Skip to content
AI ⇄ Database

Pinecone to Redis Enterprise integration — real-time, two-way sync

Keep Pinecone and Redis Enterprise in sync without custom scripts. Cut weeks of integration work, eliminate silent data drift, and give your team a single, reliable source of truth.

  • SOC 2 and 6 other compliance frameworks
  • POC with real engineers in minutes

Adopted by fast-scaling companies moving mission-critical data in real time

Case study
Migrated from MuleSoft
Case study
Migrated from Celigo
Migrated from Heroku Connect
Migrated from Matillion
Case study
Migrated from Fivetran
Case study
Migrated from Celigo
Why teams connect Pinecone and Redis Enterprise

Sync the records in Redis Enterprise into Pinecone and land its embeddings, classifications, and generated fields back on the same rows, in real time and without a pipeline to maintain.

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. Redis Enterprise 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 Redis Enterprise it describes are two halves of the same thing, and they drift the moment one is updated without the other.

Stacksync syncs Sets, Sorted Sets, Lists, Streams in Redis Enterprise with Indexes, Vectors (records), Namespaces, Collections in Pinecone in real time. Rows created or changed in Redis Enterprise 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 Redis Enterprise, 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 Redis Enterprise 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.

Common use cases

  • 01 Write embeddings and their metadata into a Pinecone index from a Postgres or warehouse table so a semantic-search or RAG feature always queries fresh vectors.
  • 02 Keep a Pinecone index aligned with a source of truth (a product catalog, knowledge base, or CRM) so new, changed, and deleted records upsert and delete the matching vectors.
  • 03 Cache CRM or ERP records into Redis hashes so applications read customer data with sub-millisecond in-memory latency instead of hitting SaaS APIs.
  • 04 Sync Postgres reference tables into RedisJSON documents that power API responses and personalization lookups.

Common sync patterns

Keep derived data fresh as sources change

When a row in Redis Enterprise 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.

Backfill once, then stay in step

Load your existing rows from Redis Enterprise into Pinecone to build the index or enrichment set, then let ongoing changes sync automatically instead of re-running the whole job.

One record, one identifier

Each item in Pinecone carries the key of the row in Redis Enterprise it came from, so results resolve back to the exact record with nothing orphaned or duplicated.

What you can sync between Pinecone and Redis Enterprise

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 Redis Enterprise objects How this pairing syncs
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. Pub/Sub channels Fire-and-forget messaging used to notify applications when synced keys change. Namespaces is specific to Pinecone and Pub/Sub channels to Redis Enterprise — 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. Search indexes Secondary indexes (RediSearch) that make synced hashes and JSON documents queryable. Collections is specific to Pinecone and Search indexes to Redis Enterprise — 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. Keys (Strings) Simple key-value pairs used to cache individual synced records or lookup values. Backups is specific to Pinecone and Keys (Strings) to Redis Enterprise — each maps to any object or custom field on the other side.
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. Hashes Field-value maps that commonly hold one synced row per hash, keyed by record ID. Index statistics is specific to Pinecone and Hashes to Redis Enterprise — 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. JSON documents Native JSON storage (RedisJSON) for nested records synced from APIs or document stores. Indexes is specific to Pinecone and JSON documents to Redis Enterprise — 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. Sets Unordered unique-member collections used for membership checks like segment or ID lists. Vectors (records) is specific to Pinecone and Sets to Redis Enterprise — each maps to any object or custom field on the other side.

How changes propagate between Pinecone and Redis Enterprise

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.

Pinecone Redis Enterprise Interval-based propagation

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 Redis Enterprise as a row-level write, with types converted between the two schemas.

Redis Enterprise Pinecone Interval-based propagation

DetectionStacksync polls Redis Enterprise for changes on an incremental schedule, reading only records changed since the previous pass. Keyspace notifications over pub/sub or reads from Redis Streams.

DeliveryEach detected change is written to Pinecone through its API, with automatic retries and rate-limit backoff.

