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What is Snowflake gross margin trajectory through 2028?

📖 2,292 words🗓️ Published Jun 21, 2026 · Updated May 5, 2026

Direct Answer

Snowflake's product gross margin is on a trajectory to compress from recent reported levels into a lower band over the next several years, with the base case landing near the company's stated long-term floor. Three forces are pulling it down: AWS inference cost on Cortex (the biggest single line item), Snowpark Container Services compute cost as customers move heavier workloads onto Snowflake, and Iceberg/open-table discounting that is repricing storage attach. Two forces are protecting the floor: the compute-storage separation architecture that lets Snowflake pass utilization gains through without re-architecting, and the multi-year AWS commit that locks in volume discounts as Cortex inference scales. The bear case is a lower print if Cortex agents land before Trainium adoption matures; the bull case is a higher print if Anthropic/OpenAI passthrough margin holds and customer-side optimization tooling sticks. Either way, this is no longer the high gross-margin SaaS story investors anchored on in prior years.

flowchart TD A[Snowflake Revenue Growth] --> B[Gross Margin Trends] B --> C[Product Revenue Mix] C --> D[Compute vs Storage Costs] D --> E[Cloud Provider Margins] E --> F[Pricing Strategy Adjustments] F --> G[Gross Margin Target] B --> H[Operating Leverage Impact]

What Compressed GM in Recent Years

AWS inference cost on Cortex - The single largest compression line. Cortex LLM functions and Cortex Search call out to managed Anthropic and Mistral endpoints, and the cost of goods rides AWS Bedrock pricing. Company commentary has flagged this as a dominant driver of the step-down.
Snowpark Container Services compute - Customers running ML training, vector workloads, and custom containers consume more raw compute per dollar of revenue than classic warehouse queries. Margin per credit on Container Services is structurally lower than on standard warehouses.
Multi-region data movement - Cross-region replication for Cortex enterprise customers and for regulated verticals (financial services, healthcare) carries egress and intra-AWS network cost that does not get fully repriced into the SKU.
Iceberg storage discount pressure - As customers adopt Iceberg-managed tables and external storage, Snowflake loses the storage-margin tailwind that previously cushioned compute volatility. Storage has been a declining mix of total revenue.
AI workload mix vs. core warehouse - The blended cost-of-revenue is rising because AI workloads (lower margin) are growing faster than the core warehouse business (higher margin). This is a mix shift, not a unit-economics regression on the core product.
Mid-market discounting to land share - Quarterly commentary has pointed to deeper discounts on multi-year mid-market deals as Snowflake leans into the segment Databricks has been winning.

What Will Compress GM Going Forward

Cortex agents inference cost - The agents launch (orchestrated multi-step LLM calls with tool use) carries higher inference cost per task vs. single-shot Cortex calls. If agents become a meaningful revenue line, the per-credit margin drag is material.
Anthropic/OpenAI passthrough margin - Snowflake takes a thin spread on managed model calls. Any pricing pressure from Anthropic or OpenAI on enterprise tiers gets absorbed by Snowflake's COGS, not the customer's bill.
Trainium/Inferentia adoption pace - The mitigation is moving Cortex inference to AWS Trainium and Inferentia. Pace of migration is the biggest single swing factor in the coming years.
Mid-market discount-to-acquire - Continued Databricks competition in the mid-market band keeps deal-level discounting elevated, dragging realized ASP and margin per customer.
Customer churn in low-margin verticals - Some retail and ad-tech accounts onboarded in prior years are running at compute-heavy, storage-light profiles that are gross-margin negative on a fully-loaded basis. Churning these helps margin; renewing them at flat pricing hurts.
Data Cloud / Marketplace revenue share - Marketplace and partner-led revenue carries a rev-share that compresses gross margin on the Snowflake-recognized line, even when net contribution is positive.

What Protects the GM Floor

Compute-storage separation architecture - Snowflake's core architectural advantage is that compute scales independently of storage, which means utilization improvements at the AWS layer flow through to gross margin without product re-architecture. This is the structural moat under the floor.
Customer lock-in via stored procedures, UDFs, and Iceberg-managed schemas - Switching cost is real even with Iceberg interop. Renewal pricing power exists, especially at the seven-figure-and-up ARR cohort.
AWS volume discounts at multi-year commit - Snowflake is one of AWS's top consumers. The committed-spend tier discount steps down materially with incremental commit, and Snowflake will cross another tier based on public commentary.
Cortex value-add pricing if it sticks - If Cortex AI SQL functions and Cortex Agents get accepted at premium pricing, the margin per AI dollar is actually accretive vs. AWS passthrough scenarios.
Customer optimization tooling on consumption - Snowflake's own optimization recommendations (auto-suspend, query acceleration, warehouse right-sizing) reduce customer waste, which actually protects renewal revenue and reduces the discount pressure that would otherwise hit gross margin.

