Databases · head to head
Chroma vs Convex
Chroma
Databases
Apache 2.0 vector and full-text search engine that runs as an embedded library, a single server or a distributed cloud service.
- From
- Free
- Rated
- -
Convex
Databases
Reactive backend combining a document database, TypeScript server functions and live queries, source-available under the Functional Source Licence.
- From
- Free
- Rated
- -
The short version
- Each has a real cost: Chroma on a single node, available memory sets a hard upper bound on collection size, roughly 245,000 records per gigabyte of RAM at 1024 dimensions, so capacity planning is a memory purchase and the ceiling arrives without warning.; Convex the Functional Source Licence is not an OSI open source licence: competing use is prohibited until each release reaches its second anniversary and converts to Apache 2.0, so you may self-host but you may not build a service on it.
- They diverge on capability: Chroma covers Embedded mode, Convex covers Reactive queries.
- Prices and features above were last checked on 30 August 2026.
Where they differ
Only the attributes on which Chroma and Convex actually diverge.
Identical on both: starting price (Free), free tier (Yes), platforms (Web), user rating (Not yet rated), category (Databases).
What each one covers
Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.
Only in Chroma
- Embedded mode
- Single-node server
- Distributed architecture
- Vector search
- Full-text search
- Metadata filtering
- Consistent API across modes
- Multi-language clients
Only in Convex
- Reactive queries
- TypeScript server functions
- ACID transactions
- Document database
- Scheduling and workflows
- File storage
- Text and vector search
- End-to-end types
What people use each for
The jobs each tool is most often brought in to do.
Chroma
- Prototyping retrieval-augmented generation where the priority is having a working index in minutes rather than choosing a permanent storenot Convex
- Agent memory in a single application process, where an embedded store avoids adding a network dependencynot Convex
- A departmental search application under roughly ten million records where one server is sufficient and simplicity is worth more than headroomnot Convex
- Local and CI testing of retrieval code with the same client library used in productionnot Convex
Convex
- Collaborative applications where several users see the same data and every client must reflect a change immediatelynot Chroma
- Agent backends that need durable state, scheduled work and transactional writes without assembling a queue, a database and a cachenot Chroma
- Small product teams who need a complete backend, including auth integration, file storage and subscriptions, without hiring infrastructure engineersnot Chroma
- Prototypes that must become production without a rewrite of the data layer, where end-to-end TypeScript types remove a class of integration bugsnot Chroma
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
Chroma
- On a single node, available memory sets a hard upper bound on collection size, roughly 245,000 records per gigabyte of RAM at 1024 dimensions, so capacity planning is a memory purchase and the ceiling arrives without warning.
- Single-node queries parallelise only up to the number of vCPUs, after which requests queue and latency rises linearly with concurrency, so throughput problems appear as a slow application rather than as errors.
- The distributed deployment behind Chroma Cloud is a different architecture from the embedded library, so latency, consistency and failure behaviour observed in a local prototype do not predict production behaviour.
- The open source server has no built-in authentication or multi-tenancy worth relying on, so a self-hosted deployment needs its own auth proxy and network controls before anything untrusted can reach it.
- The project has moved quickly through major internal rewrites and version changes, so upgrades have historically involved data migrations and client changes, and pinning versions is necessary rather than cautious.
Convex
- The Functional Source Licence is not an OSI open source licence: competing use is prohibited until each release reaches its second anniversary and converts to Apache 2.0, so you may self-host but you may not build a service on it.
- Transactions are bounded at one second of user code, 16 MiB read and written, 32,000 documents scanned and 16,000 written, so every backfill, migration or bulk import has to be chunked into scheduled batches rather than written as a single operation.
- There is no SQL and no query planner; you declare up to 32 indexes per table and traverse them, and joins are loops in TypeScript, so an unanticipated access pattern requires a schema and index change rather than a new query.
- It is not an analytical database, so reporting means streaming data out to a warehouse and BI tools cannot be pointed at Convex directly, which adds a pipeline the architecture diagram did not originally include.
