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Databases · head to head

DynamoDB vs Sourcegraph

DynamoDB logo

DynamoDB

Databases

AWS-only managed key-value and document database with fixed per-partition throughput limits and no ad hoc queries.

From
Free
Free plan
Yes
Visit DynamoDBFull review
Sourcegraph logo

Sourcegraph

Developer Tools

Code search and intelligence platform for large codebases with Cody AI assistant

From
$16000/year
Free plan
No
Visit SourcegraphFull review

The short version

  • Sourcegraph is an enterprise code intelligence platform.
  • DynamoDB has a free plan, then $0.625/mo; Sourcegraph starts at $16000/yr.
  • Where Sourcegraph falls short: enterprise pricing starting at $16k annually creates significant barrier for small teams.
  • Prices and features on this page were last checked on 13 November 2026.

Where they differ

Only the attributes on which DynamoDB and Sourcegraph actually diverge.

Attributes where DynamoDB and Sourcegraph differ
AttributeDynamoDBSourcegraph
Starting priceFree$16000/year
Pricing modelUsage-basedSubscription
Free tierYesNo
PlatformsAWSWeb, Self-hosted
CategoryDatabasesDeveloper Tools

Not yet recorded: founded for Sourcegraph.

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 DynamoDB

  • Managed and serverless
  • Predictable latency
  • On-demand or provisioned capacity
  • Global secondary indexes
  • Transactions
  • DynamoDB Streams
  • Global tables
  • Point-in-time recovery

Only in Sourcegraph

Nothing recorded that DynamoDB does not also cover.

What people use each for

The jobs each tool is most often brought in to do.

DynamoDB

  • High-volume keyed workloads such as sessions, shopping carts, device state or user profiles where the access pattern is fixed and knownnot Sourcegraph
  • Traffic that spikes unpredictably, where on-demand capacity absorbs a burst without a capacity-planning exercisenot Sourcegraph
  • Serverless applications on Lambda, where an HTTP-based datastore avoids the connection pooling problem relational databases havenot Sourcegraph
  • Event or telemetry ingestion where writes vastly outnumber reads and each record is retrieved by a known identifiernot Sourcegraph

Sourcegraph

  • Large-scale code migrations across hundreds of repositoriesnot DynamoDB
  • Enterprise governance and code ownership trackingnot DynamoDB
  • AI-assisted bug hunting and security pattern detectionnot DynamoDB
  • Onboarding engineers into large unfamiliar codebasesnot DynamoDB
  • Regulatory compliance and code audit trailsnot DynamoDB

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

DynamoDB

  • Access patterns must be designed into the key schema before launch; a query nobody anticipated needs a new global secondary index, which is a full extra copy of the projected attributes billed as storage and as writes, or an offline migration
  • Global secondary indexes are eventually consistent and cannot be read strongly, so a read-after-write against an index can legitimately miss the item that was just written, and application code must be written to tolerate that
  • Per-partition throughput is capped at roughly 3,000 read and 1,000 write units, so a hot key throttles even when the table has spare capacity overall, and the only real fix is changing the key design to spread the load
  • Items are limited to 400 KB and query results paginate at 1 MB, so large or list-shaped data has to be split, offloaded to S3 with a pointer, or read through pagination loops that complicate every consumer
  • It runs only on AWS and the API is proprietary rather than a standard, so moving the data layer means rewriting it; ScyllaDB's Alternator is the only meaningfully compatible target and it brings a much smaller ecosystem

Sourcegraph

  • Enterprise pricing starting at $16k annually creates significant barrier for small teams
  • No free tier or standard plan published; all options require custom sales engagement
  • Complex enterprise-only positioning limits adoption compared to GitHub Copilot's pervasiveness
  • Requires indexing entire codebase; not suitable for small or ephemeral projects

Pricing, plan by plan

DynamoDB

Free
  • On-Demand CapacityOn request
    • Pay-per-request pricing with automatic scaling
    • Read: 0.5 RRU per 4 KB (eventually consistent), 1 RRU per 4 KB (strongly consistent), 2 RRU per 4 KB (transactional)
    • Write: 1 WRU per 1 KB
  • Provisioned CapacityOn request
    • Fixed hourly charges based on reserved capacity
    • RCU rate: $0.00013 per hour (Standard)
    • WCU rate: $0.00065 per hour (Standard)
  • Standard Table Class Storage$0.25/per gb/month
    • $0.25 per GB/month after free tier
    • First 25 GB free per month (free tier)
  • Standard-Infrequent Access Table Class$0.1/per gb/month
    • $0.10 per GB/month

Sourcegraph

$16000/year
  • Enterprise$16,000/yr
    • Scales with team size
    • Credits for AI features per user
    • Deep Search

Which should you pick?

Choose DynamoDB if

  • You need managed and serverless.
  • You want to start without paying.
  • You work on AWS.
  • You also want predictable latency.
DynamoDB review

Choose Sourcegraph if

  • You work on Web, Self-hosted.
Sourcegraph review

Questions people ask

Is DynamoDB or Sourcegraph better?
Neither clearly leads. DynamoDB starts at Free and Sourcegraph at $16000/year, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, DynamoDB or Sourcegraph?
DynamoDB has a free tier; the other does not. Paid plans start at Free for DynamoDB and $16000/year for Sourcegraph.
Does DynamoDB or Sourcegraph run on more platforms?
DynamoDB runs on AWS. Sourcegraph runs on Web, Self-hosted.
Can I use DynamoDB for free?
Yes. DynamoDB has a free tier, so you can try it without paying. Sourcegraph starts at $16000/year.
What is DynamoDB best used for?
DynamoDB is most often used for high-volume keyed workloads such as sessions, shopping carts, device state or user profiles where the access pattern is fixed and known, traffic that spikes unpredictably, where on-demand capacity absorbs a burst without a capacity-planning exercise, serverless applications on lambda, where an http-based datastore avoids the connection pooling problem relational databases have, event or telemetry ingestion where writes vastly outnumber reads and each record is retrieved by a known identifier. Of those, high-volume keyed workloads such as sessions, shopping carts, device state or user profiles where the access pattern is fixed and known and traffic that spikes unpredictably, where on-demand capacity absorbs a burst without a capacity-planning exercise are not what Sourcegraph is typically brought in for.
What can DynamoDB do that Sourcegraph cannot?
DynamoDB covers Managed and serverless, Predictable latency, On-demand or provisioned capacity, Global secondary indexes.

Answered from the vendors’ own pages

DynamoDB: On-demand or provisioned capacity?

On-demand suits unpredictable or spiky traffic and removes capacity planning. Provisioned with autoscaling is considerably cheaper for steady high-volume workloads. Tables can be switched between them, though not arbitrarily often.

DynamoDB: Can I run DynamoDB outside AWS?

No. DynamoDB Local exists for development and testing only. For a production-compatible alternative elsewhere, ScyllaDB's Alternator implements the DynamoDB API, but it is a different system with a different ecosystem.

DynamoDB: Can I run ad hoc queries or analytics?

Not on the table itself. Scans are slow and expensive at scale. The usual pattern is to export to S3 or stream changes out and query them in Athena, Redshift or another analytical engine.

DynamoDB: Is single-table design necessary?

It is the pattern that gets the most from DynamoDB when access patterns are well known, because it lets related items be retrieved in one query. It also makes the model harder to evolve, so many teams reasonably choose multiple simpler tables and accept extra requests.

DynamoDB: What are the real limits I should design around?

400 KB per item, 1 MB per query or scan page, 100 items per transaction, roughly 3,000 read and 1,000 write units per partition, and eventual consistency on global secondary indexes.

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