Database & Backend
Pinecone
AI & ML Engineers · Global · Specification audit
Pinecone is a database & backend platform built for AI & ML Engineers teams operating in Global. Available on a permanently free entry tier, it competes in the Database & Backend segment by offering a combination of purpose-built for 1-billion-scale vector similarity search under 100ms latency and managed service eliminates all infrastructure management for embedding storage. The vendor lists GDPR, SOC2 as supported compliance frameworks in its official documentation. Verify current compliance status with the vendor before deployment in regulated environments. Infrastructure is distributed across US-East, EU-West, GCP-US, GCP-EU data centres, giving Global operators control over data residency and latency requirements.
VektorIndex Score™
Composite specification quality score. Based on compliance coverage, integration depth, pricing transparency, and vendor verification.
| Dimension | Score | Max |
|---|---|---|
| Compliance coverage | 8 | 20 |
| Integration depth | 20 | 20 |
| Specification richness | 12 | 15 |
| Risk transparency | 9 | 9 |
| Hosting regions documented | 9 | 9 |
| API data available | 7 | 7 |
| Vendor verified listing | 0 | 10 |
| Pricing transparency | 10 | 10 |
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Specification Data
| Metric | Value |
|---|---|
| Starting Price | Free plan available |
| API Rate Limit | 2000 req/min |
| Market | Global |
| Target Segment | AI & ML Engineers |
| Data Hosting Regions | US-East, EU-West, GCP-US, GCP-EU |
| Compliance Frameworks | GDPRSOC2 Per vendor documentation — not independently audited |
| Native Integrations | LangChainLlamaIndexOpenAIAnthropicCohereAWSAzureGCP |
Sourced from vendor documentation. Not independently audited.
Core Operational Advantages
Vendor-statedPurpose-built for 1-billion-scale vector similarity search under 100ms latency
Managed service eliminates all infrastructure management for embedding storage
Hybrid search combines vector similarity with metadata filters in a single query
Native SDKs for LangChain, LlamaIndex, OpenAI, and every major LLM framework
Known Risks to Validate
Per documentationVendor lock-in — migrating requires re-uploading all vectors to a new index
Free tier limited to 1 index and 100K vectors — production workloads need paid plans
Cost per vector scales quickly — pgvector (Supabase) cheaper for small datasets under 1M
Who Is This For?
Pinecone is best suited for AI & ML Engineers in Global that need purpose-built for 1-billion-scale vector similarity search under 100ms latency. Teams that require managed service eliminates all infrastructure management for embedding storage will find the feature set well-aligned with day-to-day operational demands. Validate the documented risks listed above with the vendor before committing budget.
Documented Risks to Review
Pinecone has 3 documented risks worth validating before deployment
Teams deploying Pinecone for GDPR-regulated workloads should validate these risk areas with the vendor. Our drop-in middleware may help address some of these — no data leaves your infrastructure.
Bottom Line
Based on this specification audit, Pinecone delivers 4 documented operational advantages for AI & ML Engineers teams, with 3 identified risk areas to validate before deployment. A free entry plan is available, making it low-risk to evaluate before committing budget. Native integrations with LangChain, LlamaIndex, OpenAI cover the most common ai & ml engineers stack dependencies without requiring custom middleware. The API rate limit of 2000 requests per minute is adequate for standard automation workloads but may require negotiation for high-volume programmatic use cases.
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Get a Technology Assessment →Frequently Asked Questions
- How much does Pinecone cost?
- Pinecone offers a permanently free plan. Paid tiers with additional features are available — check the vendor's current pricing page for up-to-date tier details.
- Which compliance frameworks does Pinecone list?
- Pinecone lists the following compliance frameworks in its vendor documentation: GDPR, SOC2. VektorIndex has not independently audited these claims. Request a current Data Processing Agreement before deployment in regulated environments.
- What is Pinecone's documented API rate limit?
- Pinecone documents an API rate limit of 2000 requests per minute on its standard tier. Enterprise plans may offer higher limits — contact the vendor's sales team for custom rate agreements. Verify on the vendor's developer documentation before building integrations.
- What platforms does Pinecone integrate with natively?
- Pinecone maintains native integrations with: LangChain, LlamaIndex, OpenAI, Anthropic, Cohere, AWS, Azure, GCP. Additional integrations are available via Zapier, Make, or the vendor's public API.
- Where is Pinecone data hosted?
- Pinecone lists data infrastructure in the following regions: US-East, EU-West, GCP-US, GCP-EU. Teams with strict data residency requirements should confirm the exact region configuration with the vendor prior to deployment.
Data provenance: Specifications sourced from official vendor documentation (pricing pages, DPAs, security whitepapers). Data is not independently audited by VektorIndex. Community-sourced data. Last updated: 29 August 2026. This is the date the record was last edited — not an independent verification. If you are a vendor and this data is outdated, claim this listing to update it.
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