Deeplake
Deeplake is the GPU database for AI agent workloads — serverless Postgres with GPU-accelerated vector search built for agents.
Deeplake is worth a serious look if agents own your query load and you are tired of paying CPU-database prices for GPU-adjacent work. The GPU-native claim is backed by published ClickBench and SF-100 comparisons rather than vibes, and the $0 Basic tier makes the trial cheap. It is a bad fit for conventional OLTP backends — treat it as an agent data plane, not a Supabase replacement.
Verified 20m ago · liveness 65/100 · cite: rightaichoice.com/tools/deeplake
- AI agent teams needing shared, versioned multimodal memory
- Developers running multi-agent workflows with GPU vector search
- Data teams managing large multimodal datasets for training and RAG
- Autonomous software engineering pipelines with heavy query volume
- Traditional web app backends relying on stored procedures or triggers
- Teams wanting a general-purpose DBMS with complex join optimization
- Projects with no agent or multimodal data workload
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Skip Deeplake if your primary workload is a traditional relational web backend that needs stored procedures, triggers, and complex join optimization — a general-purpose Postgres like Neon or Supabase fits that better.
Once you pass the Basic tier's 150k traces and 1.5M queries, overages bill at $0.25 per 1k traces and 10k queries — small per unit, but agent traffic adds up fast.
Basic is genuinely cheap to start — $0/month with $15 in credits and no card — which suits solo builders and early prototypes. Team at $99/seat/month is aimed at funded startups running production agent fleets, and sits above lightweight vector-only options but below typical managed-cloud data-warehouse spend. Enterprise is quote-based and competes with self-hosted or VPC-deployed data platforms for regulated teams.
In short
Deeplake — Deeplake is the GPU database for AI agent workloads — serverless Postgres with GPU-accelerated vector search built for agents. Best for AI agent teams needing shared, versioned multimodal memory, Developers running multi-agent workflows with GPU vector search, Data teams managing large multimodal datasets for training and RAG. Free to start; paid plans from $99/user/mo.
What's new in Deeplake
Checked todayAcross the latest 5 updates: 1 feature update, 2 launches and 2 news mentions.
Hivemind Skills, Enriched: Turn Session Lessons into Full Playbooks with ScrapeGraphAI
Hivemind integrates with ScrapeGraphAI to enrich skill files with live web research; the example expands a 44-line skill to 263 traceable lines.
A Deployable Annotation Service for Robotics Datasets
Open-source Roboscribe-AF runs multi-agent robotics annotation with AgentField, storing multimodal dataset branches in Deeplake with a queryable schema.
Spin up Postgres in a second: How We Built Serverless PG for Agents.
Deeplake introduces a serverless, PostgreSQL-compatible database using its storage engine and DuckDB for query execution.
Your Agents Are Drowning in Quicksand. Give Their Data a Sandbox.
Deeplake positions serverless Postgres sandboxes as agent-centric data isolation, spinning up per agent and scaling with the swarm.
Software Factory Ran Autonomously for 15h, 2x Speed Up on TPC-H, ASAN-verified, Cost $160
An autonomous agentic workflow achieved 2x speedup on TPC-H by optimizing a C++ codebase over 15 hours for $160.
What people actually say about Deeplake — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
4 mentions across 2 sources (Hacker News, Lemmy) · researched Jul 3, 2026.
Average across the 2 sources that answered — each source counts once, not each post.
- +Serverless PostgreSQL scales to zero, reducing idle costs.
- +GPU-native vector search accelerates similarity queries on AI workloads.
- +Automatic versioning and branching simplify data management for agents.
- +DuckDB query engine provides fast analytical queries with Postgres compatibility.
- +Multimodal ingestion supports images, text, audio, and video natively.
- −No independent benchmarks or real user reviews available.
- −Cold-start latency for serverless instances remains unquantified.
- −Proprietary storage engine may complicate migration away from Deeplake.
- −Community engagement is extremely low — hard to get help or feedback.
- −Limited integrations listed — no ecosystem of pre-built connectors.
- • Exact overage charges or rate limits beyond free tier not disclosed; GPU compute costs may scale unpredictably if not monitored.
