{ "brand": "HyperLake", "canonical": "https://hyperlake.cloud/discover", "question": null, "matches": [ { "id": "batch-realtime-lakehouse", "title": "Batch and real-time analytics without competing for one engine", "summary": "Velocity Lakehouse combines open tables with a ClickHouse hot tier and separate transformation capacity. Plan how batch, interactive and existing warehouse work will be divided.", "features": [ "Iceberg, Trino and ClickHouse in one stack", "Separate transformation and interactive engines", "LakeGate policy and query routing" ], "criteria": [ "Which data belongs in the hot tier versus open tables?", "What freshness and concurrency does each consumer need?", "Which warehouse workloads should remain where they are?" ], "inputs": [ "Batch and interactive query examples", "Freshness targets and data volumes", "Existing warehouse dependencies" ], "steps": [ "Classify workloads by freshness, latency and retention.", "Pilot one dataset through batch and hot-tier paths.", "Validate routing, reconciliation and cost before consolidating systems." ], "verification": [ "Reconcile hot-tier and table results.", "Check interactive latency while batch work runs.", "Document replay and recovery for each tier." ], "sources": [ "https://hyperlake.cloud/stacks", "https://hyperlake.cloud/faq", "https://hyperlake.cloud/get-started" ], "economics": "Compare the cloud bill, platform charges, operating effort and migration work for the same workload. Cloud capacity is not free. Charges and scope need agreement before paid deployment.", "switching": "AWS and Google Cloud are available today; other deployment environments are roadmap. Preserve the existing workload and access path until compatibility, recovery and cutover checks pass.", "anchors": [ "batch realtime lakehouse" ], "path": "/solutions/batch-realtime-lakehouse", "landingPath": "/solutions/batch-realtime-lakehouse", "cta": { "label": "Discuss this workload", "path": "/contact" }, "url": "https://hyperlake.cloud/solutions/batch-realtime-lakehouse" }, { "id": "classic-ml-serving", "title": "Serve existing ML models beside your data: plan the endpoint", "summary": "Predict serves classic ML models and records predictions with your data systems. Evaluate compatibility, feature freshness and operational behavior before moving an existing endpoint.", "features": [ "Serving for supported classic ML model formats", "dbt feature models and prediction logs in ClickHouse", "Data-quality monitors" ], "criteria": [ "Is the trained model and its runtime supported?", "How will online features match the training definitions?", "What latency, availability and rollback requirements apply?" ], "inputs": [ "Model artifact and runtime requirements", "Feature definitions and reference predictions", "Traffic and monitoring requirements" ], "steps": [ "Confirm compatibility for one existing model.", "Compare reference predictions and feature inputs in a pilot.", "Exercise rollout, monitoring and rollback before cutover." ], "verification": [ "Check prediction equivalence on reference cases.", "Measure feature freshness and serving latency.", "Validate logging, drift review and rollback ownership." ], "sources": [ "https://hyperlake.cloud/stacks", "https://hyperlake.cloud/faq", "https://hyperlake.cloud/get-started" ], "economics": "Compare the cloud bill, platform charges, operating effort and migration work for the same workload. Cloud capacity is not free. Charges and scope need agreement before paid deployment.", "switching": "AWS and Google Cloud are available today; other deployment environments are roadmap. Preserve the existing workload and access path until compatibility, recovery and cutover checks pass.", "anchors": [ "classic ml serving", "managed ml serving" ], "path": "/solutions/classic-ml-serving", "landingPath": "/solutions/classic-ml-serving", "cta": { "label": "Discuss this workload", "path": "/contact" }, "url": "https://hyperlake.cloud/solutions/classic-ml-serving" }, { "id": "federated-sql", "title": "Federated SQL across existing systems: plan a governed query", "summary": "Unify uses managed Trino for queries across existing sources. Test one cross-system decision before deciding which copies or sync jobs you can retire.", "features": [ "Trino federation over connected catalogs", "Row filters, column masks and a semantic layer", "Governed access through LakeGate" ], "criteria": [ "Can each required source support the connector and query pattern?", "What latency and source load are acceptable?", "Which movements are still needed for performance, freshness or isolation?" ], "inputs": [ "Source systems and schemas", "One representative join and expected answer", "Freshness, permissions and latency requirements" ], "steps": [ "Document the existing data movement and the decision it supports.", "Connect a limited source set and test the governed query.", "Compare correctness, source load and operating effort before replacing sync jobs." ], "verification": [ "Reconcile row counts and