Snowflake Cortex AI
Generative AI infrastructure software
Generative AI software
- Features
- Ease of use
- Ease of management
- Quality of support
- Affordability
- Market presence
Take the quiz to check if Snowflake Cortex AI and its alternatives fit your requirements.
Pay-as-you-go
Free Trial
Free version unavailable
Small
Medium
Large
- Healthcare and life sciences
- Banking and insurance
- Retail and wholesale
What is Snowflake Cortex AI
Snowflake Cortex AI is a set of generative AI and machine learning capabilities delivered within the Snowflake Data Cloud, designed to help teams build and run AI features close to governed enterprise data. It supports use cases such as text generation and summarization, semantic search and retrieval-augmented generation (RAG), and building data-driven assistants inside analytics and data applications. Cortex emphasizes in-warehouse execution, SQL- and Python-accessible APIs, and integration with Snowflake governance and security controls. It primarily targets data platform teams, analytics engineers, and application developers standardizing AI workloads on Snowflake.
Runs near governed data
Cortex AI operates within the Snowflake environment, which reduces the need to move data to external AI systems for many use cases. This can simplify access control, auditing, and policy enforcement by reusing existing Snowflake security and governance configurations. It also supports patterns where AI outputs must be traceable to curated datasets and controlled data products. For organizations already centralizing data in Snowflake, this can reduce integration overhead compared with assembling separate components.
Unified SQL and Python access
Cortex capabilities are accessible through Snowflake-native interfaces, including SQL functions and developer tooling that fits common data engineering workflows. This lowers friction for teams that already build pipelines, transformations, and analytics in Snowflake. It also enables embedding generative AI steps into existing ELT/ETL and application data flows. Compared with standalone AI platforms, the operational model aligns more closely with data warehouse practices.
Built-in search and RAG primitives
Cortex includes components commonly used for enterprise generative AI applications, such as semantic search and retrieval patterns that support RAG. These primitives help teams implement question answering and document-grounded generation without stitching together as many separate services. When paired with Snowflake data sharing and governed datasets, it supports multi-team reuse of indexed content and embeddings. This is useful for internal knowledge assistants and analytics copilots tied to enterprise data.
Strong Snowflake dependency
Cortex AI is designed to run inside Snowflake, so organizations not standardized on Snowflake may face significant migration or duplication of data to adopt it. Multi-cloud or multi-warehouse strategies may require additional integration work to keep AI features consistent across platforms. This can limit portability of AI workloads compared with vendor-neutral frameworks. It also concentrates operational dependency on Snowflake availability and roadmap.
Model choice and control constraints
Compared with building directly on open frameworks and self-hosted model stacks, Cortex can provide fewer low-level controls over model internals, custom serving, and specialized optimization. Some advanced use cases may require external model hosting, custom fine-tuning pipelines, or bespoke evaluation tooling beyond what is available natively. Teams with strict requirements for model transparency, reproducibility, or custom architectures may need additional components. This can increase architectural complexity for highly specialized AI programs.
Cost and usage governance needed
Generative AI workloads can introduce variable consumption patterns, and running them within a data platform can make spend harder to predict without careful monitoring. Teams typically need quotas, workload isolation, and observability to prevent runaway usage from applications or iterative experimentation. If multiple business units share the same Snowflake environment, chargeback and cost attribution may require additional governance processes. These considerations are common for AI infrastructure but become more visible when AI runs alongside core analytics workloads.
Plan & Pricing
Pricing model: Pay-as-you-go (token- and compute-based). Official rates are published in Snowflake’s “Snowflake Service Consumption Table” (effective February 18, 2026). Key examples (taken verbatim from Table 6 in the official Snowflake Service Consumption Table PDF):
REST API with Prompt Caching (Snowflake-managed compute — $ per 1 million tokens)
- openai-gpt-4.1 (Azure Global): $2.00 input / $8.00 output per 1M tokens.
- openai-gpt-5-mini (Azure Regional): $0.28 input / $2.20 output per 1M tokens.
- openai-gpt-5-nano (Azure Regional): $0.06 input / $0.44 output per 1M tokens.
- mistral-large2: $2.00 input / $6.00 output per 1M tokens.
- llama3.1-8b: $0.22 input / $0.22 output per 1M tokens.
REST API (Snowflake-managed compute — $ per 1 million tokens)
- claude-3-5-sonnet: $3.00 input / $15.00 output per 1M tokens.
- mistral-7b: $0.15 input / $0.20 output per 1M tokens.
- snowflake-llama-3.3-70b: $0.72 input / $0.72 output per 1M tokens.
Snowflake AI Features Credit Table (credits-based) — selected entries
- Cortex Analyst: 67 credits per 1,000 messages.
- Cortex Search: 6.3 credits per GB/month of indexed data.
- Document AI: 8 credits per hour of compute.
- AI Parse Document – Layout: 3.33 credits per 1,000 pages; OCR: 0.5 credits per 1,000 pages.
- Provisioned Throughput reservations (PTU): AWS 0.08 credits per PTU per hour; Azure 0.10 credits per PTU per hour.
Fine-tuning (Snowflake-managed compute — credits per 1M tokens)
- Cortex Fine-tuning – llama3.1-70b (training): 3.40 credits per 1M tokens; inference (Cortex Complete): 2.42 credits per 1M tokens.
- Cortex Fine-tuning – llama3.1-8b (training): 0.64 credits per 1M tokens; inference: 0.38 credits per 1M tokens.
Notes & links in the official docs (see Service Consumption Table):
- Some AI features are billed in USD per million tokens (REST API variants) while others are billed in Snowflake Credits (per the “Credit Table” sections); the Service Consumption Table distinguishes the unit and the exact per-model rate.
- Additional charges may apply (cloud services adjustment, storage, warehouse costs) as described in the same official table and accompanying documentation.
(Extracted only from Snowflake’s official Service Consumption Table PDF — values and units taken directly from Snowflake’s published table.)
Seller details
Snowflake Inc.
Bozeman, Montana, USA
2012
Public
https://www.snowflake.com/
https://x.com/SnowflakeDB
https://www.linkedin.com/company/snowflake-computing/