Viability Score
How well maintained and how widely used is Deeplake? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- GPU-native database built for AI agent workloads
- PostgreSQL-compatible SQL interface with ACID compliance
- DuckDB query engine over Deeplake's data lake storage
- Dedicated serverless Postgres instance boots in about one second
- Scales to zero when idle to cut idle compute cost
- GPU-accelerated vector and semantic search
- Automatic dataset versioning and branching
- Multimodal ingestion: images, text, audio, video, sensors and 3D scans
- Shared memory for multi-agent collaboration
- Hivemind skills and session layer for agent memory
- ScrapeGraphAI integration for live web research in skill files
- AgentField and Roboscribe-AF multi-agent robotics annotation example
- Per-agent serverless Postgres sandboxes for data isolation
- VPC deployment in your own cloud (BYOC)
- Daily backups with configurable retention
About Deeplake
Deeplake is a GPU-native database and memory layer built for AI agent workloads. Activeloop pairs a PostgreSQL-compatible SQL interface with the DuckDB query engine over Deeplake's data lake, so teams get familiar SQL plus a storage layer designed for multimodal data — images, text, audio, video, sensor and 3D data. Dedicated Postgres instances spin up in about a second and scale to zero when idle, which keeps idle costs down for spiky agent traffic. The pitch on the site is blunt: your AI runs on GPUs, your database does not. Deeplake puts the data where the compute is, and cites ClickBench terms of $2.15 versus $17.05 for Databricks X-Large, plus an SF-100 result of 36s against Snowflake (49s), Fabric (50.69s) and Databricks (96s). Whether or not those numbers hold for your schema, the framing tells you the intended buyer: teams running agents an order of magnitude more queries than a human app. Beyond the database, Deeplake ships Hivemind, a skills and session layer. A June 2026 post shows Hivemind enriching a skill file with ScrapeGraphAI's live web research, turning 44 lines into 263 traceable lines. In May 2026, the open-source Roboscribe-AF project ran multi-agent robotics annotation on AgentField and stored its multimodal dataset branches in Deeplake with a queryable schema. Pricing is usage-based through Hivemind: a Basic tier at $0/month with $15 in credits, 150k traces and 1.5M queries included, then $0.25 per 1k traces and 10k queries. Team costs $99 per seat per month with 1M traces and 10M queries per seat. Enterprise runs in your own VPC. If you want a general-purpose Postgres for a web app, look at Neon or Supabase; Deeplake is aimed at agent memory, RAG, training data management and multimodal pipelines.
Behind the Verdict
We'd reach for Deeplake when the workload looks like agents doing lots of small reads and writes over multimodal data — session memory, retrieval, training-set versioning — and the bill from a CPU warehouse has started to look silly. The March 2026 serverless Postgres work matters here: instances come up in about a second and drop to zero when idle, so a swarm that idles between bursts is not bleeding money. Pair that with SQL you already know and the migration conversation gets short. Where it bites: Deeplake is not a full relational DBMS substitute. If your application leans on stored procedures, triggers, or heavy join optimization, this is the wrong layer — keep Postgres for that, and put Deeplake beside it. Teams with no agent or multimodal workload should stop reading; a standard managed Postgres will be cheaper and less exotic. Hivemind is the part I find most interesting and least proven. The ScrapeGraphAI integration in June 2026 is a real demonstration that skills can accumulate research across sessions instead of resetting every run. But Hivemind is young, and shipping a skills layer alongside a database means two products to learn, not one. Budget time for that. The pricing dance is honest. Basic is free with $15 of credits, 150k traces and 1.5M queries, then metered at $0.25 per 1k traces and 10k queries. Team jumps to $99 per seat per month for 1M traces and 10M queries per seat — that is a real commitment, so know your trace volume before you sign. Enterprise runs in your own VPC with SAML SSO, SOC 2 and HIPAA, which is what regulated shops will need. Closest alternatives: Pinecone if all you need is vector search and nothing else; Neon or Supabase if you want serverless Postgres for a web app. Deeplake earns its place when the workload is agents
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Real-world workflow fit
Concrete scenarios for the personas Deeplake actually fits — and what changes day-one when you adopt it.
You sign up on the Basic tier (no card required, $15 in credits), spin up a per-agent Postgres sandbox in about a second, and write agent memory into it with standard SQL.
Outcome: You get a working agent-memory store without provisioning a server, and the instance scales to zero when your agent isn't running.
You ingest multimodal data (images, text, audio, video), branch a dataset for an experiment, and run GPU-accelerated vector search to build a RAG index.
Outcome: Versioned datasets and branches let you reproduce experiments, and vector search runs on the GPU over large collections.
You deploy a swarm of agents that share one dataset and enrich their Hivemind skills with live web research via ScrapeGraphAI.
Outcome: Shared memory keeps agents consistent, and the ScrapeGraphAI integration turns a 44-line skill into a 263-line playbook with traceable sources.
Use Cases
- Stand up a per-agent Postgres sandbox that scales with your agent swarm
- Store and retrieve multimodal agent memory (images, text, audio, video)
- Run GPU-accelerated vector and semantic search over large collections
- Version and branch training datasets for AI experiments
- Enrich Hivemind agent skills with live web research via ScrapeGraphAI
- Build multi-agent robotics annotation services with AgentField
- Deploy autonomous software testing pipelines using shared memory
Limitations
- Deeplake is positioned by its vendor as a serverless, PostgreSQL-compatible database for agent workloads rather than a traditional DBMS, and pricing is usage-based — $0.25 per 1k traces and 10k queries after the included allowances — so cost scales with agent activity.
- The Basic tier includes 150k traces and 1.5M queries per month, with per-seat collaboration at $99/seat/month on the Team tier.