The Math: 3 Scenarios

Bear (lower product GM) - Revenue grows at a slower CAGR; AI mix becomes a larger share of total revenue; Trainium migration slips; Databricks captures more new mid-market logos.
Base (near long-term floor product GM) - Revenue grows at a moderate CAGR; AI mix reaches a modest share of total; Trainium migration on schedule; mid-market deal-velocity matches plan.
Bull (higher product GM) - Revenue grows at a faster CAGR; AI mix remains moderate but at premium Cortex pricing that holds; Trainium2 lands ahead of schedule; Databricks competition softens in mid-market.

What Investors Should Watch Each Quarter

Cortex revenue % of total product revenue - Disclosed selectively; watch for explicit % disclosure in future earnings.
RPO growth (current and total) - Current RPO growth is the cleanest forward demand signal; total RPO captures multi-year commit health.
Net Revenue Retention (NRR) - Has stabilized in a range; a significant drop would be a structural warning sign.
Customers with high trailing twelve-month product revenue - Large-customer count growth is the leading indicator for whether enterprise expansion is offsetting mid-market discount.
Sales efficiency (Magic Number) - Watch for trend; sustained low quarters would force OpEx cuts that may further compress gross margin via reduced reinvestment.
OpEx leverage on R&D and S&M - Stock-based comp as % of revenue and headcount growth are the two cleanest leverage signals.
AWS commit disclosure in 10-K commitments footnote - The committed-spend tier number is the cleanest public proxy for the AWS volume-discount tailwind.

GM Compression Flow

The Role of Customer Workload Migration in Margin Compression

The shift from traditional Snowflake workloads to AI/ML and containerized compute is a structural driver of gross margin compression. Snowpark Container Services, which allows customers to run custom Python, R, and Java applications directly on Snowflake, consumes more compute resources per query than standard SQL workloads. As Snowflake targets growth for Snowpark workloads, this mix shift will add downward pressure on product gross margins. The offset is that these workloads also drive higher storage attach and longer customer retention, but the near-term margin impact is unavoidable.

How Iceberg and Open Table Formats Reshape Storage Economics

Snowflake's embrace of Apache Iceberg and open table formats is a deliberate trade-off: lower storage margins for higher compute volume. By supporting Iceberg tables, Snowflake allows customers to store data in their own cloud buckets rather than Snowflake's proprietary storage. This decouples storage revenue from compute revenue, reducing the storage attach rate that historically boosted gross margins. Management expects this trend to accelerate as enterprises demand data portability and multicloud flexibility. The net effect is a drag on product gross margins, partially offset by higher compute consumption as customers run more queries on their own data.

The Competitive Pricing Response to Databricks and BigQuery

Snowflake's pricing strategy is reacting to competitive pressure from Databricks' Unity Catalog and Google BigQuery's flat-rate pricing. Snowflake introduced consumption-based discounts for customers committing to multi-year contracts, effectively reducing per-credit pricing for large accounts. This pricing flexibility is necessary to retain enterprise customers who are evaluating alternative platforms, but it directly compresses gross margins. The counterbalance is that higher customer retention and increased compute consumption from AI workloads can offset unit price declines, but the net margin impact remains negative in the near term.

Key Timeline Events Shaping the Trajectory

The path to 2028 is not linear—it is punctuated by specific catalyst events that will accelerate or decelerate margin compression. The most significant near-term event is the ramp of Cortex Search and Cortex Analyst, which carry lower margins than classic warehouses but higher margins than raw inference. If these products achieve meaningful adoption before the next AWS commit renegotiation, they can temporarily stabilize margins in the 2025-2026 window. The second inflection point arrives with the Iceberg catalog migration deadline, where customers moving off proprietary storage formats will force Snowflake to discount storage attach more aggressively—this pressure intensifies as the 2027 contract renewal cycle begins. The third event is the potential introduction of Snowflake's own inference silicon (via its Trainium relationship), which could structurally lower COGS for Cortex workloads starting in late 2027, creating a margin recovery path into 2028.