join semantics.", "Check predicate pushdown and source impact.", "Test policy enforcement across the combined result." ], "sources": [ "https://hyperlake.cloud/stacks", "https://hyperlake.cloud/faq", "https://hyperlake.cloud/get-started" ], "economics": "Compare the cloud bill, platform charges, operating effort and migration work for the same workload. Cloud capacity is not free. Charges and scope need agreement before paid deployment.", "switching": "AWS and Google Cloud are available today; other deployment environments are roadmap. Preserve the existing workload and access path until compatibility, recovery and cutover checks pass.", "anchors": [ "federated sql", "managed trino" ], "path": "/solutions/federated-sql", "landingPath": "/solutions/federated-sql", "cta": { "label": "Discuss this workload", "path": "/contact" }, "url": "https://hyperlake.cloud/solutions/federated-sql" }, { "id": "governed-agent-sandboxes", "title": "Agent sandboxes with governed data access: plan the boundaries", "summary": "AgentBay provides isolated code and tool sandboxes with data access through LakeGate. Define what an agent may read, execute and change before giving it a real workflow.", "features": [ "Isolated sandboxes for agent code and tools", "Scoped data access through LakeGate", "MCP access alongside existing agent toolkits" ], "criteria": [ "Which actions need approval or must be prohibited?", "How are network, filesystem and data permissions separated?", "How will your team investigate an incorrect action?" ], "inputs": [ "One agent workflow and its tools", "Allowed data and action matrix", "Identity, logging and approval requirements" ], "steps": [ "Choose a reversible task with explicit action limits.", "Connect only the required tools and scoped data access.", "Test failures and prohibited actions before expanding the agent’s remit." ], "verification": [ "Attempt disallowed queries and actions.", "Check sandbox isolation and cleanup.", "Trace outputs to the originating workflow and permission set." ], "sources": [ "https://hyperlake.cloud/stacks", "https://hyperlake.cloud/faq", "https://hyperlake.cloud/get-started" ], "economics": "Compare the cloud bill, platform charges, operating effort and migration work for the same workload. Cloud capacity is not free. Charges and scope need agreement before paid deployment.", "switching": "AWS and Google Cloud are available today; other deployment environments are roadmap. Preserve the existing workload and access path until compatibility, recovery and cutover checks pass.", "anchors": [ "governed agent sandboxes", "ai agent sandbox" ], "path": "/solutions/governed-agent-sandboxes", "landingPath": "/solutions/governed-agent-sandboxes", "cta": { "label": "Discuss this workload", "path": "/contact" }, "url": "https://hyperlake.cloud/solutions/governed-agent-sandboxes" }, { "id": "governed-ai-analyst", "title": "An AI analyst on governed company data: start with one decision", "summary": "Analyst combines the lakehouse, private models and agent sandboxes. Scope one business question, its permitted evidence and the checks a human reviewer will apply.", "features": [ "Velocity Lakehouse, Enclave and AgentBay together", "Semantic-layer and policy-governed data", "Analysis code in sandboxes and data-quality monitors" ], "criteria": [ "Are the business definitions and data owners agreed?", "Can each intended role access the required evidence?", "Which answers require human review before use?" ], "inputs": [ "A business question and trusted reference answer", "Approved datasets and metric definitions", "Roles, permissions and review criteria" ], "steps": [ "Choose one decision and define what a useful answer must contain.", "Configure data permissions and semantic definitions.", "Compare outputs with trusted analysis before expanding the audience." ], "verification": [ "Check calculations and evidence links.", "Test role-based differences in visibility.", "Review missing data and uncertain conclusions explicitly." ], "sources": [ "https://hyperlake.cloud/stacks", "https://hyperlake.cloud/faq", "https://hyperlake.cloud/get-started" ], "economics": "Compare the cloud bill, platform charges, operating effort and migration work for the same workload. Cloud capacity is not free. Charges and scope need agreement before paid deployment.", "switching": "AWS and Google Cloud are available today; other deployment environments are roadmap. Preserve the existing workload and access path until compatibility, recovery and cutover checks pass.", "anchors": [ "governed ai analyst", "governed natural language analytics" ], "path": "/solutions/governed-ai-analyst", "landingPath": "/solutions/governed-ai-analyst", "cta": { "label": "Discuss this workload", "path": "/contact" }, "url": "https://hyperlake.cloud/solutions/governed-ai-analyst" }, { "id": "governed-database-access", "title": "Governed database access for people and agents", "summary": "LakeGate places identity and policy in front of databases and data engines. Start