Two-way sync
Changes in Pinecone or Supabase instantly reflect in both systems. No stale data, no manual imports.
Keep Pinecone and Supabase in sync without custom scripts. Cut weeks of integration work, eliminate silent data drift, and give your team a single, reliable source of truth.
AI systems do not hold customers or invoices the way business apps do. What they hold is derived from your data: the vectors and metadata in a vector store, or the classifications, extracted fields, and generated text a model produces over records it was given. Supabase is where those source records actually live. The bridge between the two is the row itself, since an item in Pinecone and the record in Supabase it describes are two halves of the same thing, and they drift the moment one is updated without the other.
Stacksync syncs Database Functions, Storage Object Metadata, Tables, Views in Supabase with Collections, Backups, Index statistics, Indexes in Pinecone in real time. Rows created or changed in Supabase flow into Pinecone so inference and embedding run on current data, and the scores, labels, and generated fields Pinecone produces flow back onto the matching rows in Supabase, mapped field by field. A change on either side appears on the other within seconds, with no extraction job or webhook plumbing to keep alive.
Because matching is by a stable identifier, every row in Supabase stays tied to its AI-side counterpart in Pinecone. Retrieval, enrichment, and generated content always resolve back to the record they came from, so there are no orphaned vectors and no labels describing a version of a row that no longer exists.
When a row in Supabase is updated or removed, its counterpart in Pinecone is updated or removed too, so nothing in Pinecone describes a record that has since changed or gone.
Load your existing rows from Supabase into Pinecone to build the index or enrichment set, then let ongoing changes sync automatically instead of re-running the whole job.
Each item in Pinecone carries the key of the row in Supabase it came from, so results resolve back to the exact record with nothing orphaned or duplicated.
Representative objects on each side — any object or custom field can map to any target. Schemas are auto-detected; types are converted between the two systems.
| Pinecone objects | Supabase objects | How this pairing syncs | |
|---|---|---|---|
| Index statistics Describe_index_stats returns total and per-namespace vector counts, the index dimension, and index fullness; read to size a sync and to detect drift between Pinecone and the source of truth. | Schemas Namespaces (public and custom) that scope sync access. | Index statistics is specific to Pinecone and Schemas to Supabase — each maps to any object or custom field on the other side. | |
| Indexes Serverless or pod-based containers holding vectors of a fixed dimension and distance metric (cosine, dotproduct, euclidean); managed on the control plane (api.pinecone.io) via create, list, describe, configure, and delete. describe_index returns the per-index data-plane host. | auth.users Managed authentication users, often mirrored into CRM or support systems. | Indexes is specific to Pinecone and auth.users to Supabase — each maps to any object or custom field on the other side. | |
| Vectors (records) The core data: an id (up to 512 chars), a dense values array, optional sparse_values, and JSON metadata (up to 40 KB filterable per record). Full CRUD on the data plane via upsert, update, fetch, query, and delete, so write is supported here. | Row Level Security Policies Row-level access rules that govern what the REST layer exposes. | Vectors (records) is specific to Pinecone and Row Level Security Policies to Supabase — each maps to any object or custom field on the other side. | |
| Namespaces Partitions inside an index; every read and write targets one namespace and vectors across namespaces are isolated. Enumerated with list_namespaces and sized per namespace via describe_index_stats. | JSONB Columns Semi-structured payloads such as event properties or nested objects. | Namespaces is specific to Pinecone and JSONB Columns to Supabase — each maps to any object or custom field on the other side. | |
| Collections Immutable snapshots of a pod-based index that store its data but not its definition; created, listed, and deleted on the control plane and used to recreate a pod-based index. Serverless indexes use Backups instead. | Database Functions Postgres functions that can transform or validate synced rows. | Collections is specific to Pinecone and Database Functions to Supabase — each maps to any object or custom field on the other side. | |
| Backups Point-in-time snapshots of a serverless index; created, listed, and restored into a new index on the control plane for recovery or cloning. Read as a recovery-asset inventory. | Storage Object Metadata File metadata rows that can be joined to synced application data. | Backups is specific to Pinecone and Storage Object Metadata to Supabase — each maps to any object or custom field on the other side. |
Each direction of the sync is driven by what the source system can signal and what the destination accepts — detection, delivery, and expected latency below.