The Competitive Pricing Feedback Loop

Snowflake's gross margin trajectory is not determined in isolation—it is heavily influenced by pricing moves from Databricks and BigQuery. When Databricks cuts per-credit pricing on serverless SQL or BigQuery offers flat-rate discounts for Iceberg workloads, Snowflake must respond with its own pricing adjustments, which directly flow through to reported margins. This competitive dynamic creates a ratchet effect: each round of price compression is permanent because customers anchor to the new lower price point. The most visible example is the Iceberg storage discount program, which was a direct response to open-table format support in competing platforms. As competitors continue to push lower prices for multi-cloud, open-format workloads, Snowflake's margin floor will be tested repeatedly through 2028.

The Optimization Offset That May Not Scale

Management has consistently pointed to customer-side optimization tooling (like query pruning, materialized views, and auto-suspend) as a margin protection mechanism—the logic being that as customers optimize their usage, Snowflake can deliver the same workload value with fewer credits, preserving revenue while reducing COGS. However, this offset has diminishing returns. Early adopters of Snowflake already optimized heavily during the 2022-2024 efficiency wave, leaving less low-hanging fruit for future optimization. The remaining optimization opportunities are in newer workloads (Cortex, Container Services) where usage patterns are still forming. If these workloads grow faster than optimization can keep pace, the net effect will be margin compression rather than protection. The key question for 2028 is whether optimization tooling can outrun the structural margin dilution from new product adoption.

FAQ

Is Snowflake's gross margin really going to drop below its long-term floor? The base case trajectory points to gross margin landing near the company's stated long-term floor, down from higher recent levels. The decline is driven by rising costs from AWS inference for Cortex and Snowpark Container Services, but the company's stated floor provides a target range rather than a hard guarantee.

What is the biggest factor pulling margins down? AWS inference cost on Cortex is the single largest line item pressuring margins. As Snowflake scales AI workloads, these compute expenses increase, though multi-year AWS commitments help lock in volume discounts to partially offset the impact.

How does Snowpark Container Services affect gross margin? It adds compute costs as customers shift heavier workloads onto Snowflake's platform. This is a second major drag, but it also represents growing usage that can be optimized over time through Snowflake's compute-storage separation architecture.

Will Iceberg and open-table formats hurt margins permanently? They repricing storage attach by offering more flexibility, which can reduce per-unit storage revenue. However, the impact is gradual and partly offset by higher compute usage, so it's a contributing factor rather than a dominant one.

Could gross margin ever fall significantly below the floor? In a bear case scenario, yes—if Cortex agents scale quickly before AWS Trainium adoption matures, margins could dip further. This depends on the timing of cost optimization and customer-side tooling adoption.

What could keep margins at the higher end of the range? A bull case is possible if Anthropic or OpenAI passthrough margins hold steady and customer-side optimization tools improve efficiency. This would require favorable mix shifts and slower cost growth from AI workloads.

Bottom Line

Snowflake gross margin is no longer a high-margin story; the realistic base case is near the company's long-term floor, with Cortex inference cost as the single biggest swing factor and Trainium adoption pace as the single biggest mitigation. Investors who anchor on the prior margin profile will misread every Cortex-revenue beat as a margin miss; the right frame is that Snowflake is trading some structural gross margin for the right to be the system of record for enterprise AI workloads.

Related on PULSE

[What data sources are most effective for training AI models to predict next best action in complex enterprise deals?](/knowledge/q16721)
[How does the expanding size of B2B buying committees increase the risk of vendor consolidation paralysis?](/knowledge/q16720)
[Which vendor consolidation strategies are failing most often when integrating AI sales tools into existing stacks?](/knowledge/q16719)
[Why are longer sales cycles now correlating with a shift from pipeline velocity to deal value predictability?](/knowledge/q16718)
[What specific metrics are B2B RevOps teams using to measure AI's impact on lead quality in the top-of-funnel?](/knowledge/q16717)

Sources

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![What is Snowflake gross margin trajectory through 2028?](https://i0.wp.com/fourweekmba.com/wp-content/uploads/2023/03/snowflake-gross-margins.png?fit=2560%2C1931&ssl=1)

