Deeplake pricing, free plan and limits

Last updated: 2026-10-08

Deeplake is an open-source database for AI agents from Activeloop, with more than 5 million downloads reported by the vendor. It joins a Postgres-compatible SQL layer to a multimodal data lake, executes queries on GPUs, and holds text, images, video and embeddings in one place. Its Hivemind product adds shared memory for coding assistants.

About Deeplake

Deeplake is the database product from Activeloop, a San Francisco company that joined Y Combinator in Summer 2018. Activeloop now describes it as a GPU database for agents: a serverless Postgres interface with a multimodal data lake underneath, queried with SQL. The core is open source under Apache-2.0 on GitHub, where the repository has passed 9,000 stars.

The design bet is that agents run far more queries than people do, so the store should sit next to the GPUs that run the models. The vendor's own SF-100 analytical benchmark reports 36 seconds for Deeplake against 96 for Databricks, at $0.02 against $0.74 per run. Treat that as vendor-reported, because it compares published results rather than a test HokAI ran. The incumbent lakehouse to weigh it against is Databricks, and the lighter embedded store is LanceDB.

Activeloop's second product, Hivemind, sits on top and gives coding agents a shared memory. One installer hooks into Claude Code, OpenClaw, Hermes, Codex, Cursor and pi, captures prompts and tool calls, and lets a teammate's agent recall another person's session. It overlaps with Mem0 on memory and with LangChain and LlamaIndex on retrieval plumbing, though it is a storage layer rather than a framework.

For multimodal training data the nearer neighbours are Encord for annotation and Hugging Face for dataset hosting. Deeplake's pitch there is streaming datasets into model training without copying them first, with versioning kept alongside the data.

Pricing

The pricing page, headed Hivemind Pricing, lists three plans. 5M queries included. Team is $99 per seat per month after a 7-day trial, with 1M traces and 10M queries per seat.

25 per 1k traces and 10k queries. Enterprise is custom, runs inside your own VPC and adds SAML SSO and HIPAA. The open-source library is free to self-host.

Key Features

• Serverless Postgres interface: Exposes standard SQL with ACID guarantees on top of PostgreSQL, so agents and analysts query multimodal data with existing drivers.
• GPU-native query execution: Runs analytical queries on GPUs, and the vendor reports a 36 second run against 96 seconds on Databricks in its published analytical benchmark.
• Multimodal data lake: Stores text, images, video, sensor data, 3D scans and model weights in one versioned dataset format.
• Hivemind shared agent memory: One installer hooks six coding assistants into a shared, searchable store that captures prompts, tool calls and responses.
• Bring your own cloud: Connects to storage on Google Cloud Storage, Azure Blob or Amazon S3, with S3-compatible on-prem available on request.
• Hybrid retrieval: BM25 full-text search is the default, with semantic embeddings opt-in for hybrid lexical and vector recall.

Pros

• The Apache-licensed core is public on GitHub, so the data format is not tied to one vendor's cloud and teams can self-host before paying anyone.
• Homepage names a SOC 2 Type II certification and a run-in-your-own-VPC option, two items procurement teams usually ask for in the first call.
• A last-mile delivery customer quoted on the vendor site reports 19.5% higher model accuracy and 32% lower training cost, which is a testimonial, not an audited result.

Cons

• The pricing page is headed Hivemind Pricing and prices agent traces and queries, so the cost of the GPU database for large analytical workloads is not published.
• The Hivemind installer needs Node.js 22 or newer and writes hooks into every detected assistant, which security teams will want to review before a rollout.
• No G2 star rating or Trustpilot page with a public review count exists yet, so buyers have only vendor testimonials to go on.

Visit Deeplake Official Website