A practical comparison of HydraDB and Google Embedding 2 from a product manager's perspective, covering cost, latency, multimodal support, vendor lock-in, and when each makes sense for your product roadmap.
If you are building any AI-powered product in 2026, semantic search, RAG, recommendation engines, content moderation, you will hit a fork in the road: where do your embeddings live, and who generates them? The answer affects latency budgets, infrastructure cost, vendor dependency, and the ceiling on what your product can do twelve months from now.
HydraDB is a purpose-built vector database designed for high-throughput similarity search with native support for HNSW and DiskANN indexing. Google Embedding 2, part of the Vertex AI family, is an embedding-as-a-service offering that pairs tightly with Google Cloud's managed vector search infrastructure. They solve overlapping but fundamentally different problems, and choosing between them is rarely a pure technology question.
HydraDB separates the storage and compute layers, letting you scale query throughput independently from dataset size. The indexing engine supports both HNSW for in-memory workloads and DiskANN for datasets that exceed available RAM. At 10 million 768-dimension vectors, a single HydraDB node with 32 GB RAM and HNSW indexing sustains roughly 4,200 QPS at 98.5% recall with ef_search set to 128. Switching to DiskANN drops QPS to around 1,800 but lets you index 100 million vectors on the same hardware.
The write path uses a log-structured merge approach, batching incoming vectors into segments before building index partitions. This design means bulk inserts of one million vectors complete in approximately 14 minutes on a 16-core machine, but individual inserts see higher latency, typically 8-12 ms per vector, because the system prioritizes batch efficiency.
Google Embedding 2 is not a database. It is an embedding model exposed through the Vertex AI Embeddings API that produces 768-dimensional vectors by default, with a task_type parameter that optimizes the output for retrieval, classification, clustering, or semantic similarity. The vectors it produces are then stored in Vertex AI Vector Search (formerly Matching Engine), which uses Google's proprietary ScaNN algorithm for approximate nearest neighbor lookups.
This tight coupling is the defining trade-off. You get a fully managed pipeline, embed with one API call, index and search with another, but every vector is generated and stored inside Google's ecosystem. Moving those vectors to a different provider means re-embedding your entire corpus, which at 50 million documents costs roughly $2,500 in API calls alone.
HNSW performance in HydraDB depends on two parameters that directly trade recall for speed: M (the number of bi-directional links per node, typically 16-64) and ef_construction (the size of the dynamic candidate list during index build, typically 100-500). Increasing M from 16 to 48 improves recall@10 from 94.2% to 98.7% on a 5-million-vector benchmark, but doubles memory consumption from 1.2 GB to 2.4 GB per million vectors at 768 dimensions.
The ef_search parameter controls query-time recall. At ef_search=64, HydraDB returns results in 2.1 ms median latency with 95.8% recall. At ef_search=256, latency climbs to 8.4 ms but recall reaches 99.2%. For most production RAG applications, ef_search between 100 and 200 hits the sweet spot where users cannot perceive the latency difference but retrieval quality noticeably improves.
Google's ScaNN algorithm in Vertex AI Vector Search uses asymmetric hashing with quantized residuals, achieving recall@10 above 98% on standard benchmarks like ANN-SIFT1B at sub-5ms latency. However, these numbers come from Google's own infrastructure with pre-warmed indices. In real deployments, first-query latency after a scale-up event can spike to 200-400 ms while new replicas load index shards into memory. Sustained QPS at 10 million vectors typically lands around 3,000-5,000 per deployed node, depending on machine type.
Google Embedding 2 outputs 768 dimensions by default. OpenAI's text-embedding-3-large produces 3072 dimensions, and Cohere Embed v3 offers 1024. The dimension count directly impacts storage, memory, and search latency. At 768 dimensions with float32, each vector consumes 3,072 bytes. At 3072 dimensions, that becomes 12,288 bytes, quadrupling your storage and memory requirements per vector.
HydraDB supports dimensionality reduction through Matryoshka representation learning, allowing you to truncate 3072-dimension vectors to 768 or 1024 at query time with modest recall degradation, typically 1-3% on MTEB benchmarks. This lets you store full-resolution vectors but search at lower dimensionality for speed-sensitive queries. Google Embedding 2 does not support variable-dimension output; you always get 768.
HydraDB supports scalar quantization (float32 to int8) and product quantization, reducing memory per vector by 75% with recall degradation under 2% for most workloads. Half-precision (float16) storage is also available, cutting memory in half while preserving 99.5%+ recall on inner product similarity. At 100 million vectors with 768 dimensions, the difference between float32 and int8 quantization is roughly 230 GB of RAM, which translates to approximately $1,400/month in cloud compute savings.
When a HydraDB node starts, it must load HNSW graph structures into memory before serving queries. For 10 million vectors at 768 dimensions with M=32, the graph occupies roughly 15 GB and takes 45-90 seconds to load from SSD storage. During this window, queries either queue or fail depending on your configuration. Teams running HydraDB in Kubernetes typically set readiness probes that wait for index loading to complete, accepting that new replicas take 1-2 minutes to become available during scale-up events.
Vertex AI Vector Search requires deploying an index to an endpoint before it can serve queries. The initial deployment takes 10-30 minutes depending on index size. Subsequent autoscaling events are faster, typically 2-5 minutes, but still significantly slower than traditional service scaling. This makes Vertex AI Vector Search unsuitable for workloads with extreme burst patterns unless you maintain warm standby capacity, which eliminates the cost advantage of fully managed scaling.
