Turbopuffer vs. Zilliz Cloud: A Compliance and Enterprise Readiness Evaluation for Multi-Tenant Vector Search
Choosing a vector database for production isn't just about query speed and cost. When you're building for enterprise customers, the questions that matter most often come from security reviews, compliance audits, and procurement checklists: Can you guarantee data isolation? What happens when a user requests data deletion? Where are your SOC 2 and ISO 27001 certifications? Do you support Private Link?
In our previous companion article, we benchmarked Turbopuffer and Zilliz Cloud on performance and cost across 160 million vectors and 128,000 tenants. In this article, we focus on the dimensions that determine whether a vector database can actually ship in an enterprise environment: search correctness as a data quality issue, delete consistency and GDPR compliance, security certifications, access governance, disaster recovery, and operational tooling.
These findings are based on the same two-week, $500 evaluation — same data, same region, same client hardware.
Test Design
We modeled a multi-tenant retrieval SaaS with three tenant tiers designed to mirror real-world distribution:
| Tenant Type | Count | Vectors per Tenant |
|---|---|---|
| Large (core enterprise customer) | 1 | 16,000,000 |
| Medium (standard customer) | 16 | 1,000,000 each |
| Small (long-tail / free-tier user) | 128,000 | 1,000 each |
Total data: 160M vectors, 768 dimensions, ~250 GB. Both products were tested on AWS us-west-2 (Oregon).
Finding 1: Search Correctness — A Data Quality and Compliance Risk
In a multi-tenant deployment, every query includes a filter — typically a tenant ID — to isolate results to a specific customer's data. This is not an edge case. It's how every multi-tenant vector search system works.
We ran 1,000 queries at top-100 against 10M vectors, then applied tenant filters at varying selectivity levels:
| Filter Selectivity | Turbopuffer Recall | Zilliz Cloud Recall |
|---|---|---|
| Broad (id > 50%) | 0.78 | 0.99+ |
| Moderate (id > 90%) | 0.69 | 0.99+ |
| Narrow (id > 99%) — typical small tenant | 0.54 | 0.99+ |
At 0.54 recall under narrow filtering, Turbopuffer silently drops nearly half of the relevant results.
From a compliance and data quality perspective, this matters in several ways:
- Audit trail integrity. If a compliance review asks "show me all relevant documents for this query," your system is returning an incomplete set — with no indication that results are missing.
- Regulatory search requirements. In regulated industries (finance, healthcare, legal), search completeness is not optional. Missing half the relevant documents in an e-discovery, adverse event search, or regulatory filing is a compliance failure.
- Silent failure mode. Turbopuffer doesn't flag low recall. There is no warning, no metric, no alert. Your application silently operates on incomplete data.
The architectural cause is fundamental: Turbopuffer applies filters as post-processing on ANN results, rather than building filter-aware indexes. There is no tuning parameter (no ef_search equivalent) to trade latency for better recall. For any application where search completeness is a requirement — not a nice-to-have — this is a disqualifying finding.
Finding 2: Delete Operations — Unstable Latency, Stale Metadata, Broken Consistency
Latency Variance
| Operation | Avg | P99 | Min | Max | Variance |
|---|---|---|---|---|---|
| Single record delete | 842 ms | 2,716 ms | 64 ms | 3,280 ms | 51x |
| Namespace delete | 183 ms | 775 ms | 118 ms | 5,345 ms | 45x |
A 51x variance between best-case and worst-case delete latency means you cannot set meaningful SLAs for delete operations. For GDPR compliance (right to erasure), data lifecycle management, or any workflow that depends on predictable delete performance, this is a serious concern.
Metadata Staleness
After deletion, Turbopuffer's metadata (row counts) do not update immediately. We measured a 30-second update cycle — during which namespace.meta() returns stale counts. If your application verifies deletions by checking row counts (a common pattern), you'll get incorrect data for up to half a minute.
Post-Delete Consistency
In extended testing, we observed a more severe consistency issue. After a delete_by_filter operation, the API returned success and the deleted documents no longer appeared in results. But result counts were wrong — requesting top-100 returned 48, then 64, then 69 results, slowly creeping back up to 100 over approximately one hour.
The delete removed documents from results (soft delete) but did not trigger an index rebuild. The underlying index continued reflecting the pre-delete state, converging over time with no notification, no progress indicator, and no way to force a rebuild.
For downstream systems that depend on accurate result counts — analytics pipelines, data quality monitors, consistency checks — this creates a window of silently incorrect data that can last up to an hour.