DetectionStacksync polls Pinecone for changes on an incremental schedule, reading only records changed since the previous pass. No webhooks and no native change-data-capture feed.
DeliveryEach detected change is applied to Supabase as a row-level write, with types converted between the two schemas.
DetectionSupabase pushes changes as they happen — webhook events backed by change data capture. Log-based CDC via Postgres logical replication, the same WAL feed that powers Supabase Realtime.
DeliveryEach detected change is written to Pinecone through its API, with automatic retries and rate-limit backoff.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Pinecone–Supabase connection.
Changes in Pinecone or Supabase instantly reflect in both systems. No stale data, no manual imports.
Trigger automated workflows whenever Pinecone or Supabase data changes, update records, fire webhooks, or kick off sequences without brittle API scripts.
Handle millions of events per minute without losing a single Pinecone or Supabase record.
Track your Pinecone ⇄ Supabase sync health, view errors, and replay failed events in one click.
Transform legacy EDI complexity into simple database interactions between Pinecone and Supabase.
Configure and sync within minutes, no code. Whether you sync 50k or 100M+ records, Stacksync handles the queues, infra, and plumbing. Integrations are non-invasive and need zero setup on your systems.
Authenticate Pinecone and Supabase with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.
Pick the Pinecone and Supabase objects to sync — Stacksync auto-detects both schemas, including custom fields where the platform exposes them. Sync to existing tables, or let Stacksync create new ones with ideal data types.
Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.
Yes. Stacksync provides a managed, real-time two-way integration between Pinecone and Supabase: authenticate both systems, choose the objects to sync (such as Pinecone's Index statistics and Indexes), map fields visually, and changes propagate both ways in milliseconds — no code required.
Stacksync pricing is usage-based and starts at $1,000/month, including the managed Pinecone and Supabase connectors, real-time two-way sync, monitoring, and support. That replaces building and maintaining a custom Pinecone–Supabase integration in-house.
Yes — Stacksync ships production-grade connectors for both Pinecone and Supabase. The connectors handle authentication, schema detection, rate limits, and retries; you configure the sync, and Stacksync operates it.
Change detection on Pinecone: No webhooks and no native change-data-capture feed. Vectors carry no server-side update timestamp, so Stacksync detects changes by re-reading - paginating vector ids with the list operation (serverless indexes) and fetching by id, or by re-upserting from the source of truth. describe_index_stats bounds a resync with per-namespace counts. On Supabase: Log-based CDC via Postgres logical replication, the same WAL feed that powers Supabase Realtime; database webhooks can also fire on row changes. Each detected change propagates to the other side in milliseconds, with field-level conflict resolution and an inspectable event log.
On the Pinecone side: Collections, Backups, Index statistics, Indexes, plus custom fields where Pinecone exposes them. On the Supabase side: Database Functions, Storage Object Metadata, Tables, Views. Stacksync auto-detects both schemas and converts types between the two systems.
Yes. Each object mapping can be bidirectional or restricted to a single direction (both systems accept writes). Read-only mirrors, one-way pushes, and full two-way sync can be mixed in the same integration.
As a data company, we understand the importance of keeping your data secure. Stacksync is built with security best practices to keep your data safe at every layer, and is DPF-certified for US, EU, UK and CH data transfers.
Let your users access Stacksync from your centralized user management systems. Works with Okta, Azure, Google SSO and more.
Immediately get alerted about record syncing issues over email, Slack, PagerDuty and WhatsApp. Resolve issues from a centralized dashboard with retry and revert options.
Securely connects to your systems with:
Every pair below is a real-time, two-way sync. Search all 513 integrations available for Pinecone and Supabase.