Features that make Vector Panda different

We've rethought vector search from the ground up. No compromises on accuracy, no complexity for developers, no surprises in billing. Here's how we do it.

Zero Configuration

Import and query. That's it.

Traditional vector databases require hours of parameter tuning, index optimization, and infrastructure setup. We believe that's complexity you shouldn't have to deal with. Vector Panda automatically optimizes everything behind the scenes.

  • ✓
    Automatic index optimization
    Our system analyzes your data patterns and automatically selects the optimal indexing strategy
  • ✓
    Smart shard management
    Data is automatically distributed across shards for optimal performance
  • ✓
    No parameter tuning
    No need to understand HNSW parameters, IVF settings, or quantization options
Traditional Vector DB Setup
# Complex configuration required
index = faiss.IndexIVFPQ(d, nlist, m, nbits)
index.nprobe = 10
index.train(vectors)
index.add_with_ids(vectors, ids)
Vector Panda Setup
# Just works
client = Client("your-api-key")
client.add(vectors)

3 Steps to Production

pip install veep
client = Client(api_key)
results = client.search(query)

100% Perfect Recall

Never miss a relevant result.

Most vector databases trade accuracy for speed, using approximate algorithms that can miss important results. Our proprietary PCA indexing guarantees 100% recall while maintaining blazing-fast performance through intelligent caching and distribution.

  • ✓
    Guaranteed accuracy
    Every query returns all relevant results, not just most of them
  • ✓
    12.8× faster than competition
    Perfect recall doesn't mean slow - our distributed architecture ensures speed
  • ✓
    No recall/speed tradeoffs
    No need to choose between accuracy and performance

Benchmark Results

100%
Recall Rate
8.3ms
Avg Latency
0
Missed Results

Transparent Pricing

Pay for storage. Query unlimited.

No usage meters, no query counters, no surprise bills. You pay a simple monthly fee based on how many vectors you store. That's it. Query as much as you want, scale as much as you need, sleep soundly knowing exactly what you'll pay.

  • ✓
    Unlimited queries included
    Whether you query once or a million times, the price stays the same
  • ✓
    No hidden fees
    No charges for API calls, bandwidth, or compute time
  • ✓
    Predictable costs
    Know exactly what you'll pay before you start

Simple Pricing Model

HOT
$2.99
per million vectors
WARM
$0.49
per million vectors
COLD
$0.09
per million vectors
Storage
+
∞ Queries
=
Simple

What you WON'T see on your bill:

API Calls
Bandwidth
Compute Time
Overage Fees

Built to Scale

From prototype to planet-scale.

Start with a thousand vectors, scale to billions. Our distributed architecture automatically handles growth without any changes to your code. No re-indexing, no migrations, no downtime. Just seamless scaling as your needs grow.

  • ✓
    Automatic distribution
    Data is automatically distributed across regions for optimal performance
  • ✓
    No code changes required
    The same API works whether you have 1K or 1B vectors
  • ✓
    Global edge network
    Queries are routed to the nearest datacenter automatically
🚀
Prototype
1K - 1M vectors
🏢
Production
1M - 100M vectors
🌍
Planet Scale
100M - 10B+ vectors

Same code. Same API. Any scale.

# Works at any scale
client = Client("your-api-key")
results = client.search(query_vector)

Drop-in Compatible

Switch in minutes, not months.

Already using Pinecone, Weaviate, or another vector database? Our API is designed to be compatible with existing solutions. Change a few lines of code and you're running on Vector Panda with better performance and lower costs.

  • ✓
    Compatible APIs
    Familiar methods and patterns that work with your existing code
  • ✓
    All embedding models supported
    Works with OpenAI, Cohere, Hugging Face, and custom embeddings
  • ✓
    Migration tools included
    Simple scripts to move your data from other providers
Before: Other Vector DB
index.upsert(vectors=[
("id1", vector1, metadata1),
("id2", vector2, metadata2)
])
After: Vector Panda
client.upsert(vectors=[
("id1", vector1, metadata1),
("id2", vector2, metadata2)
])

Works with everything

🤖
OpenAI
🔮
Cohere
🤗
Hugging Face
🐍
Python
📦
Node.js
🚀
Go

Enterprise Ready

Security and compliance built in.

Built with enterprise requirements from day one. SOC 2 Type II certified, 99.99% SLA, end-to-end encryption, and comprehensive audit logs. Your data is safe, your compliance team is happy, and your developers can focus on building.

  • ✓
    SOC 2 Type II Certified
    Annual audits ensure we meet the highest security standards
  • ✓
    99.99% Uptime SLA
    Guaranteed availability with automatic failover and redundancy
  • ✓
    End-to-end encryption
    Your vectors are encrypted in transit and at rest
  • ✓
    Comprehensive audit logs
    Every operation is logged for compliance and debugging
99.99%
Uptime SLA
SOC 2
Type II Certified
🔒
Bank-grade Security
256-bit AES encryption everywhere

Ready to experience the difference?

Start with 1 million free vectors. No credit card required.

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