## Direct Answer

Snowflake's product gross margin is on a trajectory to compress from recent reported levels into a lower band over the next several years, with the base case landing near the company's stated long-term floor. Three forces are pulling it down: AWS inference cost on Cortex (the biggest single line item), Snowpark Container Services compute cost as customers move heavier workloads onto Snowflake, and Iceberg/open-table discounting that is repricing storage attach. Two forces are protecting the floor: the compute-storage separation architecture that lets Snowflake pass utilization gains through without re-architecting, and the multi-year AWS commit that locks in volume discounts as Cortex inference scales. The bear case is a lower print if Cortex agents land before Trainium adoption matures; the bull case is a higher print if Anthropic/OpenAI passthrough margin holds and customer-side optimization tooling sticks. Either way, this is no longer the high gross-margin SaaS story investors anchored on in prior years.

```mermaid
flowchart TD
A[Snowflake Revenue Growth] --> B[Gross Margin Trends]
B --> C[Product Revenue Mix]
C --> D[Compute vs Storage Costs]
D --> E[Cloud Provider Margins]
E --> F[Pricing Strategy Adjustments]
F --> G[Gross Margin Target]
B --> H[Operating Leverage Impact]
```

## What Compressed GM in Recent Years

![What is Snowflake gross margin trajectory through 2028? — What Compressed GM in Recent Years](https://image.pollinations.ai/prompt/high%20quality%20editorial%20business%20professional%20office%20photograph%20illustrating%20What%20Compressed%20GM%20in%20Recent%20Years%20What%20is%20Snowflake%20gross%20margin%20trajectory%20thr%2C%20realistic%20magazine%20style%2C%20warm%20light%2C%20no%20text%2C%20no%20watermark?width=1200&height=675&nologo=true&model=flux&seed=27333)


- **AWS inference cost on Cortex** - The single largest compression line. Cortex LLM functions and Cortex Search call out to managed Anthropic and Mistral endpoints, and the cost of goods rides AWS Bedrock pricing. Company commentary has flagged this as a dominant driver of the step-down.
- **Snowpark Container Services compute** - Customers running ML training, vector workloads, and custom containers consume more raw compute per dollar of revenue than classic warehouse queries. Margin per credit on Container Services is structurally lower than on standard warehouses.
- **Multi-region data movement** - Cross-region replication for Cortex enterprise customers and for regulated verticals (financial services, healthcare) carries egress and intra-AWS network cost that does not get fully repriced into the SKU.
- **Iceberg storage discount pressure** - As customers adopt Iceberg-managed tables and external storage, Snowflake loses the storage-margin tailwind that previously cushioned compute volatility. Storage has been a declining mix of total revenue.
- **AI workload mix vs. core warehouse** - The blended cost-of-revenue is rising because AI workloads (lower margin) are growing faster than the core warehouse business (higher margin). This is a mix shift, not a unit-economics regression on the core product.
- **Mid-market discounting to land share** - Quarterly commentary has pointed to deeper discounts on multi-year mid-market deals as Snowflake leans into the segment Databricks has been winning.

## What Will Compress GM Going Forward

![What is Snowflake gross margin trajectory through 2028? — What Will Compress GM Going Forward](https://image.pollinations.ai/prompt/high%20quality%20editorial%20business%20professional%20office%20photograph%20illustrating%20What%20Will%20Compress%20GM%20Going%20Forward%20What%20is%20Snowflake%20gross%20margin%20trajectory%20th%2C%20realistic%20magazine%20style%2C%20warm%20light%2C%20no%20text%2C%20no%20watermark?width=1200&height=675&nologo=true&model=flux&seed=25089)


- **Cortex agents inference cost** - The agents launch (orchestrated multi-step LLM calls with tool use) carries higher inference cost per task vs. single-shot Cortex calls. If agents become a meaningful revenue line, the per-credit margin drag is material.
- **Anthropic/OpenAI passthrough margin** - Snowflake takes a thin spread on managed model calls. Any pricing pressure from Anthropic or OpenAI on enterprise tiers gets absorbed by Snowflake's COGS, not the customer's bill.
- **Trainium/Inferentia adoption pace** - The mitigation is moving Cortex inference to AWS Trainium and Inferentia. Pace of migration is the biggest single swing factor in the coming years.
- **Mid-market discount-to-acquire** - Continued Databricks competition in the mid-market band keeps deal-level discounting elevated, dragging realized ASP and margin per customer.
- **Customer churn in low-margin verticals** - Some retail and ad-tech accounts onboarded in prior years are running at compute-heavy, storage-light profiles that are gross-margin negative on a fully-loaded basis. Churning these helps margin; renewing them at flat pricing hurts.
- **Data Cloud / Marketplace revenue share** - Marketplace and partner-led revenue carries a rev-share that compresses gross margin on the Snowflake-recognized line, even when net contribution is positive.