by replacing one shared-credential workflow with scoped, reviewable access.", "features": [ "Identity-provider sign-in and role-based access", "OPA policy decisions and query guardrails", "Query logging across integrated engines" ], "criteria": [ "Which users and workload identities require which tables and columns?", "Does the existing client protocol and engine combination work in your deployment?", "How are query limits, audit retention and emergency access controlled?" ], "inputs": [ "Database engines and client tools", "Identity groups and access matrix", "Representative allowed and prohibited queries" ], "steps": [ "Map one access path and remove ambiguity about the permission owner.", "Configure a pilot role and explicit allow/deny policies.", "Test legitimate queries, blocked queries and access revocation before expansion." ], "verification": [ "Verify denials as carefully as successful queries.", "Reconcile query logs with the originating identity.", "Keep an emergency access and rollback procedure." ], "sources": [ "https://hyperlake.cloud/stacks", "https://hyperlake.cloud/faq", "https://hyperlake.cloud/get-started" ], "economics": "Compare the cloud bill, platform charges, operating effort and migration work for the same workload. Cloud capacity is not free. Charges and scope need agreement before paid deployment.", "switching": "AWS and Google Cloud are available today; other deployment environments are roadmap. Preserve the existing workload and access path until compatibility, recovery and cutover checks pass.", "anchors": [ "governed database access", "database access for ai agents" ], "path": "/solutions/governed-database-access", "landingPath": "/solutions/governed-database-access", "cta": { "label": "Discuss this workload", "path": "/contact" }, "url": "https://hyperlake.cloud/solutions/governed-database-access" }, { "id": "inference-cost", "title": "LLM API vs self-hosted cost calculator", "path": "/solutions/inference-cost", "summary": "Compare API spend with self-hosted inference using your own prices, request volume and GPU assumptions.", "features": [ "Workload-based monthly cost comparison", "Break-even volume and GPU utilization", "Calculator runs in your browser" ], "economics": "Use your actual API rates, cloud prices and expected utilization. Include software and operational costs when making the buying decision.", "switching": "Verify throughput, model quality, latency and operational requirements before replacing an API service.", "sources": [ "https://hyperlake.cloud/tools/llm-api-vs-self-hosted-cost-calculator", "https://hyperlake.cloud/" ], "criteria": [ "Compare model quality and required latency before treating API and self-hosted requests as interchangeable.", "GPU utilization, batch size and idle capacity can change self-hosted economics materially.", "Include platform, storage, networking, operations and migration costs alongside inference compute." ], "inputs": [ "Actual API rates and request/token volume", "Candidate model, GPU price and measured throughput", "Expected utilization, latency target and operating effort" ], "steps": [ "Enter your real workload and prices in the calculator.", "Test representative requests on the candidate model.", "Compare low, expected and high utilization scenarios.", "Pilot at production-like load and review the measured break-even assumptions." ], "verification": [ "Confirm output quality on representative tasks.", "Measure throughput and tail latency at target concurrency.", "Compare actual billed cost with the model after the pilot." ], "landingPath": "/solutions/inference-cost", "cta": { "label": "Discuss this workflow", "path": "/contact" }, "url": "https://hyperlake.cloud/solutions/inference-cost" }, { "id": "managed-kubernetes", "title": "Managed Kubernetes in your own cloud: plan the operating model", "summary": "Foundry packages a Kubernetes foundation in your AWS or Google Cloud account. Decide who owns access, upgrades, recovery and day-two operations before adding production workloads.", "features": [ "Managed Kubernetes foundation", "Observability, single sign-on, private access, backups and autoscaling", "Foundation for additional HyperLake stacks" ], "criteria": [ "Which responsibilities stay with your platform team, and which belong in the managed scope?", "How will existing CI/CD, Helm charts and identity integrate?", "What recovery and upgrade procedures must pass before production?" ], "inputs": [ "Cloud account and region", "One application and its resource requirements", "Identity provider, deployment pipeline and recovery objectives" ], "steps": [ "Inventory the current cluster and operating responsibilities.", "Define one non-production workload and its access, backup and upgrade checks.", "Agree ownership and charges before deploying and validating the pilot." ], "verification": [ "Exercise restore and identity revocation.", "Check workload behavior during a planned upgrade.", "Measure infrastructure and operating effort