GDPR Implications
GDPR Article 17 requires that personal data be erased "without undue delay." While the definition of "undue delay" is debated, a system where:
- Delete latency is unpredictable (64 ms to 3,280 ms)
- Metadata remains stale for 30 seconds after deletion
- Search results take up to an hour to fully reflect deletions
...creates significant compliance risk. If a data subject requests erasure and your system still surfaces their data in search results for up to an hour afterward, you may struggle to demonstrate compliance under audit.
Finding 3: The Enterprise Readiness Gap
A vector database in production isn't just about query speed. It's about what happens when things go wrong, when your team needs visibility, when compliance asks for an audit trail, and when your business requires uptime guarantees. This is where the gap between a developer tool and a production platform becomes obvious.
Observability and Monitoring
| Capability | Turbopuffer | Zilliz Cloud |
|---|---|---|
| Performance metrics dashboard | No | 18+ metrics with real-time dashboards |
| Automated alerting | No | 41 alerts across 26 metrics (Enterprise) |
| Query and operation logs | No | Built-in with audit log forwarding |
| Third-party observability integration | No | Datadog, Prometheus |
| Custom alert thresholds and rules | No | Yes, per-organization and per-project |
Turbopuffer's console gives you four tabs: Overview, Namespaces, API Keys, Settings. You can see aggregate storage and query counts. That's it. There is no way to drill into per-tenant latency, identify slow queries, set up alerts for latency spikes, or export metrics to your existing monitoring stack.
With Zilliz Cloud, you get real-time dashboards covering QPS, latency, throughput, CPU, memory, and storage — and you can pipe everything into Datadog or Prometheus to unify vector DB monitoring with the rest of your infrastructure. When a tenant's queries start degrading at 3 AM, the alert fires before your users notice. With Turbopuffer, you find out from a support ticket.
Security and Compliance
| Capability | Turbopuffer | Zilliz Cloud |
|---|---|---|
| Encryption in transit | TLS | TLS 1.2+ with AES-256 |
| Encryption at rest | SSE (S3) | AES-256 with per-user encryption keys |
| SOC 2 Type II | Not listed | Certified |
| ISO 27001 | Not listed | Certified |
| HIPAA readiness | Not listed | Available (Business Critical plan) |
| GDPR readiness | Not listed | Available |
| Private Link (no public internet) | Not available | Available |
| Network-isolated clusters | Shared infrastructure | Dedicated VPC per cluster |
For any team in healthcare, finance, government, or enterprise SaaS, compliance certifications are not optional — they're a procurement requirement. SOC 2 Type II, ISO 27001, and HIPAA readiness are table stakes for closing enterprise deals. Turbopuffer does not publicly list any of these certifications.
Private Link is another hard requirement for many enterprises: the ability to connect to the database without traffic traversing the public internet. Zilliz Cloud supports this. Turbopuffer does not.
Access Control and Governance
| Capability | Turbopuffer | Zilliz Cloud |
|---|---|---|
| API key types | 4 levels (admin/read-write/write-only/read-only) | Fine-grained per-cluster, per-collection |
| RBAC | No | Collection-level RBAC |
| Enterprise SSO (SAML/OIDC) | No | Okta, Microsoft Entra, and others |
| Audit logs | No | Full operation audit with cloud storage export |
| Team/role management | Basic | Organization → Project → Cluster hierarchy |
Turbopuffer's access control is limited to four API key types. There's no way to restrict a key to a specific namespace, no SSO integration for enterprise identity providers, no audit trail of who queried or modified what data. For any team with more than a few developers — or any customer that requires access governance — this means building your own authorization layer on top.
Data Protection and Disaster Recovery
| Capability | Turbopuffer | Zilliz Cloud |
|---|---|---|
| Automated backups | Not documented | Scheduled with configurable policies |
| Point-in-time recovery | Not available | Available (Business Critical) |
| Cross-region replication | Not available | Global Cluster with CDC-based replication |
| Automated failover | Not available | Zero-code failover to nearest healthy region |
| Multi-AZ deployment | Not documented | Automatic replica distribution across AZs |
Turbopuffer stores data in S3, which provides durability. But durability is not the same as recoverability. If a bad write corrupts data, if a bulk delete goes wrong, or if you need to restore to a specific point in time — there is no documented mechanism to do so.
Zilliz Cloud's Global Cluster provides cross-region replication via a CDC pipeline, with automatic failover that requires no code changes or connection string updates. For mission-critical applications, this is the difference between "our data is safe on S3" and "our service stays up when a region goes down."