## What Protects the GM Floor

![What is Snowflake gross margin trajectory through 2028? — What Protects the GM Floor](https://image.pollinations.ai/prompt/high%20quality%20editorial%20business%20professional%20office%20photograph%20illustrating%20What%20Protects%20the%20GM%20Floor%20What%20is%20Snowflake%20gross%20margin%20trajectory%20through%20202%2C%20realistic%20magazine%20style%2C%20warm%20light%2C%20no%20text%2C%20no%20watermark?width=1200&height=675&nologo=true&model=flux&seed=59889)


- **Compute-storage separation architecture** - Snowflake's core architectural advantage is that compute scales independently of storage, which means utilization improvements at the AWS layer flow through to gross margin without product re-architecture. This is the structural moat under the floor.
- **Customer lock-in via stored procedures, UDFs, and Iceberg-managed schemas** - Switching cost is real even with Iceberg interop. Renewal pricing power exists, especially at the seven-figure-and-up ARR cohort.
- **AWS volume discounts at multi-year commit** - Snowflake is one of AWS's top consumers. The committed-spend tier discount steps down materially with incremental commit, and Snowflake will cross another tier based on public commentary.
- **Cortex value-add pricing if it sticks** - If Cortex AI SQL functions and Cortex Agents get accepted at premium pricing, the margin per AI dollar is actually accretive vs. AWS passthrough scenarios.
- **Customer optimization tooling on consumption** - Snowflake's own optimization recommendations (auto-suspend, query acceleration, warehouse right-sizing) reduce customer waste, which actually protects renewal revenue and reduces the discount pressure that would otherwise hit gross margin.

## The Math: 3 Scenarios

- **Bear (lower product GM)** - Revenue grows at a slower CAGR; AI mix becomes a larger share of total revenue; Trainium migration slips; Databricks captures more new mid-market logos.
- **Base (near long-term floor product GM)** - Revenue grows at a moderate CAGR; AI mix reaches a modest share of total; Trainium migration on schedule; mid-market deal-velocity matches plan.
- **Bull (higher product GM)** - Revenue grows at a faster CAGR; AI mix remains moderate but at premium Cortex pricing that holds; Trainium2 lands ahead of schedule; Databricks competition softens in mid-market.

## What Investors Should Watch Each Quarter

- **Cortex revenue % of total product revenue** - Disclosed selectively; watch for explicit % disclosure in future earnings.
- **RPO growth (current and total)** - Current RPO growth is the cleanest forward demand signal; total RPO captures multi-year commit health.
- **Net Revenue Retention (NRR)** - Has stabilized in a range; a significant drop would be a structural warning sign.
- **Customers with high trailing twelve-month product revenue** - Large-customer count growth is the leading indicator for whether enterprise expansion is offsetting mid-market discount.
- **Sales efficiency (Magic Number)** - Watch for trend; sustained low quarters would force OpEx cuts that may further compress gross margin via reduced reinvestment.
- **OpEx leverage on R&D and S&M** - Stock-based comp as % of revenue and headcount growth are the two cleanest leverage signals.
- **AWS commit disclosure in 10-K commitments footnote** - The committed-spend tier number is the cleanest public proxy for the AWS volume-discount tailwind.

## GM Compression Flow

```mermaid
graph LR
  D1["AWS Bedrock Inference Cost"] --> C1["Cortex COGS Pressure"]
  D2["Snowpark Container Compute"] --> C2["Mix Shift Drag"]
  D3["Iceberg Storage Discount"] --> C3["Storage Margin Loss"]
  D4["Mid-Market Discounting"] --> C4["ASP Compression"]
  D5["Cortex Agents Launch"] --> C1
  C1 --> Z["Blended Product GM Near Floor"]
  C2 --> Z
  C3 --> Z
  C4 --> Z
  P1["AWS Commit Tier Discount"] --> F["Long-Term Floor"]
  P2["Trainium2 Migration"] --> F
  P3["Compute-Storage Separation"] --> F
  P4["Cortex Premium SKU Pricing"] --> F
  P5["Optimization Tooling"] --> F
  Z --> F
  style Z fill:#FFE4B5
  style F fill:#90EE90
  style C1 fill:#FFB6B6
```

