AI-Powered Database Migration Planner

Database Migrations Are Risky

Schema changes on production databases can cause downtime, data loss, and cascading failures. AI migration planning generates safe, tested migration scripts with automatic rollback plans.

How It Works

import vincony

client = vincony.Client(api_key="YOUR_API_KEY")

migration = client.tools.plan_migration(
    current_schema="schema_v1.sql",
    target_schema="schema_v2.sql",
    database="postgresql",
    data_volume="10M_rows",
    constraints=[
        "zero_downtime",
        "backward_compatible",
        "reversible"
    ]
)

print(f"Steps: {len(migration.steps)}")
for step in migration.steps:
    print(f"  {step.order}. {step.description}")
    print(f"     Risk: {step.risk_level}")

migration.save_scripts("./migrations/")
migration.save_rollback("./migrations/rollback/")

Planning Before You Touch Production

The most dangerous migrations are the ones nobody planned. A solid plan starts with a schema diff — a precise list of what tables, columns, indexes, and constraints changed between the current and target state. The AI reads both schemas, computes that diff, and then orders the changes by dependency: you cannot add a foreign key before the referenced table exists, and you cannot drop a column that a view or trigger still references. Getting this ordering wrong is the classic cause of half-applied migrations that leave a database in an unrecoverable state.

Because Vincony routes each step to whichever of its 800+ models handles it best, the same request that diffs your schema can also flag risky operations — a NOT NULL added to a populated column, a type change that silently truncates data, or an index build that will lock a large table. Each step comes back tagged with a risk level so you can review the dangerous ones before anything runs. You can explore the full request surface in the Developer API docs.

Generating Scripts and Rollbacks Together

Every forward migration needs a matching way back. A migration script that has no rollback is a one-way door, and one-way doors are how outages become incidents. The planner generates the up and down scripts as a pair, so a failed step can be reversed cleanly instead of leaving orphaned columns or dangling constraints behind. For destructive changes it favors reversible intermediate states — renaming rather than dropping, keeping the old column until the new one is proven — so rollback stays possible right up to the final cutover.

import vincony

client = vincony.Client(api_key="YOUR_API_KEY")

# Diff the schemas, order steps by dependency, and
# generate paired up/down scripts in one request.
plan = client.tools.plan_migration(
    current_schema="schema_v1.sql",
    target_schema="schema_v2.sql",
    database="postgresql",
    strategy="expand_contract",
    generate_rollback=True,
)

for step in plan.steps:
    print(f"{step.order}. {step.description} "
          f"[risk={step.risk_level}]")
    step.write_up(f"./migrations/{step.order}_up.sql")
    step.write_down(f"./migrations/{step.order}_down.sql")

# Route the trickiest data-transform step through the
# best-fit model automatically.
print("Router picked:", plan.steps[-1].model_used)

The Smart Model Router chooses the right model per step behind a single API key, so a heavy data-transform step and a quick constraint-ordering step do not have to use the same model — or cost the same.

Large Tables and Data Transforms

Small tables migrate in a single statement; large tables do not. Rewriting tens of millions of rows in one transaction bloats the write-ahead log, holds locks far too long, and can exhaust disk. The planner breaks bulk transforms into batches keyed by primary range, so the migration processes a bounded number of rows at a time and lets the database checkpoint between batches. When a column's meaning changes — splitting a full name into first and last, or normalizing an enum — it emits the transform as an idempotent backfill that can be re-run safely if it is interrupted partway through.

Zero-Downtime Strategies

The AI recommends expand-and-contract patterns, shadow columns, and dual-write strategies for zero-downtime migrations on large tables. In the expand phase you add the new structure without removing the old, deploy application code that writes to both, backfill historical rows in batches, and only then flip reads to the new column. The contract phase — dropping the now-unused old column — happens in a later, separate migration once you are confident nothing reads it. Keeping expand and contract in different deploys is what makes the whole sequence reversible at every stage.

Data Validation

After migration, automatically generate validation queries to verify data integrity — row counts, foreign key consistency, and value distributions. It is not enough for the migration to run without errors; the data has to actually match. You can also route the same dataset through a multi-model validation pipeline or a multi-model code review of the migration scripts themselves before they ship, and pair it with the SQL optimizer to make sure the new schema still performs.

# Generate validation queries
validation = migration.generate_validation(
    checks=["row_counts", "fk_integrity",
            "value_distributions", "null_checks"],
    sample_size=1000
)

for check in validation.queries:
    print(f"{check.name}: {check.sql[:100]}...")

FAQ

Which databases are supported? The planner works with PostgreSQL, MySQL, SQLite, and SQL Server dialects. You pass the current and target schema plus the target engine, and the generated scripts use that dialect's syntax for constraints, index builds, and batched updates.

Can I trust the rollback scripts without testing them? No — treat generated rollbacks as a strong starting point, not a guarantee. Always run the full up-then-down sequence against a staging copy of production data before trusting it live. The expand-and-contract structure is designed precisely so that reversal stays possible while you verify.

Do I need a separate account for each model? No. Vincony is a unified aggregator — 800+ models behind one subscription and one API key, so the schema diff, script generation, and data-transform steps can each use the best-fit model without you managing separate providers. You can sign up here to try it.

Pricing

3 migration plans/month on Free. Unlimited with rollback scripts on Pro and Enterprise.