Snowflake validated the Iceberg-first architecture in October 2025. Not "added support" - validated. Data architects spent two years designing Snowflake-optional systems, and Snowflake's October 2025 GA was strategic alignment: full DML for externally managed Iceberg tables. "Treat Snowflake as replaceable compute; Iceberg gives ACID without proprietary warehouse format." S3 as single source of truth. Iceberg metadata. Multiple engines querying the same data. Exit optionality without vendor lock-in.
The catch: Egress fees still apply. AWS charges $0.09/GB ($90/TB) as its baseline data transfer out rate. Cross-region and cross-cloud transfers vary by path and region ($20-$155/TB depending on routing). Same-region transfers are free, but multi-region and multi-cloud migrations - the scenarios enabling vendor independence - incur these costs. Technical portability solved. Financial viability remains unsolved.
Snowflake's Iceberg Endorsement Validates Open Architecture
Snowflake's October 2025 announcement was strategic validation. Data architects have been designing "Snowflake-optional" systems for years. Snowflake heard them.
What Snowflake now provides:
- Full DML support for external Iceberg tables: INSERT, UPDATE, DELETE, MERGE work natively.
- Apache Polaris: Open catalog positioned as "shared control plane" across engines.
- Performance parity goal: Snowflake's strategy is observable - performance parity between external Iceberg and native tables. As one Snowflake engineer noted before the GA announcement: "We want Iceberg/Parquet to be at or as close to parity as possible with our native format. The value add is the platform, not lock in."
Industry direction is visible. Snowflake chose Iceberg over Delta Lake as its open table format for external write support - signaling where multi-engine lakehouse architectures are converging.
The pattern: Store data in Iceberg format on S3. Query with Snowflake today. Retain ability to query with Spark or Trino tomorrow. Architects want multi-engine access - Snowflake for BI, Spark for ETL, Trino for ad-hoc queries. Same data. Different engines. No vendor dependence.
Exit Costs Make Migration Financially Impossible
Open architecture meets financial reality.
You've designed your Iceberg lakehouse on S3. Snowflake queries it beautifully. Then contract renewal comes. Snowflake raises prices 40%. You have options, right?
Not quite.
Migrating 100TB from S3 US-East to a different cloud region costs $2,000-$14,000 in egress fees. Cross-cloud costs $9,000-$15,500. For example, a team pulling 50TB/month across Spark (for ETL) and Snowflake (for BI) faces $4,500-$7,000 monthly in egress costs. Even GetObject API calls add $0.0004/1000 requests - granular charges that compound.
Technical feasibility doesn't equal financial viability.
Architects design migrations assuming metadata-heavy moves - catalog updates, not byte-for-byte transfers. Cloud billing treats them as byte-heavy events. The design intent hits egress reality. You have exit optionality on paper. Not in your budget.
Multi-engine access creates a second problem: Who verifies integrity when Snowflake writes, Spark reads, Trino analyzes? Iceberg solves metadata consistency. Storage needs to solve cryptographic integrity.
Strategic risk Iceberg was supposed to eliminate:
- No negotiating leverage: Switching costs $50K-$150K in egress fees, you're forced into unfavorable terms.
- Single vendor dependence: You built for portability, but financial penalties keep you trapped.
The architecture is vendor-independent. The billing model isn't.
The Open Lakehouse Convergence
Data architects are converging on a pattern: Store everything in Iceberg format on object storage, let multiple engines query it. Bronze/silver in Iceberg on S3. Snowflake for BI, Spark for data science, Trino for ad-hoc analytics. Same tables. Zero data duplication.
Shared principle: Iceberg on S3 = source of truth. Snowflake = one query engine among many.
The promise: "If Snowflake gets too expensive, we have an exit path."
The reality: Exit path costs $50K-$150K in egress fees.
Why Zero-Egress Storage Changes the Equation
Snowflake external Iceberg tables on Akave solve both problems.
Problem 1: Egress Fees Block Portability
Akave charges $0/GB egress. Migrate 100TB to a different engine? Zero cost. Replicate data across regions for multi-engine access? Zero cost. Pull 50TB/month for Spark workloads alongside Snowflake? Zero cost.
Not "discounted egress." Zero. The meter doesn't run.
Problem 2: Verification Across Engines
Iceberg provides ACID semantics and schema evolution. But who verifies data integrity across multiple query engines? If Snowflake writes, Spark reads, Trino analyzes - how do you prove nothing was tampered with?
Akave adds cryptographic integrity proofs at the storage layer. Every Iceberg commit generates a blockchain-anchored receipt binding to snapshot metadata and referenced object hashes - so any engine (Snowflake, Spark, Trino) can verify the same committed state. Data lineage tracked regardless of which engine accesses it.
How It Works
- S3-compatible storage: Iceberg tables on Akave via Snowflake external stages. Same configuration as AWS S3.
- Multi-engine access: Spark, Trino, Flink query the same Iceberg tables - zero egress penalty.
- Blockchain-anchored proofs: Every commit creates tamper-evident receipt. All engines verify the same proof.
Snowflake performance today. Vendor independence tomorrow. Cryptographic audit trails forever.
TCO: 10TB Iceberg Data, 5TB/Month Multi-Engine Access
Not 10% cheaper. 78% cheaper.
The larger value appears when this cost structure becomes a negotiation lever, not a line item. "We can migrate without $150K in egress fees" changes contract terms.
Run your numbers. Calculate what you're currently paying for multi-engine access or replication across regions. Then calculate what you'd pay with zero egress.