## The Role of Customer Workload Migration in Margin Compression

The shift from traditional Snowflake workloads to AI/ML and containerized compute is a structural driver of gross margin compression. Snowpark Container Services, which allows customers to run custom Python, R, and Java applications directly on Snowflake, consumes more compute resources per query than standard SQL workloads. As Snowflake targets growth for Snowpark workloads, this mix shift will add downward pressure on product gross margins. The offset is that these workloads also drive higher storage attach and longer customer retention, but the near-term margin impact is unavoidable.

## How Iceberg and Open Table Formats Reshape Storage Economics

Snowflake's embrace of Apache Iceberg and open table formats is a deliberate trade-off: lower storage margins for higher compute volume. By supporting Iceberg tables, Snowflake allows customers to store data in their own cloud buckets rather than Snowflake's proprietary storage. This decouples storage revenue from compute revenue, reducing the storage attach rate that historically boosted gross margins. Management expects this trend to accelerate as enterprises demand data portability and multicloud flexibility. The net effect is a drag on product gross margins, partially offset by higher compute consumption as customers run more queries on their own data.

## The Competitive Pricing Response to Databricks and BigQuery

Snowflake's pricing strategy is reacting to competitive pressure from Databricks' Unity Catalog and Google BigQuery's flat-rate pricing. Snowflake introduced consumption-based discounts for customers committing to multi-year contracts, effectively reducing per-credit pricing for large accounts. This pricing flexibility is necessary to retain enterprise customers who are evaluating alternative platforms, but it directly compresses gross margins. The counterbalance is that higher customer retention and increased compute consumption from AI workloads can offset unit price declines, but the net margin impact remains negative in the near term.

## Key Timeline Events Shaping the Trajectory
The path to 2028 is not linear—it is punctuated by specific catalyst events that will accelerate or decelerate margin compression. The most significant near-term event is the ramp of Cortex Search and Cortex Analyst, which carry lower margins than classic warehouses but higher margins than raw inference. If these products achieve meaningful adoption before the next AWS commit renegotiation, they can temporarily stabilize margins in the 2025-2026 window. The second inflection point arrives with the Iceberg catalog migration deadline, where customers moving off proprietary storage formats will force Snowflake to discount storage attach more aggressively—this pressure intensifies as the 2027 contract renewal cycle begins. The third event is the potential introduction of Snowflake's own inference silicon (via its Trainium relationship), which could structurally lower COGS for Cortex workloads starting in late 2027, creating a margin recovery path into 2028.

## The Competitive Pricing Feedback Loop
Snowflake's gross margin trajectory is not determined in isolation—it is heavily influenced by pricing moves from Databricks and BigQuery. When Databricks cuts per-credit pricing on serverless SQL or BigQuery offers flat-rate discounts for Iceberg workloads, Snowflake must respond with its own pricing adjustments, which directly flow through to reported margins. This competitive dynamic creates a ratchet effect: each round of price compression is permanent because customers anchor to the new lower price point. The most visible example is the Iceberg storage discount program, which was a direct response to open-table format support in competing platforms. As competitors continue to push lower prices for multi-cloud, open-format workloads, Snowflake's margin floor will be tested repeatedly through 2028.

## The Optimization Offset That May Not Scale
Management has consistently pointed to customer-side optimization tooling (like query pruning, materialized views, and auto-suspend) as a margin protection mechanism—the logic being that as customers optimize their usage, Snowflake can deliver the same workload value with fewer credits, preserving revenue while reducing COGS. However, this offset has diminishing returns. Early adopters of Snowflake already optimized heavily during the 2022-2024 efficiency wave, leaving less low-hanging fruit for future optimization. The remaining optimization opportunities are in newer workloads (Cortex, Container Services) where usage patterns are still forming. If these workloads grow faster than optimization can keep pace, the net effect will be margin compression rather than protection. The key question for 2028 is whether optimization tooling can outrun the structural margin dilution from new product adoption.

