: Deeplake starts with a $15 credit on pay-as-you-go rates (compute from $0.15/compute-unit/hr, storage from $23/TB/mo), with a Team tier at $99 per seat per month and enterprise deployments by contacting sales.
Runs on: Web

Strengths and trade-offs

Strengths

Vector and tensor data in one store, queried over Postgres
Streams data to GPUs for fine-tuning
Per-unit compute and storage rates plus a cost estimator
Agent traces become shared team memory via Hivemind

Trade-offs

Prices are published for Deeplake only, not Hivemind or Refinery
Both published tiers cap storage at 500 GB and one availability zone
Data egress is billed separately, from $0.09 per GB
Enterprise deployment requires a sales conversation

Pricing

Published plans from Activeloop’s own pricing page, in USD. Usage charges and add-ons may apply on top.

Activeloop pricing tiers, monthly rates in USD
Plan	Monthly	Includes
Basic	—pay as you go	$15 credit for testing and starter projects Compute from $0.15/compute-unit/hr, storage from $23/TB/mo Up to 500 GB storage, 8-16 GB memory, 1 zone Daily backups with 1-day retention, SSO and MFA
Team	$99per seat	7-day free trial 1M traces and 10M queries per month per seat Up to 500 GB storage, 8-16 GB memory, 1 zone 1 business day support response
Enterprise	Contact sales	For enterprise deployments Pricing by contacting sales

How this score is made up

Each dimension is scored out of 100 and combined into the headline score using fixed weights.

MCP support: 0 out of 100
API quality: 35 out of 100
Documentation: 80 out of 100
Agent friendliness: 85 out of 100
Pricing transparency: 100 out of 100
Changelog: 55 out of 100
Marketing site structure: 75 out of 100
Operational trust: 55 out of 100

Measured, but not part of the score

Useful to know, but not a mark for or against the product — so these do not affect the ranking.

Openness: 60 out of 100
Maintenance: 70 out of 100

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