Databases · head to head
Chroma vs Dgraph
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
- -
Dgraph
Databases
Apache 2.0 distributed graph database written in Go, maintained by Hypermode, queried through GraphQL or its own DQL language.
- 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.; Dgraph sharding is by predicate, so a single very hot predicate lives entirely in one Raft group and cannot be split further; adding nodes does not relieve it and the fix is a data model change.
- They diverge on capability: Chroma covers Embedded mode, Dgraph covers Generated GraphQL API.
- Prices and features above were last checked on 30 August 2026.
Where they differ
Only the attributes on which Chroma and Dgraph actually diverge.
Identical on both: starting price (Free), free tier (Yes), 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 Dgraph
- Generated GraphQL API
- DQL query language
- Predicate sharding
- Raft replication
- Distributed ACID transactions
- Written in Go
- Full-text and geo indexing
- Bulk and live loaders
Both cover
- Apache 2.0 licence
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 Dgraph
- Agent memory in a single application process, where an embedded store avoids adding a network dependencynot Dgraph
- A departmental search application under roughly ten million records where one server is sufficient and simplicity is worth more than headroomnot Dgraph
- Local and CI testing of retrieval code with the same client library used in productionnot Dgraph
Dgraph
- An application whose core data is a graph, such as permissions, social connections or product relationships, where the frontend already consumes GraphQLnot Chroma
- A knowledge graph that outgrew a single machine and needs storage distributed across nodes without a per-core licence negotiationnot Chroma
- Recommendation or fraud-detection features that traverse several hops at request time, where a relational join chain has become the bottlenecknot Chroma
- Teams that want a graph database they can read, fork and self-host under a permissive licence rather than a source-available onenot 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.
Dgraph
- Sharding is by predicate, so a single very hot predicate lives entirely in one Raft group and cannot be split further; adding nodes does not relieve it and the fix is a data model change.
- Stewardship passed from Dgraph Labs to Hypermode after the original company stopped operating independently, so anyone building on it is betting on a second custodian rather than on the original team's roadmap.
- The GraphQL layer is generated and opinionated, so anything it does not express drops you into DQL, which is a second language your team must learn and which no other database speaks.
- The community is a fraction of Neo4j's, so operational answers, tuning experience, hiring and third-party tooling are all thinner, and unusual failure modes in a Zero and Alpha cluster leave you reading source rather than a forum.
- There is no portable graph standard to migrate to; Cypher, Gremlin and SPARQL are all different query models, so the schema and every query is a rewrite if you later leave, and that cost grows with the application.
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
Dgraph
Free- CommunityFree
- Native GraphQL
- Graph queries
- Full-text search
- Cloud$39/month
- Managed service
- Auto-scaling
- Enterprise support
Which should you pick?
Choose Chroma if
- You need embedded mode.
- You want to start without paying.
- You also want single-node server.
Choose Dgraph if
- You need generated graphql api.
- You want to start without paying.
- You work on Linux, Mac, Docker, Web.
- You also want dql query language.
Questions people ask
- Is Chroma or Dgraph better?
- Neither clearly leads. Chroma starts at Free and Dgraph at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Chroma or Dgraph?
- Chroma starts at Free and Dgraph at Free.
- Does Chroma or Dgraph run on more platforms?
- Chroma runs on Web. Dgraph runs on Linux, Mac, Docker, Web.
- 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 Dgraph is typically brought in for.
- What can Chroma do that Dgraph cannot?
- Chroma covers Embedded mode, Single-node server, Distributed architecture, Vector search. Dgraph covers Generated GraphQL API, DQL query language, Predicate sharding, Raft replication. Both handle Apache 2.0 licence.
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.
Dgraph: Is Dgraph open source?
Yes. The current repository is Apache 2.0, which is a permissive OSI licence, and the project is at v25 under Hypermode's maintenance.
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.
Dgraph: Is it really GraphQL?
It serves a generated GraphQL API, which is real GraphQL for clients. Its native language, DQL, resembles GraphQL syntactically but is Dgraph's own language and is not the GraphQL specification.
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.
Dgraph: How does it compare to Neo4j?
Neo4j has the larger ecosystem, Cypher, and far more operational precedent. Dgraph distributes storage across nodes by default and gives you a GraphQL endpoint without writing resolvers. The choice usually turns on whether you need horizontal scale and a GraphQL surface more than you need ecosystem depth.
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.
Dgraph: What does a production cluster look like?
At minimum a set of Zero nodes for coordination and a replicated set of Alpha nodes for data, typically three of each for fault tolerance, which is a meaningfully larger operational footprint than a single graph server.
Chroma: What licence is it under?
Apache 2.0, which permits self-hosting and embedding in commercial products without a competing-use restriction.
Dgraph: Who maintains it now?
Hypermode. Dgraph Labs, the original company, no longer operates it, and that change of custodian is the main non-technical risk to weigh.
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