Deployment Flexibility
| Capability | Turbopuffer | Zilliz Cloud |
|---|---|---|
| Deployment model | Serverless only | Serverless, Dedicated, BYOC |
| BYOC (Bring Your Own Cloud) | Not available | AWS, GCP, Azure |
| Infrastructure as Code | Not available | Official Terraform provider |
| Region availability | Limited | AWS, GCP, Azure across multiple regions |
| Uptime SLA | Not published | 99.95% (Enterprise Dedicated) |
Turbopuffer offers one deployment model: their serverless platform. If your security team requires infrastructure in your own VPC, if your compliance framework mandates data residency, or if you need a published uptime SLA for your own customer contracts — there is no path to get there with Turbopuffer.
Zilliz Cloud offers Dedicated clusters with a 99.95% uptime SLA, BYOC deployment in your own AWS/GCP/Azure VPC, and a Terraform provider for infrastructure-as-code workflows. These aren't nice-to-haves — they're checkboxes on enterprise procurement checklists.
Integration Ecosystem
| Capability | Turbopuffer | Zilliz Cloud |
|---|---|---|
| AI framework integrations | Limited | LangChain, LlamaIndex, Haystack, DSPy |
| Data pipeline connectors | Not available | Kafka, Spark, Airbyte, Fivetran |
| Migration tooling | Not available | VTS (open-source), managed migration service |
| Cloud marketplace availability | Not listed | AWS, GCP, Azure marketplaces |
Production AI applications don't exist in isolation. They sit in data pipelines — Kafka for streaming ingestion, Spark for batch processing, Airflow for orchestration, LangChain or LlamaIndex for RAG. Zilliz Cloud has native connectors for all of these. Turbopuffer requires custom integration code for each, adding engineering time and maintenance burden.
For teams migrating from another vector database, Zilliz Cloud's Vector Transport Service supports migration from Elasticsearch, OpenSearch, Pinecone, Qdrant, Weaviate, and PostgreSQL — with zero-downtime options for Milvus sources. With Turbopuffer, migration is a manual, application-level effort.
Summary
Here is a side-by-side summary of our compliance and enterprise readiness findings:
| Dimension | Turbopuffer | Zilliz Cloud |
|---|---|---|
| Search correctness (narrow filter) | 0.54 recall — silent data quality risk | 0.99+ |
| Delete latency variance | 51x (64 ms – 3,280 ms) | Predictable |
| Post-delete consistency | ~1 hour to converge | Immediate |
| Security certifications | Not listed (SOC 2, ISO 27001, HIPAA) | Certified |
| Private Link | Not available | Available |
| Operational tooling | Minimal (4-tab console) | Full monitoring, logging, alerting |
| Deployment options | Serverless only, no SLA published | Serverless, Dedicated (99.95% SLA), BYOC |
What to Test Before You Commit
If you're evaluating a vector database for enterprise deployment, here are the compliance and readiness tests we'd recommend:
- Test recall with your actual filter conditions. If your use case requires search completeness (regulatory, legal, healthcare), measure recall under realistic tenant-ID filtering. If recall drops below 0.95, your search results may not meet compliance standards.
- Test deletes and verify consistency. Delete a batch of records, then immediately query and check result counts. Check again at 1 minute, 5 minutes, 30 minutes, and 1 hour. Note when counts stabilize. Evaluate whether the convergence window meets your GDPR or data lifecycle requirements.
- Verify security certifications against your procurement checklist. Confirm SOC 2 Type II, ISO 27001, HIPAA, and GDPR certifications. Check for Private Link support, RBAC, SSO, and audit logging. These are often hard requirements — not negotiable.
- Evaluate disaster recovery capabilities. Test backup/restore workflows. Confirm cross-region replication and failover options. Validate that your RPO/RTO targets are achievable.
All benchmark data was collected in December 2025 on AWS us-west-2. Both products were tested on their latest publicly available versions. The 160M-vector multi-tenant benchmark used identical data, configurations, and client hardware for both systems. Recall and consistency tests were conducted as part of an extended evaluation. Raw data, scripts, and detailed logs are available upon request.
For detailed performance and cost benchmark results, see our companion article: "Turbopuffer vs. Zilliz Cloud: A Performance and Cost Benchmark for Multi-Tenant Vector Search."
Yanliang QiaoYanliang Qiao is a Senior Quality Assurance Engineer at Zilliz.
- Test Design
- Finding 1: Search Correctness — A Data Quality and Compliance Risk
- Finding 2: Delete Operations — Unstable Latency, Stale Metadata, Broken Consistency
- Finding 3: The Enterprise Readiness Gap
- Summary
- What to Test Before You Commit
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