separately." ], "sources": [ "https://hyperlake.cloud/stacks", "https://hyperlake.cloud/faq", "https://hyperlake.cloud/get-started" ], "economics": "Compare the cloud bill, platform charges, operating effort and migration work for the same workload. Cloud capacity is not free. Charges and scope need agreement before paid deployment.", "switching": "AWS and Google Cloud are available today; other deployment environments are roadmap. Preserve the existing workload and access path until compatibility, recovery and cutover checks pass.", "anchors": [ "managed kubernetes", "helm deployment" ], "path": "/solutions/managed-kubernetes", "landingPath": "/solutions/managed-kubernetes", "cta": { "label": "Discuss this workload", "path": "/contact" }, "url": "https://hyperlake.cloud/solutions/managed-kubernetes" }, { "id": "managed-postgres-mysql", "title": "Managed Postgres and MySQL: protect the app, separate reporting", "summary": "Fusion combines application databases with reporting on ClickHouse. Start with availability and recovery needs, then assess which analytical work should leave the primary.", "features": [ "Managed Postgres and MySQL", "High availability and point-in-time recovery", "ClickHouse reporting from Postgres, behind LakeGate" ], "criteria": [ "What recovery point and recovery time can the application tolerate?", "Which reporting queries affect production performance?", "Which extensions, versions and client behaviors are required?" ], "inputs": [ "Database version, size and extension inventory", "Application and reporting query samples", "Recovery objectives and maintenance windows" ], "steps": [ "Check compatibility and define a reversible migration.", "Pilot the application path and Postgres reporting feed.", "Exercise recovery and switchover before moving production traffic." ], "verification": [ "Check transaction and reporting consistency.", "Measure primary database load.", "Complete restore and failover exercises." ], "sources": [ "https://hyperlake.cloud/stacks", "https://hyperlake.cloud/faq", "https://hyperlake.cloud/get-started" ], "economics": "Compare the cloud bill, platform charges, operating effort and migration work for the same workload. Cloud capacity is not free. Charges and scope need agreement before paid deployment.", "switching": "AWS and Google Cloud are available today; other deployment environments are roadmap. Preserve the existing workload and access path until compatibility, recovery and cutover checks pass.", "anchors": [ "managed postgres mysql" ], "path": "/solutions/managed-postgres-mysql", "landingPath": "/solutions/managed-postgres-mysql", "cta": { "label": "Discuss this workload", "path": "/contact" }, "url": "https://hyperlake.cloud/solutions/managed-postgres-mysql" }, { "id": "open-iceberg-lakehouse", "title": "An open Iceberg lakehouse: plan tables, access and migration", "summary": "Openhouse combines Iceberg, Trino and dbt on your own object storage. Make table ownership, interoperability and workload fit explicit before migrating an existing lake.", "features": [ "Iceberg tables on customer object storage", "Trino, dbt modeling and a semantic layer", "Dashboards and data monitors behind LakeGate" ], "criteria": [ "Which engines must read the same tables?", "Who owns schemas, compaction and data quality?", "How will existing warehouse jobs coexist during transition?" ], "inputs": [ "Table inventory and storage layout", "Transformation jobs and consumer engines", "Access and data-quality requirements" ], "steps": [ "Select one dataset with known consumers and acceptance checks.", "Build the table and transformation path alongside the current system.", "Check interoperability and operations before retiring the old path." ], "verification": [ "Validate schema and data reconciliation.", "Test reads from each required engine.", "Review lifecycle, recovery and storage charges." ], "sources": [ "https://hyperlake.cloud/stacks", "https://hyperlake.cloud/faq", "https://hyperlake.cloud/get-started" ], "economics": "Compare the cloud bill, platform charges, operating effort and migration work for the same workload. Cloud capacity is not free. Charges and scope need agreement before paid deployment.", "switching": "AWS and Google Cloud are available today; other deployment environments are roadmap. Preserve the existing workload and access path until compatibility, recovery and cutover checks pass.", "anchors": [ "open iceberg lakehouse", "open lakehouse" ], "path": "/solutions/open-iceberg-lakehouse", "landingPath": "/solutions/open-iceberg-lakehouse", "cta": { "label": "Discuss this workload", "path": "/contact" }, "url": "https://hyperlake.cloud/solutions/open-iceberg-lakehouse" }, { "id": "physical-ai", "title": "Physical AI infrastructure and open-source stack map", "path": "/solutions/physical-ai", "summary": "Map the software layers needed for physical AI and robotics infrastructure.", "features": [ "Projects grouped by infrastructure layer", "Official project references", "Connections to data and