- The application is written against Convex's function and client APIs rather than a standard protocol, so leaving means rewriting the data access layer and replacing the reactivity model, not repointing a connection string.
Pricing, plan by plan
Chroma
Free- StarterFree
- 10 databases
- 10 team members
- Community Slack access
- Team$250/month
- 100 databases
- 30 team members
- $100 in included credits
- Enterprise$null/month
- Unlimited databases
- Unlimited team members
- Dedicated support
Convex
Free- Free & StarterFree
- Supports 1-6 developers
- Reactive database
- File storage
- Professional$25/month per developer
- Supports up to 20 developers
- All Starter features
- Log streaming
- Business & Enterprise$2500/month minimum
- Supports 50+ developers
- SAML/SSO
- Service SLAs
Which should you pick?
Choose Chroma if
- You need embedded mode.
- You want to start without paying.
- You also want single-node server.
Choose Convex if
- You need reactive queries.
- You want to start without paying.
- You also want typescript server functions.
Questions people ask
- Is Chroma or Convex better?
- Neither clearly leads. Chroma starts at Free and Convex at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Chroma or Convex?
- Chroma starts at Free and Convex at Free.
- Does Chroma or Convex run on more platforms?
- Both run on Web, so platform support will not decide this one for you.
- Can I use Chroma for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Chroma best used for?
- Chroma is most often used for prototyping retrieval-augmented generation where the priority is having a working index in minutes rather than choosing a permanent store, agent memory in a single application process, where an embedded store avoids adding a network dependency, a departmental search application under roughly ten million records where one server is sufficient and simplicity is worth more than headroom, local and ci testing of retrieval code with the same client library used in production. Of those, prototyping retrieval-augmented generation where the priority is having a working index in minutes rather than choosing a permanent store and agent memory in a single application process, where an embedded store avoids adding a network dependency are not what Convex is typically brought in for.
- What can Chroma do that Convex cannot?
- Chroma covers Embedded mode, Single-node server, Distributed architecture, Vector search. Convex covers Reactive queries, TypeScript server functions, ACID transactions, Document database.
Answered from the vendors’ own pages
Chroma: Do I need to run a server?
No. Chroma runs embedded in your process with persistence to a local directory, which is how most projects start. The server and distributed modes exist for when multiple clients or larger collections require them.
Convex: Is Convex open source?
It is source-available under FSL-1.1-Apache-2.0. You may read, modify and self-host it, but competing use is prohibited until each release converts to Apache 2.0 on its second anniversary.
Chroma: How large can a single node get?
The project puts single-node deployments at fewer than about ten million records across a handful of collections, with collection size bounded by system memory at roughly 245,000 records per gigabyte at 1024 dimensions.
Convex: Can I self-host it?
Yes. The backend, dashboard and CLI can run on your own infrastructure, with most of the features of the cloud product. Self-hosted instances include a telemetry beacon that can be disabled.
Chroma: Is Chroma Cloud the same software?
It is the same API and project, but the distributed deployment is a different architecture, using independent services, object storage and SSD caches rather than a single process. Behaviour under load differs accordingly.
Convex: Does it support SQL?
No. Data is accessed through a TypeScript query builder over declared indexes. Relationships are traversed in code, which is explicit and type-safe but means no ad hoc querying.
Chroma: How does it compare with pgvector?
pgvector keeps vectors in a Postgres database you already operate, with SQL, joins and transactions. Chroma is a dedicated retrieval engine with a lower setup cost and a retrieval-shaped API. If you already run Postgres, pgvector removes a system; if you do not, Chroma removes a decision.
Convex: What happens if a mutation exceeds the limits?
It fails rather than running longer, so bulk work must be split into batches and scheduled. The limits are per transaction: one second of user code, 16 MiB read and written, 32,000 documents scanned and 16,000 written.
Chroma: What licence is it under?
Apache 2.0, which permits self-hosting and embedding in commercial products without a competing-use restriction.
Convex: How does reactivity actually work?
Queries are deterministic functions and Convex records the data each one read. When a mutation changes that data, affected queries are re-run and subscribed clients receive the new result, so cache invalidation is handled by the platform.
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