- Enterprise deployment runs inside your own infrastructure (own VPC) with SAML SSO, SOC 2, HIPAA, and named support, requiring a sales contact.
- The evidence does not document detailed concurrency, join-optimization, or on-prem install behavior.
as of 2026-09-14
Verification history
We have re-verified Deeplake 7 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 7 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Deeplake tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Basic
$0/mo
Ideal for
Solo builders and early prototypes that want to test agent memory and vector search without a credit card.
What this tier adds
Free entry point: $15 in credits, 150k traces and 1.5M queries included per month, then pay-as-you-go.
Team
$99/seat/mo
Ideal for
Funded startups running production agent fleets that need per-seat collaboration and higher included usage.
What this tier adds
Adds per-seat collaboration and lifts included usage to 1M traces and 10M queries per month per seat for $99/seat.
Enterprise
Custom
Ideal for
Regulated or security-conscious organizations that need to run the platform inside their own infrastructure.
What this tier adds
Adds VPC deployment, SAML SSO, SOC 2, HIPAA, and a named support engineer, priced via sales.
Where the pricing makes sense
The company stage and team size where Deeplake's pricing actually pencils out — and where peers do it cheaper.
Basic is genuinely cheap to start — $0/month with $15 in credits and no card — which suits solo builders and early prototypes. Team at $99/seat/month is aimed at funded startups running production agent fleets, and sits above lightweight vector-only options but below typical managed-cloud data-warehouse spend. Enterprise is quote-based and competes with self-hosted or VPC-deployed data platforms for regulated teams.
Setup time & first value
How long it actually takes to get something useful out of Deeplake — broken out by persona, not the marketing-page minute.
Solo developer: minutes — sign up on the free Basic tier, no card required, and a dedicated Postgres instance boots in about a second. Startup team: under an hour to wire SQL clients and ingest a first dataset. Enterprise: days to weeks, since VPC/BYOC deployment, SAML SSO, and compliance review go through sales.
Switching to or from Deeplake
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From Neon or Supabase: point your PostgreSQL-compatible client at a Deeplake instance and move tables over standard SQL, accepting that stored procedures and triggers won't carry across.
- →From a self-managed PostgreSQL box: use DuckDB-backed SQL execution and Deeplake storage instead of operating the server yourself.
- →From S3-only storage: ingest images, text, audio, and video into Deeplake and query them with SQL plus vector search.
- →From a pure vector store: keep your embeddings but gain SQL, dataset versioning, and branching alongside the similarity search.
- ↗To a general-purpose Postgres (Neon, Supabase): export your tables over standard SQL when you need stored procedures, triggers, or complex joins.
- ↗To a light vector-only store (Pinecone): move just your embeddings if all you need is similarity search without SQL or versioning.
- ↗To plain object storage: pull multimodal files and metadata back out to S3 if you no longer need query or versioning.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Deeplake”, and we withheld 6: 6 could not be judged, because “Deeplake” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Deeplake.
Official links
Tools that pair well with Deeplake
Common stack mates teams adopt alongside Deeplake, with the specific reason each pairing earns its keep.
Tidb
Open-source distributed SQL database unifying transactions, HTAP analytics, and native vector search for AI agents.
Pinecone
Fully managed vector database and knowledge platform for AI retrieval, with Pinecone Nexus compiling enterprise data into governed knowledge for agent queries.
RAGFlow
Open-source RAG engine that turns messy documents into a trustworthy context layer for AI agents, with ETL, hybrid search and agentic retrieval.
Featured Head-to-Head Comparisons
Deeplake vs Spider Cloud
If your primary need is fast, cost-effective web data extraction for AI agents, Spider Cloud is the clear choice with its Rust engine and 99.9% success rate at $0.03/1k pages. For teams building complex multi-agent systems that require shared memory, versioned multimodal datasets, and GPU-accelerated vector search, Deeplake's serverless Postgres and datalake offer a purpose-built runtime. Choose based on whether your bottleneck is data acquisition or data management.
Deeplake vs Presto Voice
For a QSR chain automating drive-thru orders with proven upselling ROI (up to 6% revenue lift), Presto Voice is the clear choice. If your team builds AI coding agents needing GPU-accelerated multimodal data and serverless Postgres, Deeplake is unmatched—especially with its new Hivemind skills and sandboxed Postgres. These tools serve completely different domains; choose based on your problem: restaurant operations or agent data infrastructure.
Deeplake vs Temporal Ai
Choose Temporal AI if you need durable, crash-resistant orchestration for AI agents and long-running workflows that survive failures. Choose Deeplake if you need a serverless, GPU-accelerated multimodal datalake with vector search and shared memory for multi-agent collaboration. For most agent teams, combining both—Temporal for orchestration and Deeplake for state—can be a powerful stack.
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Tidb
Open-source distributed SQL database unifying transactions, HTAP analytics, and native vector search for AI agents.
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