Software · head to head
BigQuery ML vs Cohere
The short version
- Each has a real cost: BigQuery ML not available in BigQuery's Standard edition, so the cheapest tier cannot use it; Cohere aPI-only service with no self-hosted options for most users
- They diverge on capability: BigQuery ML covers SQL-based ML, Cohere covers Generate.
Where they differ
Only the attributes on which BigQuery ML and Cohere actually diverge.
| Attribute | BigQuery ML | Cohere |
|---|---|---|
| Platforms | Web | Api, Cloud |
| Founded | 2008 | 2019 |
Identical on both: starting price (Free), pricing model (usage-based), free tier (Yes), user rating (Not yet rated), category (Unknown).
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 BigQuery ML
- SQL-based ML
- AutoML Tables
- Model export
- Prediction functions
- Feature preprocessing
- BigQuery
- Vertex AI
- TensorFlow
Only in Cohere
- Generate
- Embed
- Rerank
- Classify
- REST API
- SDKs
- Cloud deployment
- Api support
What people use each for
The jobs each tool is most often brought in to do.
BigQuery ML
- Training models in SQL without exporting datanot Cohere
- Linear and logistic regression on warehouse datanot Cohere
- K-means clustering and matrix factorisation for recommendationsnot Cohere
- Time series forecasting with ARIMA_PLUSnot Cohere
- Running imported ONNX, TensorFlow or XGBoost models against BigQuery datanot Cohere
Cohere
- ai tools managementnot BigQuery ML
- Workflow automationnot BigQuery ML
- Reportingnot BigQuery ML
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
BigQuery ML
- Not available in BigQuery's Standard edition, so the cheapest tier cannot use it
- Billed through BigQuery compute and storage rather than as its own product, so training cost tracks data scanned
- Remote models incur extra Agent Platform charges on top
- Externally trained model types such as boosted trees and AutoML run through Agent Platform rather than inside BigQuery
Cohere
- API-only service with no self-hosted options for most users
- Trial tier severely limited at 1,000 calls per month
- Smaller context window compared to some competing APIs
- Less emphasis on safety and alignment compared to competing APIs
Pricing, plan by plan
BigQuery ML
Free- Free TierFree
- 10GB storage
- 1TB queries
- On-Demand$5/TB
- Pay per TB scanned
- ML training costs
Cohere
Free- Free TrialFree
- Rate limited
- Evaluation
- Production$0.4/per-million-tokens
- Full access
- SLA
Which should you pick?
Choose BigQuery ML if
- You need sql-based ml.
- You want to start without paying.
- You also want automl tables.
Choose Cohere if
- You need generate.
- You want to start without paying.
- You work on Api, Cloud.
- You also want embed.
Questions people ask
- Is BigQuery ML or Cohere better?
- Neither clearly leads. BigQuery ML starts at Free and Cohere at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, BigQuery ML or Cohere?
- BigQuery ML starts at Free and Cohere at Free.
- Does BigQuery ML or Cohere run on more platforms?
- BigQuery ML runs on Web. Cohere runs on Api, Cloud.
- Can I use BigQuery ML for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is BigQuery ML best used for?
- BigQuery ML is most often used for training models in sql without exporting data, linear and logistic regression on warehouse data, k-means clustering and matrix factorisation for recommendations, time series forecasting with arima_plus. Of those, training models in sql without exporting data and linear and logistic regression on warehouse data are not what Cohere is typically brought in for.
- What can BigQuery ML do that Cohere cannot?
- BigQuery ML covers SQL-based ML, AutoML Tables, Model export, Prediction functions. Cohere covers Generate, Embed, Rerank, Classify.
Answered from the vendors’ own pages
Cohere: Does Cohere offer a free tier?
Yes. Cohere provides Trial API keys that allow 1,000 free API calls per month across all models and endpoints. Trial keys are rate-limited to 20 requests per minute for Chat endpoints and 5-10 requests per minute for other endpoints, and cannot be used for production or commercial purposes.
SourceCohere: What is the cost structure for production use?
Cohere uses pay-as-you-go pricing based on tokens consumed. Costs vary by model: Command costs from 0.15 to 2.50 USD per 1M input tokens, with output tokens priced higher. Embed models cost 0.10 USD per 1M input tokens. Production keys have monthly billing with invoices at month-end or when charges reach 250 USD.
SourceCohere: Can I self-host Cohere models?
No. Cohere operates as an API-only platform. However, enterprise customers can arrange dedicated or managed deployments through the Model Vault platform starting at 4.00 USD per hour with custom pricing for dedicated instances.
SourceCohere: What are the main differences between Cohere and Claude API?
Cohere excels in cost-effective NLP applications and retrieval-augmented generation (RAG) capabilities. Claude API emphasizes reasoning and safety with Constitutional AI training. Cohere's Command R+ offers similar performance to GPT-4 at 40-50 percent lower cost, while Claude focuses on factual accuracy and transparency.
SourceRelated pages
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