## FAQ

**Is Snowflake's gross margin really going to drop below its long-term floor?**  
The base case trajectory points to gross margin landing near the company's stated long-term floor, down from higher recent levels. The decline is driven by rising costs from AWS inference for Cortex and Snowpark Container Services, but the company's stated floor provides a target range rather than a hard guarantee.

**What is the biggest factor pulling margins down?**  
AWS inference cost on Cortex is the single largest line item pressuring margins. As Snowflake scales AI workloads, these compute expenses increase, though multi-year AWS commitments help lock in volume discounts to partially offset the impact.

**How does Snowpark Container Services affect gross margin?**  
It adds compute costs as customers shift heavier workloads onto Snowflake's platform. This is a second major drag, but it also represents growing usage that can be optimized over time through Snowflake's compute-storage separation architecture.

**Will Iceberg and open-table formats hurt margins permanently?**  
They repricing storage attach by offering more flexibility, which can reduce per-unit storage revenue. However, the impact is gradual and partly offset by higher compute usage, so it's a contributing factor rather than a dominant one.

**Could gross margin ever fall significantly below the floor?**  
In a bear case scenario, yes—if Cortex agents scale quickly before AWS Trainium adoption matures, margins could dip further. This depends on the timing of cost optimization and customer-side tooling adoption.

**What could keep margins at the higher end of the range?**  
A bull case is possible if Anthropic or OpenAI passthrough margins hold steady and customer-side optimization tools improve efficiency. This would require favorable mix shifts and slower cost growth from AI workloads.

## Bottom Line

Snowflake gross margin is no longer a high-margin story; the realistic base case is near the company's long-term floor, with Cortex inference cost as the single biggest swing factor and Trainium adoption pace as the single biggest mitigation. Investors who anchor on the prior margin profile will misread every Cortex-revenue beat as a margin miss; the right frame is that Snowflake is trading some structural gross margin for the right to be the system of record for enterprise AI workloads.

## Tags

["snowflake","gross-margin","cortex-ai","aws-inference","data-infrastructure","saas-finance","trainium","databricks-competition","product-margin"]

<!--pillar-weave-->
## Related on PULSE

- [What data sources are most effective for training AI models to predict next best action in complex enterprise deals?](/knowledge/q16721)
- [How does the expanding size of B2B buying committees increase the risk of vendor consolidation paralysis?](/knowledge/q16720)
- [Which vendor consolidation strategies are failing most often when integrating AI sales tools into existing stacks?](/knowledge/q16719)
- [Why are longer sales cycles now correlating with a shift from pipeline velocity to deal value predictability?](/knowledge/q16718)
- [What specific metrics are B2B RevOps teams using to measure AI's impact on lead quality in the top-of-funnel?](/knowledge/q16717)

## Sources

- [Snowflake Reports Financial Results for the Fourth Quarter and Full Year of Fiscal 2025](https://investors.snowflake.com/news/news-details/2025/Snowflake-Reports-Financial-Results-for-the-Fourth-Quarter-and-Full-Year-of-Fiscal-2025/default.aspx)
- [Snowflake SEC Filings (10-K and 10-Q)](https://www.sec.gov/cgi-bin/browse-edgar?action=getcompany&CIK=0001640147&type=10-K)
- [Snowflake Stock Financials on StockAnalysis](https://stockanalysis.com/stocks/snow/financials/)
- [Snowflake Stock Analysis on Public.com](https://public.com/stocks/snow/analysis)
- [Snowflake Investor Relations](https://investors.snowflake.com/overview/default.aspx)

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Sources cited

investors.snowflake.comhttps://investors.snowflake.com/news/news-details/2026/Snowflake-Reports-Financial-Results-for-the-Fourth-Quarter-and-Full-Year-of-Fiscal-2026/default.aspx sec.govhttps://www.sec.gov/cgi-bin/browse-edgar?action=getcompany&CIK=0001640147&type=10-K goldmansachs.comhttps://www.goldmansachs.com/insights/pages/snowflake-cortex-ai-margin-analysis.html morganstanley.comhttps://www.morganstanley.com/ideas/snowflake-fy26-data-infrastructure-outlook bvp.comhttps://www.bvp.com/atlas/state-of-the-cloud-2026 softwareequity.comhttps://softwareequity.com/research/saas-public-multiples-2026 stockanalysis.comhttps://stockanalysis.com/stocks/snow/financials/public.comhttps://public.com/stocks/snow/analysis

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