Snowflake Cortex offers powerful machine learning capabilities. There’s no doubt there. But understanding its complex pricing structure? That’s where things get complicated.
Cortex costs vary significantly based on compute resources, storage, and the specific AI functions you need.
This article breaks down:
- What exactly Snowflake Cortex is
- Snowflake Cortex’s key capabilities
- Top use cases
- How much Snowflake Cortex costs
- Snowflake Cortex FAQs
Read on to learn everything you need to know about Snowflake Cortex pricing, benefits, and more.
What Is Snowflake Cortex?
Launched in 2023, Snowflake Cortex is Snowflake’s first stride into the world of AI. Cortex acts as Snowflake’s internal AI and ML-powered platform engine designed to help you build and maintain enterprise-grade workloads. It helps your team run LLMs, perform data analytics, build intelligent applications without ever having to move outside of snowflake, and build data agents.
Cortex currently supports an ever-growing catalog of pre-integrated LLMs like Snowflake ARctic, Reka, Google, Meta Llama 3, and Mistral. It can also support RAG (retrieval-augmented generation) and document intelligence.
Let’s take a closer look at the many benefits Snowflake Cortex brings to the table to see exactly how it can help support your team.
Snowflake Cortex’s Key Capabilities
Snowflake Cortex includes a wide range of features.
Its generally available features include:
- Snowflake Copilot: An LLM assistant that uses natural language queries to support understanding of your data and create SQL queries.
- Snowflake Cortex LLM Functions: Use an LLM of your choice to parse, query, translate, generate text, and summarize SQL or Python code.
- Cortex Fine-Tuning: Adapt your LLM to better suit your needs and improve accuracy and performance for certain tasks.
- Document AI: Intelligent document processing (IDP) that helps you extract the context of a large scale of documents.
- Cortex Search: Text search that helps you comb your data with an LLM helper.
- Cortex Analyst: Natural language interface for any business questions.
- ML Functions: Uses predictive analysis to pull Snowflake data performance insights.
And the follow are features that are still in public preview:
- Cortex Agents: Use structured and unstructured data to deliver insights.
- AI Observability: Evaluates, debugs, and optimizes generative AI applications.
What Are Snowflake Cortex’s Top Use Cases?
Snowflake Cortex is an infinitely flexible tool, adapting to your needs as you work and scaling with you.
Its top use cases include:
- Retrieval-augmented generation (RAG) and semantic search. Basically, Cortex uses LLMs to find the context for any proprietary data before replying to your query.
- Natural-language data exploration and processing. Any business user can ask a question, and Cortex will translate that into a SQL query, making it much easier for BI to get the information they need without bothering your engineers.
- Intelligent, automated data processing – at scale. Use Document AI to get structured data from scanned documents and PDFs, easily automating workflows for any department.
- Predictive analysis, no MLOps overhead needed. You can get forecasting, anomaly detection, and classification directly within your ELT pipelines with SQL UDFs, without any added overhead.
- Accelerated analytics workflows. Copilot and Cortex Analyst can help you generate insights, create dashboards, and review your reporting pipeline for optimization opportunities.
- Automated cost anomaly detection. Cortex’s automated cost anomaly detection and spend forecasting helps identify any unusual patterns in Snowflake’s usage before they become budget problems.
How Much Does Snowflake Cortex Cost?
Cortex costs vary depending on the type of service you use.
Here’s a breakdown of how Snowflake costs work depending on the service you need:
| Service | Pricing |
| Virtual warehouse compute | Operation uses compute resources, so you pay based on credits. |
| Serving compute | Compute cost incurred per GB per month of uncompressed indexed data. Costs will continue to incur so long as the service is available to respond to queries, even if not queries are served. |
| EMBED_TEXT tokens compute | You incur a credit cost per token embedded, and costs are only incurred for added or changed documents. |
| Storage | Storage charges on a flat rate per terabyte. |
| Cloud services compute | Cloud services compute costs are only calculated if the daily cloud service cost is greater than 10% of the daily warehouse cost for that account. |
It’s important to remember that several factors can also significantly impact your Cortex costs:
- Data volume: More Documents means higher indexing costs
- Query frequency: Impacts serving compute costs
- Model choice: Larger models cost more per request
- Warehouse size: Oversized warehouse waste credits on AI workloads
Let’s look at a real-world FinOps situation to see how Cortex pricing impacts your cloud budget:
Say your organization runs monthly cost optimization analyses across 500GB of Snowflake usage data. You want to use Cortex to:
- Analyze 50,000 cost anomaly alerts (which is about 150 tokens per, or 7.5 million tokens) using Llama 3.1 8B for classification
- Index 500GB of historical usage logs with Cortex Search for trend analysis
- Run predictive models to forecast next quarter’s spend
This is what your cost breakdown would look like:
- LLM processing: 7.5 million tokens X [X credits per million tokens] = Y credits
- Cortex Search indexing: 500GB x [Z credits per GB] = W credits
- Predictive ML functions: [V credits for forecasting models]
- Virtual warehouse compute: [U credits for MEDIUM warehouse runtime]
So, your total monthly FinOps AI costs would be Y + W + V + U credits.
NOTE: Exact credit consumption varies by model choice and data volume. Reference Snowflake’s pricing for current model rates.
Is Snowflake Cortex Worth It?
Cortex means AI workflows are a matter of a few button clicks inside Snowflake. You can run your AI directly where your data lives without needing to develop full-stack RAG applications, meaning you can cut down on your experimentation to production pipeline and support more diverse use cases.
How to Optimize Snowflake Cortex Pricing
Snowflake Cortex pricing, like most other Snowflake costs, can easily get out of hand. Stay on top of everything with a strong cost optimization strategy.
Keep Snowflake Cortex costs reasonable by keeping an eye on your indexing:
- Warehouse size optimization: For most Cortex workloads, SMALL or MEDIUM warehouses are more than enough to achieve optimal price-performance ratios.
- Suspend indexing when freshness isn’t required. Or increase your target lag when you don’t need changes in your document to immediately propagate into search.
- Define primary keys. Defining your Cortex Search Service primary keys can help cut back on indexing cost and latency significantly.
And don’t forget about managing your serving costs either. Suspend serving when it isn’t running so you don’t get charged for work that’s not being done.
Tracking all of these variables across multiple Cortex services gets complicated fast. While manual optimization helps, there’s an even better way to keep your data in check.
Third-party Snowflake optimization tools like Yuki will automate everything you need. Right-sizing warehouses? Optimizing query performance? Sending automated alerts when your compute costs get out of hand and shutting things down before you start to break the bank? These are all things Yuki can help with – no additional dev lift needed. Previous clients have seen monthly savings up to 30% after they started using it.
Curious to see how much you can save? Reach out now for your free demo.
