Developer Tools · head to head
Depot vs DynamoDB
Depot
Developer Tools
Cloud-based builds and CI runs 55x faster with less cost
- From
- Free
- Free plan
- Yes
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
The short version
- Depot is a cloud-based CI/CD platform.
- Free plans on both; paid from $20/mo and $0.625/mo.
- Only Depot lists Depot CI; only DynamoDB lists managed and serverless.
- Where Depot falls short: developer plan minute limits may be insufficient for active development.
- Prices and features on this page were last checked on 13 November 2026.
Where they differ
Only the attributes on which Depot and DynamoDB actually diverge.
Identical on both: starting price (Free), free tier (Yes).
Not yet recorded: pricing model for Depot.
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 Depot
- Depot CI
- Container builds
- GitHub Actions runners
- Distributed caching
- Build API
- Depot Registry
- Cross-platform builds
- SSH debugging
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
What people use each for
The jobs each tool is most often brought in to do.
Depot
- Accelerating Docker builds for container-based developmentnot DynamoDB
- Replacing GitHub Actions runners with faster infrastructurenot DynamoDB
- Distributing cache across Monorepo buildsnot DynamoDB
- Debugging failed CI builds interactively with SSHnot DynamoDB
DynamoDB
- High-volume keyed workloads such as sessions, shopping carts, device state or user profiles where the access pattern is fixed and knownnot Depot
- Traffic that spikes unpredictably, where on-demand capacity absorbs a burst without a capacity-planning exercisenot Depot
- Serverless applications on Lambda, where an HTTP-based datastore avoids the connection pooling problem relational databases havenot Depot
- Event or telemetry ingestion where writes vastly outnumber reads and each record is retrieved by a known identifiernot Depot
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
Depot
- Developer plan minute limits may be insufficient for active development
- Startup plan at $200/mo is expensive for smaller teams
- Business plan requires custom pricing consultation
- macOS M2 runners not available on Developer plan
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
Pricing, plan by plan
Depot
Free- Developer$20/mo
- 1 user
- 500 Docker build minutes per month
- 2,000 Depot CI minutes per month
- Startup$200/mo
- Unlimited users
- 5,000 Docker build minutes per month
- 20,000 Depot CI minutes per month
- BusinessOn request
- Custom pricing
- Unlimited users
- Custom monthly compute
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
Which should you pick?
Choose Depot if
- You need depot ci.
- You want to start without paying.
- You work on Web, GitHub, API.
- You also want container builds.
Choose DynamoDB if
- You need managed and serverless.
- You want to start without paying.
- You work on AWS.
- You also want predictable latency.
Questions people ask
- Is Depot or DynamoDB better?
- Neither clearly leads. Depot starts at Free and DynamoDB at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Depot or DynamoDB?
- Depot starts at Free and DynamoDB at Free.
- Does Depot or DynamoDB run on more platforms?
- Depot runs on Web, GitHub, API. DynamoDB runs on AWS.
- Can I use Depot for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Depot best used for?
- Depot is most often used for accelerating docker builds for container-based development, replacing github actions runners with faster infrastructure, distributing cache across monorepo builds, debugging failed ci builds interactively with ssh. Of those, accelerating docker builds for container-based development and replacing github actions runners with faster infrastructure are not what DynamoDB is typically brought in for.
- What can Depot do that DynamoDB cannot?
- Depot covers Depot CI, Container builds, GitHub Actions runners, Distributed caching. DynamoDB covers Managed and serverless, Predictable latency, On-demand or provisioned capacity, Global secondary indexes.
Answered from the vendors’ own pages
Depot: How much faster are Depot runners compared to GitHub Actions?
Depot runners offer 30% faster CPUs and 10x faster networking than standard GitHub-hosted runners, enabling builds to complete significantly faster.
SourceDynamoDB: 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.
Depot: What is included in the Developer plan?
Developer plan ($20/month) includes 500 Docker build minutes, 2,000 Depot CI minutes, 2,000 GitHub Actions minutes, 25 GB storage, unlimited concurrency, and community support.
SourceDynamoDB: 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.
Depot: Can I use Depot with my own AWS account?
Yes, Depot Managed allows deployment within customer AWS accounts. This is available to interested customers; contact sales for details.
SourceDynamoDB: 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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- DynamoDB vs Moonrepo
- DynamoDB vs GitLab
- DynamoDB vs MongoDB
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- DynamoDB vs BigQuery
- DynamoDB vs FaunaDB
- DynamoDB vs Memcached
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- DynamoDB vs PlanetScale
- DynamoDB vs Couchbase
- DynamoDB vs DataGrip
- DynamoDB vs Firebase Realtime Database
- DynamoDB vs Microsoft SQL Server
- DynamoDB vs QuestDB
- DynamoDB vs Vitess
- DynamoDB vs Aiven
- DynamoDB vs CouchDB
- DynamoDB vs Google Cloud SQL
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