Iceberg Unlocks Portability, Akave Makes It Financially Viable
Today: Snowflake performance without compromising external Iceberg architecture. S3 compatibility means zero code changes.
Tomorrow: Switch to Spark, Trino, Flink without egress penalties. Negotiate Snowflake contracts with actual leverage. Multi-engine workloads don't bleed budgets.
Forever: Cryptographic integrity proofs work across any query engine. Data lineage tracked independently of vendors.
The architecture is vendor-independent. The billing model catches up when egress goes to zero.
Future you - six months from now, contract renewal in hand - will thank present you for building exit optionality that doesn't cost $50K-$150K to exercise.
If You're Betting on Iceberg for Exit Optionality, This Is the Missing Piece
Migrate one external stage. Keep your Snowflake queries. Add Spark or Trino when ready. Zero egress penalties. Blockchain-anchored integrity proofs.
FAQ
What makes Apache Iceberg "vendor independent" if egress fees still apply?
Apache Iceberg solves technical lock-in: open table format, ACID semantics, multi-engine compatibility. Your data is portable - Snowflake, Spark, Trino can all query the same Iceberg tables without format conversions. But technical portability doesn't equal financial viability. Egress fees ($20-$140/TB cross-region, $90-$155/TB cross-cloud) make migrations expensive. Migrating 100TB costs $9K-$15.5K in egress alone. Result: you have exit optionality on paper, not in your budget. True vendor independence requires both technical portability (Iceberg delivers) and zero egress economics (Akave delivers).
We already store data in Iceberg format on S3 - doesn't that give us exit optionality?
Yes, technically. You can switch from Snowflake to Spark or Trino without reformatting data. But exit optionality assumes you can afford to exercise it. Here's the gap: S3 charges $90/TB for cross-region egress. A 100TB migration costs $9,000 in egress fees alone. Multi-engine access compounds costs - if you're running Snowflake for BI and Spark for ETL on the same 50TB dataset, you're paying $4,500-$7,000/month in egress. You have the architecture for vendor independence. You don't have the economics. That's what zero egress solves.
How do you actually configure Snowflake external Iceberg tables on Akave?
Three configuration steps: (1) Create an external stage in Snowflake pointing to Akave's S3-compatible endpoint using standard CREATE EXTERNAL STAGE syntax (same as AWS S3). (2) Configure Snowflake catalog integration to reference Iceberg metadata stored on Akave. (3) Use Snowflake's DML commands (INSERT, UPDATE, DELETE, MERGE) on external Iceberg tables - Snowflake GA'd full DML support in October 2025. From Snowflake's perspective, Akave looks identical to S3. Your queries don't change. Your Iceberg metadata structure doesn't change. The difference: egress meter stays at $0 when other engines (Spark, Trino) access the same tables.
If we're running Snowflake for BI and Spark for ETL, what does zero egress change?
It eliminates the cost multiplier on multi-engine architectures. Right now, every time Spark reads training data or pulls transformed datasets from S3 for ETL, you're paying egress fees - even if Snowflake wrote that data an hour ago. A 50TB dataset accessed by both Snowflake (BI queries) and Spark (ETL pipelines) generates $4,500-$7,000/month in egress costs at standard S3 rates. With zero egress, that line item disappears. You can run compute-optimized workloads on Spark, governed analytics on Snowflake, and ad-hoc queries on Trino - all querying the same Iceberg tables - without egress penalties. The architecture you designed for flexibility finally has the economics to match.
How does $50K-$150K in avoided egress fees translate to contract negotiation leverage?
When Snowflake knows switching costs you $50K-$150K in egress fees, they can price accordingly. You have no credible exit threat. But when migration costs $0 in egress, your negotiation position changes. Example scenario: Snowflake proposes 40% price increase at renewal. With S3 egress fees, migration costs more than accepting the increase - you're trapped. With zero egress, you can credibly say "we'll migrate to Databricks/Spark" and mean it. The $50K-$150K isn't just a line item. It's the difference between accepting unfavorable contract terms and having real alternatives. This matters most for teams with 100TB+ datasets where egress costs become prohibitive.
Why does multi-engine access require cryptographic integrity proofs, not just Iceberg metadata?
Iceberg solves metadata consistency (schema evolution, transaction isolation, snapshot management). But Iceberg doesn't verify data integrity across engines. If Snowflake writes a commit, Spark reads it, Trino analyzes it - who proves the data wasn't tampered with between writes? Iceberg metadata tells you "this is the current snapshot." It doesn't tell you "this snapshot is cryptographically verified and unchanged since write." Blockchain-anchored receipts solve this: every Iceberg commit generates a tamper-evident proof. All engines (Snowflake, Spark, Trino) verify the same proof. Data lineage is tracked independently of any single vendor. For regulated industries or cross-vendor incident investigations, cryptographic verification isn't optional - it's the difference between "we think the data is intact" and "we can prove it."
Where does Akave fit if Snowflake's Iceberg support already solves vendor lock-in?
Snowflake's Iceberg support solves technical lock-in (open format, full DML, multi-engine compatibility). Akave solves financial lock-in (zero egress) and verification gaps (cryptographic integrity proofs). Architecture: Snowflake → Akave external stage (S3-compatible) → Iceberg tables. Snowflake queries via external stage exactly as it would with S3. Difference: (1) Zero egress when you migrate to Spark/Trino or run multi-engine workloads. (2) Blockchain receipts for every Iceberg commit, verifiable by all engines. Snowflake gives you the analytics platform and DML support. Akave gives you the economics and cryptographic verification to make vendor independence financially viable, not just architecturally possible.