Machine Learning · head to head
Comet ML vs PlanetScale

Comet ML
Machine Learning
Platform for tracking, comparing, and optimizing ML experiments
- From
- Free
- Rated
- -
The short version
- Each has a real cost: Comet ML the free cloud tier caps data at 25,000 spans a month with 60 day retention; PlanetScale pricing varies significantly across 17+ AWS and GCP regions
- They diverge on capability: Comet ML covers Experiment tracking, PlanetScale covers Database Branching.
Where they differ
Only the attributes on which Comet ML and PlanetScale actually diverge.
| Attribute | Comet ML | PlanetScale |
|---|---|---|
| Pricing model | freemium | usage-based |
| Platforms | Web, Linux, Mac, Windows | Cloud-hosted (AWS, GCP, Azure) |
| Category | Machine Learning | Databases |
| Founded | 2017 | 2018 |
Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated).
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 Comet ML
- Experiment tracking
- Code versioning
- Model registry
- Hyperparameter optimization
- Production monitoring
- PyTorch
- TensorFlow
- Keras
Only in PlanetScale
- Database Branching
- Non-blocking Schema Changes
- Insights
- Horizontal Scaling
- Connection Pooling
- Query Caching
- Automatic Backups
- Global Replication
Both cover
- Web support
What people use each for
The jobs each tool is most often brought in to do.
Comet ML
- LLM observability and monitoringnot PlanetScale
- AI agent testing and debuggingnot PlanetScale
- Experiment tracking for machine learningnot PlanetScale
- Model registry and version managementnot PlanetScale
- ML model training monitoringnot PlanetScale
PlanetScale
- MySQL-compatible applications requiring horizontal scalingnot Comet ML
- PostgreSQL deployments with custom cluster configurationsnot Comet ML
- Multi-region database deployments on AWS or GCPnot Comet ML
- Applications requiring transparent sharding via Vitessnot Comet ML
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
Comet ML
- The free cloud tier caps data at 25,000 spans a month with 60 day retention
- Retention stays at 60 days even on the paid Pro plan, and extending it is a $29 per 100k spans add on
- Overage on Pro is $5 per additional 100,000 spans
- The free MLOps tier is a single user with 100 GB of storage and training hours governed by a fair usage policy
- Pro MLOps is $19 per user per month and caps the team at 10 users
PlanetScale
- Pricing varies significantly across 17+ AWS and GCP regions
- Additional costs for EBS storage beyond base tier, backup storage, and egress
- Dedicated PgBouncer and replicas incur separate charges
- Metal tier pricing increases sharply with larger configurations
Pricing, plan by plan
Comet ML
Free- Free CloudFree
- Up to 10 team members
- 25,000 spans per month
- 60-day data retention
- Pro Cloud$19/month
- Up to 50 team members
- 100,000 spans per month
- 60-day data retention
- MLOps FreeFree
- 1 user with fair usage policy
- Experiment tracking
- Dataset management
- MLOps Pro$19/user/month
- Up to 10 users
- 1,500 training hours included
- 500GB storage included
PlanetScale
Free- Postgres EBS Single-Node (ARM64 PS-5)$5/month
- 512 MiB RAM
- Single-node configuration
- EBS storage included
- Postgres EBS HA (ARM64 PS-5)$15/month
- 512 MiB RAM
- 3-node high-availability setup
- 1 primary + 2 replicas
- Postgres Metal (M-10)$50/month
- 1/8 vCPU, 1 GiB RAM
- 3-node HA configuration
- 10 GiB NVMe storage included
- Vitess Non-Metal 3-Node$39/month
- Sharding-capable database
- x86-64 architecture
- 3-node configuration
Which should you pick?
Choose Comet ML if
- You need experiment tracking.
- You want to start without paying.
- You work on Web, Linux, Mac, Windows.
- You also want code versioning.
Choose PlanetScale if
- You need database branching.
- You want to start without paying.
- You work on Cloud-hosted (AWS, GCP, Azure).
- You also want non-blocking schema changes.
Questions people ask
- Is Comet ML or PlanetScale better?
- Neither clearly leads. Comet ML starts at Free and PlanetScale at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Comet ML or PlanetScale?
- Comet ML starts at Free and PlanetScale at Free.
- Does Comet ML or PlanetScale run on more platforms?
- Comet ML runs on Web, Linux, Mac, Windows. PlanetScale runs on Cloud-hosted (AWS, GCP, Azure).
- Can I use Comet ML for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Comet ML best used for?
- Comet ML is most often used for llm observability and monitoring, ai agent testing and debugging, experiment tracking for machine learning, model registry and version management. Of those, llm observability and monitoring and ai agent testing and debugging are not what PlanetScale is typically brought in for.
- What can Comet ML do that PlanetScale cannot?
- Comet ML covers Experiment tracking, Code versioning, Model registry, Hyperparameter optimization. PlanetScale covers Database Branching, Non-blocking Schema Changes, Insights, Horizontal Scaling. Both handle Web support.
Answered from the vendors’ own pages
Comet ML: Does Comet.ml offer a free plan?