Rate-limit considerations

  • Pinecone: Data-plane operations are capped per second: query, upsert, update, and delete at 100 req/s per namespace, fetch at 100 req/s per index, and list at 200 req/s per index; breaches return HTTP 429. Hard request limits also apply: an upsert is max 2 MB or 1000 records, metadata max 40 KB per record, and query top_k up to 10,000 with a 4 MB result cap.
  • Redis Enterprise: Throughput is bounded by provisioned cluster capacity rather than published API rate limits.
What ships with Pinecone ⇄ Redis Enterprise

Connect Pinecone and Redis Enterprise for flexible, real-time data sync.

Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Pinecone–Redis Enterprise connection.

Real-time

Two-way sync

Changes in Pinecone or Redis Enterprise instantly reflect in both systems. No stale data, no manual imports.

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Pinecone or Redis Enterprise data changes, update records, fire webhooks, or kick off sequences without brittle API scripts.

At scale

Event queues

Handle millions of events per minute without losing a single Pinecone or Redis Enterprise record.

Observability

Monitoring

Track your Pinecone ⇄ Redis Enterprise sync health, view errors, and replay failed events in one click.

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between Pinecone and Redis Enterprise.

How the Pinecone and Redis Enterprise connectors work

Pinecone

Integration surface
Two HTTP APIs: a control plane at api.pinecone.io (manage indexes, collections, backups, and, via the Admin API, projects and API keys) and a per-index data plane at the host returned by describe_index (upsert, query, fetch, update, delete, list). A gRPC data-plane transport is available through the official SDKs.
Authentication
API key in the Api-Key request header, scoped to one project; every request also sends an X-Pinecone-Api-Version header (date-based, e.g. 2025-10). The organization Admin API instead uses OAuth2 client-credentials (service accounts) via login.pinecone.io/oauth/token, passing a Bearer token to api.pinecone.io/admin (Enterprise).
Change detection
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.
Capabilities
read · write
Rate limits
Data-plane operations are capped per second: query, upsert, update, and delete at 100 req/s per namespace, fetch at 100 req/s per index, and list at 200 req/s per index; breaches return HTTP 429. Hard request limits also apply: an upsert is max 2 MB or 1000 records, metadata max 40 KB per record, and query top_k up to 10,000 with a 4 MB result cap.

Redis Enterprise

Integration surface
Redis wire protocol (RESP) via client libraries; separate REST API for cluster management
Authentication
Password or ACL-based credentials, typically over TLS
Change detection
Keyspace notifications over pub/sub or reads from Redis Streams; no transaction-log CDC surface for data
Capabilities
read · write
Rate limits
Throughput is bounded by provisioned cluster capacity rather than published API rate limits
How it works

How to connect Pinecone to Redis Enterprise — three steps, no code

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.

  1. 01

    Connect your apps

    Authenticate Pinecone and Redis Enterprise with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.

    • OAuth 2.0
    • SSH tunnel
    • VPC peering
    Pinecone connected
    Redis Enterprise connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Pinecone and Redis Enterprise 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.

    • Standard objects
    • Custom objects
    • Auto-schema
    objects · Pinecone ⇄ Redis Enterprise
    Customers 12,480
    Sales Orders 8,213
    Invoices 5,902
    Items 1,344
  3. 03

    Map fields

    Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.

    • Auto-map
    • Type casting
    • Transforms
    Pinecone Redis Enterprise
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
Try interactive demo
FAQ

Pinecone and Redis Enterprise integration FAQ

SECURITY

Security teams trust Stacksync

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.

Learn more about security
SOC 2 Type II
ISO 27001
HIPAA BAA
GDPR
CCPA
DPF US-EU-UK-CH
→ SECURITY WITH BENEFITS

SSO & SCIM

Let your users access Stacksync from your centralized user management systems. Works with Okta, Azure, Google SSO and more.

Alerts

Immediately get alerted about record syncing issues over email, Slack, PagerDuty and WhatsApp. Resolve issues from a centralized dashboard with retry and revert options.

Secure connection options

Securely connects to your systems with:

OAuth 2 SSH Tunnelling SSL certificates IP Whitelisting VPN gateway VPC peering and more

Related integrations

Every pair below is a real-time, two-way sync. Search all 407 integrations available for Pinecone and Redis Enterprise.

Popular · 7 of 407
All Pinecone integrations All Redis Enterprise integrations Browse all integrations
Coworkers laughing in front of a laptop in a casual office setting

Your last integration took months.
Your next one takes a prompt.

Book a demo Tour the platform on your own