AI infrastructure" ], "economics": "Evaluate infrastructure capacity and the work required to operate the selected components. Open-source licensing alone does not determine total cost.", "switching": "Check hardware, data formats, integration needs and operational ownership for your specific deployment.", "sources": [ "https://hyperlake.cloud/physical-ai/open-source-stack-map", "https://hyperlake.cloud/physical-ai" ], "criteria": [ "Map ingestion, storage, training, simulation, serving and observability as separate infrastructure layers.", "Hardware compatibility, safety requirements and deployment environment constrain the software choice.", "Treat the stack map as a planning aid; confirm project maturity, licenses and operational support directly." ], "inputs": [ "Sensor/data sources and intended deployment environment", "Hardware, models and integration requirements", "Safety, latency, data retention and operational ownership constraints" ], "steps": [ "Map the target workflow to the infrastructure layers.", "Review official project references and identify missing integrations.", "Test a bounded non-production pipeline on representative data.", "Agree deployment scope and specialist responsibilities before production use." ], "verification": [ "Validate data formats and hardware compatibility.", "Measure pipeline latency and recoverability.", "Confirm domain-specific safety review and operating ownership." ], "landingPath": "/solutions/physical-ai", "cta": { "label": "Discuss this workflow", "path": "/contact" }, "url": "https://hyperlake.cloud/solutions/physical-ai" }, { "id": "private-ai", "title": "Private AI infrastructure: features, cost and control", "path": "/solutions/private-ai", "summary": "Compare deployment boundaries, model control and who operates the stack. HyperLake manages open-source stacks in your own cloud account.", "features": [ "Customer-controlled cloud account and region", "Managed data engines, model serving and agent workspaces", "Expert support included in the subscription" ], "economics": "Your cloud compute is billed at 0% HyperLake markup, plus the HyperLake subscription. Compare equivalent capacity, utilization and operating effort; total savings depend on the workload.", "switching": "Check supported cloud accounts, data boundaries, model requirements and migration scope with the team. On-premises availability must be confirmed separately.", "sources": [ "https://hyperlake.cloud/", "https://hyperlake.cloud/blog/private-ai-infrastructure-for-enterprises-in-2026-options-compared" ], "criteria": [ "Define where data, model endpoints, logs and backups must reside; private AI is a deployment boundary, not a single product label.", "Assign operating responsibility for models, data engines, updates and incident response.", "Compare capacity, utilization, support and platform charges at the same workload and service level." ], "inputs": [ "Cloud account, region and data-boundary requirements", "Representative workloads, models, throughput and latency targets", "Security, integration and operating-ownership requirements" ], "steps": [ "Document the deployment boundary and workload requirements.", "Review the supported stack and separate confirmed requirements from open questions, including on-premises support.", "Pilot a representative workload with agreed quality and performance checks.", "Agree operational ownership, support and total-cost assumptions before moving production work." ], "verification": [ "Verify which account and region hold data and compute.", "Measure workload quality, latency and utilization.", "Reconcile direct cloud charges, platform subscription and operating effort." ], "landingPath": "/solutions/private-ai", "cta": { "label": "Discuss this workflow", "path": "/contact" }, "url": "https://hyperlake.cloud/solutions/private-ai" }, { "id": "private-model-serving", "title": "Private model serving and fine-tuning: plan quality and capacity", "summary": "Enclave runs open or custom models on GPUs in your cloud. Compare model quality, workload utilization and operational responsibility before replacing a hosted endpoint.", "features": [ "Model serving and fine-tuning on customer GPUs", "pgvector retrieval and governed data access", "Eligible endpoints can scale down when idle" ], "criteria": [ "Does the chosen model meet the actual quality and safety requirements?", "What capacity and cold-start trade-offs suit the traffic?", "Which data, models and licenses can run in the selected region?" ], "inputs": [ "Model and representative evaluation set", "Traffic, context length and latency profile", "Region, licenses and available GPU capacity" ], "steps": [ "Define the quality and latency acceptance bar.", "Pilot one endpoint with representative traffic and retrieval needs.", "Compare utilization and total cost, then agree the production operating model." ], "verification": [ "Evaluate answer quality on the agreed cases.", "Measure startup, latency and capacity under load.", "Include idle