Yes, Comet.ml offers free tiers for both Opik (cloud observability) and MLOps platforms. Free Cloud Opik includes up to 10 team members and 25,000 spans/month. Free MLOps tier is limited to 1 user.
SourcePlanetScale: How much does a PlanetScale Postgres database cost per month?
PlanetScale Postgres pricing starts at $5/month for single-node ARM64 configurations with 512 MiB RAM and $15/month for the same specs in high-availability mode with 1 primary and 2 replicas. Metal tier starts at $50/month for M-10 configuration (1/8 vCPU, 1 GiB RAM). Exact pricing depends on cluster size, node architecture (ARM64 vs x86-64), storage configuration, and selected AWS/GCP region.
SourceComet ML: How many team members can use the free Comet.ml tier?
Free Cloud supports up to 10 team members. The Pro Cloud plan supports up to 50 team members at $19/month.
SourcePlanetScale: Is there a free tier for PlanetScale?
PlanetScale offers a free tier for development and testing workloads. After free tier limits are reached, usage-based pricing applies starting at $5/month for the smallest Postgres single-node configuration, with costs scaling based on cluster size, compute, storage, and additional features like dedicated PgBouncer or replicas.
SourceComet ML: What is a span in Comet.ml pricing?
A span represents a single tracked operation such as model requests or function calls. Free Cloud tier includes 25,000 spans per month.
SourcePlanetScale: What is the difference between PlanetScale ARM64 and x86-64 pricing?
ARM64 instances cost significantly less than x86-64 equivalents. For example, a Postgres EBS HA cluster with 512 MiB RAM costs $15/month on ARM64 but $39/month on x86-64. This pricing difference extends across all cluster sizes, with larger x86-64 configurations reaching up to $5,599/month compared to ARM64 alternatives.
SourceComet ML: Does Comet.ml offer academic pricing?
Yes, a free Pro plan is available for academic users; verification is required via signup.
SourcePlanetScale: What is included in a PlanetScale cluster price versus additional costs?
The advertised cluster price covers the base compute and configured storage. Additional charges apply for EBS storage beyond the base allocation, backup storage, data egress, optional dedicated PgBouncer connections, and replicas beyond the base high-availability configuration. Regional pricing varies across 17+ AWS and GCP zones.
SourceRelated pages
More on PlanetScale
Other head to heads
- Comet ML vs AWS SageMaker
- Comet ML vs Google Vertex AI
- Comet ML vs Azure Machine Learning
- Comet ML vs DataRobot
- Comet ML vs MLflow
- Comet ML vs Snowflake
- Comet ML vs TensorFlow
- Comet ML vs Jupyter
- Comet ML vs LangChain
- Comet ML vs Pinecone
- Comet ML vs Python
- Comet ML vs PyTorch
- Comet ML vs scikit-learn
- Comet ML vs Apache Spark MLlib
- Comet ML vs Weaviate
- Comet ML vs Weights & Biases
- Comet ML vs Alteryx
- Comet ML vs Anaconda
- Comet ML vs Cockroach Labs
- Comet ML vs PostgreSQL
- Comet ML vs Airtable
- Comet ML vs Amazon Aurora
- Comet ML vs Elasticsearch
- Comet ML vs Apache Kafka
- Comet ML vs Meilisearch
- Comet ML vs Turso
- Comet ML vs Azure SQL
- Comet ML vs ClickHouse
- Comet ML vs Couchbase
- Comet ML vs DuckDB
- Comet ML vs MariaDB
- Comet ML vs Oracle Database
- Comet ML vs DataGrip
- Comet ML vs Firebolt
- Comet ML vs Google Cloud SQL
- Comet ML vs MotherDuck
- PlanetScale vs AWS SageMaker
- PlanetScale vs Google Vertex AI
- PlanetScale vs Azure Machine Learning
- PlanetScale vs DataRobot
- PlanetScale vs MLflow
- PlanetScale vs Snowflake
- PlanetScale vs TensorFlow
- PlanetScale vs Jupyter
- PlanetScale vs LangChain
- PlanetScale vs Pinecone
- PlanetScale vs Python
- PlanetScale vs PyTorch
- PlanetScale vs scikit-learn
- PlanetScale vs Apache Spark MLlib
- PlanetScale vs Weaviate
- PlanetScale vs Weights & Biases
- PlanetScale vs Alteryx
- PlanetScale vs Anaconda
- PlanetScale vs Cockroach Labs
- PlanetScale vs PostgreSQL
- PlanetScale vs Airtable
- PlanetScale vs Amazon Aurora
- PlanetScale vs Elasticsearch
- PlanetScale vs Apache Kafka
- PlanetScale vs Meilisearch
- PlanetScale vs Turso
- PlanetScale vs Azure SQL
- PlanetScale vs ClickHouse
- PlanetScale vs Couchbase
- PlanetScale vs DuckDB
- PlanetScale vs MariaDB
- PlanetScale vs Oracle Database
- PlanetScale vs DataGrip
- PlanetScale vs Firebolt
- PlanetScale vs Google Cloud SQL
- PlanetScale vs MotherDuck

