deep lake, in plain language
honeycomb keeps your memory in deep lake, the database for ai built by activeloop. it is the part of honeycomb that holds what your assistants learn. here is what it is, why it is unusual, and why it means you own your data and pay almost nothing to keep it.
see how honeycomb workswhat it actually is
most databases are good at one kind of question. a spreadsheet-style database is great at exact lookups (find the note that mentions this exact word). a vector database is great at meaning-based search (find the note that is about this idea, even in different words). deep lake does both in one place, so honeycomb can find the right memory by the words you used and by what you meant, in a single query. it stores plain text, structured fields, and the meaning-vectors side by side, as columns in the same table.
read the trust pagehow your memory is stored and recalled
- 01
a note becomes a row
when honeycomb distills something worth keeping, it writes a row into deep lake: a short headline, a longer summary, the full original, and a meaning-vector that captures what the note is about. nothing is thrown away, and every row carries where it came from.
- 02
the row keeps its history
deep lake keeps a full version history instead of overwriting in place. when a fact changes, honeycomb adds the new version and marks the old one replaced. you can always see what was true before, and nothing is ever silently lost.
- 03
recall searches two ways at once
when your assistant needs context, honeycomb searches deep lake by exact words and by meaning together, then hands back the few notes that fit. because both kinds of search run against the same store, there is no second system to keep in sync.
- 04
it lives where you choose
deep lake is serverless and can run in your own cloud account, so the rows that hold your memory sit in storage you control. you are not renting a black box that holds your data hostage.
why it is cheap and durable
activeloop reports deep lake running a standard analytics workload at a fraction of the cost of traditional cloud data platforms, on the order of eight times lower in their published comparison. for you that means keeping memory around is close to free. (deeplake.ai, captured 2026-06.)
exact search, meaning-based search, and the structured fields all live in one place. you are not paying for a database plus a separate vector index plus the glue between them, and there is less to break.
deep lake is serverless and can run in your own cloud (your bucket on google cloud, azure, or amazon). the memory honeycomb keeps stays inside your perimeter. you own your data, fully. (activeloop.ai, captured 2026-06.)
why a store built for ai matters
a plain database was designed for rows of numbers and text, in a world before ai. it can be bolted onto meaning-based search, but the vectors live in a separate system and the two drift apart. deep lake was designed the other way around: vectors and structured data are first-class together, so the store honeycomb writes to is the same store it searches, with no seam in between.
this is also why memory in honeycomb can be inspected and repaired instead of trapped behind someone else closed recall api. every note is a real row you can read, scope, and version. when a fact goes stale, the newer version supersedes it and the history stays. the memory gets sharper as it grows rather than turning into a pile you cannot audit.
the practical promise is simple. your memory is yours, it is cheap to keep, and it is durable. deep lake gives the memories somewhere honest to live, and honeycomb gives every assistant one consistent way to use them.
deep lake is the database for ai built by activeloop: a store that holds exact data and meaning-vectors together, keeps full version history, and can run in your own cloud. the facts on this page are rewritten in honeycomb own words from activeloop public materials.
common questions
do i need to understand databases to use this?
no. you never touch deep lake directly. honeycomb reads and writes it for you, and a friendly dashboard shows what has been remembered. this page exists for the curious, not as a requirement.
what does "you own your data" mean here?
deep lake is serverless and can run in your own cloud account (google cloud, azure, or amazon). that means the rows holding your memory can sit in a bucket you control, inside your own perimeter, rather than on a vendor server you cannot see. (activeloop.ai, captured 2026-06.)
why is keeping memory so cheap?
deep lake is built to run analytics-style queries directly against low-cost object storage, which activeloop reports as far cheaper than traditional cloud data platforms at scale. keeping notes around does not run up a big bill. (deeplake.ai, captured 2026-06.)
is anything ever lost when memory changes?
no. deep lake keeps a full version history. when a fact changes, the new version is added and the old one is marked replaced, so you can always see what was true before. nothing is overwritten silently.