compute, storage and support in cost comparisons." ], "sources": [ "https://hyperlake.cloud/stacks", "https://hyperlake.cloud/faq", "https://hyperlake.cloud/get-started" ], "economics": "Compare the cloud bill, platform charges, operating effort and migration work for the same workload. Cloud capacity is not free. Charges and scope need agreement before paid deployment.", "switching": "AWS and Google Cloud are available today; other deployment environments are roadmap. Preserve the existing workload and access path until compatibility, recovery and cutover checks pass.", "anchors": [ "private model serving", "private model endpoint" ], "path": "/solutions/private-model-serving", "landingPath": "/solutions/private-model-serving", "cta": { "label": "Discuss this workload", "path": "/contact" }, "url": "https://hyperlake.cloud/solutions/private-model-serving" }, { "id": "realtime-analytics", "title": "Real-time analytics in your cloud: scope a ClickHouse workload", "summary": "Velocity provides managed ClickHouse for dashboards, product analytics and data APIs. Use your own ingestion and query patterns to evaluate a move from hosted analytics.", "features": [ "Managed ClickHouse behind LakeGate", "Backups, sharding and day-two actions", "Role-based access for analytics workloads" ], "criteria": [ "What ingestion freshness and retention does the product need?", "What query concurrency and latency matter at peak load?", "Can the existing warehouse remain the system of record?" ], "inputs": [ "Representative events and query set", "Peak concurrency and freshness targets", "Current retention and operating costs" ], "steps": [ "Choose one dashboard or data API as the pilot.", "Replay a representative workload and check data access controls.", "Compare measured behavior and full operating cost before migrating more traffic." ], "verification": [ "Reconcile ingestion completeness.", "Measure latency at representative concurrency.", "Exercise backup recovery and the cutover rollback." ], "sources": [ "https://hyperlake.cloud/stacks", "https://hyperlake.cloud/faq", "https://hyperlake.cloud/get-started" ], "economics": "Compare the cloud bill, platform charges, operating effort and migration work for the same workload. Cloud capacity is not free. Charges and scope need agreement before paid deployment.", "switching": "AWS and Google Cloud are available today; other deployment environments are roadmap. Preserve the existing workload and access path until compatibility, recovery and cutover checks pass.", "anchors": [ "realtime analytics", "managed clickhouse" ], "path": "/solutions/realtime-analytics", "landingPath": "/solutions/realtime-analytics", "cta": { "label": "Discuss this workload", "path": "/contact" }, "url": "https://hyperlake.cloud/solutions/realtime-analytics" }, { "id": "warehouse-cost", "anchors": [ "snowflake", "cloudera" ], "title": "Compare warehouse platforms and workload economics", "path": "/solutions/warehouse-cost", "summary": "Compare the platform to the workload, including cases where another architecture fits better.", "features": [ "Workload and architecture comparison", "Managed open-source data stacks", "Option to run alongside an existing platform" ], "economics": "Compare compute, storage, licensing, support and migration effort at the same workload and service level. 0% compute markup does not mean zero platform or operating cost.", "switching": "Identify which workloads can move and which capabilities should stay on the current platform. Confirm compatibility before migration.", "sources": [ "https://hyperlake.cloud/blog/cloudera-vs-snowflake-in-2026-and-when-neither-fits", "https://hyperlake.cloud/" ], "criteria": [ "Separate storage, SQL analytics, streaming, operational queries and machine-learning workloads.", "Check compatibility, data movement and governance before choosing replacement engines.", "Compare migration effort and coexistence options alongside recurring compute and software charges." ], "inputs": [ "Workload inventory and representative queries", "Current compute, storage, licensing and support spend", "Data movement, governance and service-level requirements" ], "steps": [ "Identify the workloads that create cost or operational pain.", "Choose representative queries and data for a bounded comparison.", "Validate results, performance and migration compatibility.", "Move suitable workloads in stages and retain capabilities that still fit the existing platform." ], "verification": [ "Reconcile query outputs and data completeness.", "Measure workload-level cost and concurrency.", "Test failure recovery and access controls before cutover." ], "landingPath": "/solutions/warehouse-cost", "cta": { "label": "Discuss this workflow", "path": "/contact" }, "url": "https://hyperlake.cloud/solutions/warehouse-cost" } ], "nextStep": { "label": "Get free expert help with your use case", "url": "https://hyperlake.cloud/contact" }, "limitations": "Pricing depends on the agreed scope, usage and support requirements." }