HydraDB supports synchronous and asynchronous replication across nodes. In synchronous mode, writes confirm only after the primary and at least one replica acknowledge the vector, adding 3-5 ms to write latency but guaranteeing zero data loss on node failure. Asynchronous replication has a replication lag window of typically 50-200 ms, meaning a node failure could lose that window of recent vectors. For most recommendation and search workloads, asynchronous replication is acceptable because a missing vector for 200 ms is invisible to end users.
Vertex AI Vector Search runs on Google's managed infrastructure with automatic replication across zones within a region. The SLA guarantees 99.9% availability for deployed endpoints. Multi-region failover requires deploying separate index endpoints in each region and implementing client-side routing, which doubles your infrastructure cost but provides resilience against regional outages. Google does not offer cross-region replication as a built-in feature for Vector Search endpoints.
At 1 million 768-dimension vectors, HydraDB runs comfortably on a single n2-standard-8 instance (8 vCPUs, 32 GB RAM) on GCP, costing approximately $245/month with committed use discounts. The HNSW index fits entirely in memory with room to spare. Vertex AI Vector Search at this scale requires a minimum deployment of one e2-standard-16 machine type, costing approximately $390/month, plus embedding API costs of roughly $5-8/month for ongoing new document processing at a few thousand documents per day.
At 10 million vectors, HydraDB requires either a high-memory instance (n2-highmem-16 at $520/month) or two standard nodes with sharded indices ($490/month total). Vertex AI Vector Search costs approximately $780/month for the deployed endpoint, but operational overhead drops to near zero since Google handles scaling, patching, and monitoring. The break-even point where Vertex AI's higher infrastructure cost is offset by reduced operations staffing is typically around 10-15 million vectors for teams without dedicated infrastructure engineers.
At 100 million vectors, the cost difference becomes stark. HydraDB with DiskANN indexing can run on a cluster of four n2-highmem-32 instances with NVMe local SSDs, totaling approximately $3,200/month. The same scale on Vertex AI Vector Search costs $5,500-7,000/month depending on QPS requirements and machine type selection. However, HydraDB at this scale demands a dedicated infrastructure engineer spending 15-20 hours per month on maintenance, index rebuilds, and capacity planning, costing roughly $3,000-4,000/month in fully loaded salary in Bengaluru.
If you start with Google Embedding 2 and Vertex AI Vector Search, migrating to HydraDB requires exporting your vectors from the Vertex AI index (possible via batch export), transforming them into HydraDB's ingestion format, and rebuilding the HNSW index. The vectors themselves are portable since they are just float arrays. The real cost is re-testing retrieval quality, since switching the storage and index layer can shift recall characteristics by 1-3% even with identical vectors, enough to require re-tuning your RAG prompts.
Moving to Vertex AI is straightforward for the vector storage layer: export vectors from HydraDB, upload to Cloud Storage, and create a Vertex AI Vector Search index. The complication arises if you were using a non-Google embedding model. Vertex AI Vector Search works with any vectors, but switching to Google Embedding 2 for ongoing documents means your existing corpus uses one embedding space and new documents use another. The only clean solution is re-embedding the entire corpus, which at scale can take days and cost thousands in API fees.
HydraDB exposes Prometheus-compatible metrics for query latency (p50, p95, p99), index memory usage, segment merge frequency, replication lag, and cache hit rates. The most critical alert to configure is on p99 query latency exceeding your SLA threshold, which typically indicates the HNSW graph has outgrown available memory and the system is falling back to disk reads. A secondary alert on replication lag exceeding 500 ms catches network partitions before they cascade into stale search results.
Vertex AI Vector Search surfaces metrics through Cloud Monitoring: request count, latency distribution, error rates, and deployed index size. The limitation is that you cannot inspect internal index behavior, segment health, or memory pressure. When performance degrades, your debugging surface is limited to request-level metrics. Teams that need deeper observability often deploy a shadow HydraDB cluster for comparison testing, which partially defeats the purpose of using a managed service.
Google Embedding 2 integrates natively with Vertex AI Pipelines for batch embedding jobs, BigQuery for analytics on embedding metadata, Cloud Functions for event-driven embedding generation, and Vertex AI Feature Store for combining vector features with structured features. If your ML stack already runs on GCP, the operational convenience of these integrations can save 20-40 engineering hours per month compared to stitching together equivalent workflows with HydraDB and standalone tools.
HydraDB runs identically on AWS, GCP, Azure, or bare metal. This portability matters when your organization has a multi-cloud strategy or regulatory requirements that mandate data residency in regions where Vertex AI is not available. Teams building products for the Indian government sector, for instance, often need to deploy in data centers within India that may not have full Vertex AI service availability, making HydraDB the only viable option.
Google Embedding 2 supports text embeddings natively, and Google offers a separate multimodal embedding model that handles image, text, and video in a shared embedding space. HydraDB is model-agnostic and stores any vector regardless of how it was generated, meaning you can use CLIP, SigLIP, or any custom multimodal encoder. The trade-off is that with HydraDB you own the embedding pipeline complexity, while Google provides a single API call for multimodal embeddings at the cost of model choice flexibility.
Choose Google Embedding 2 and Vertex AI Vector Search when your team is already on GCP, you have fewer than 20 million vectors, your engineering team is under 10 people and cannot dedicate someone to vector infrastructure, and you value time-to-market over long-term cost optimization. The fully managed approach lets a small team in Bengaluru ship a production semantic search feature in two weeks instead of six.
Choose HydraDB when you need multi-cloud portability, your dataset exceeds 50 million vectors, you require fine-grained control over index parameters for recall optimization, your product needs to support multiple embedding models simultaneously, or cost efficiency at scale is a primary concern. HydraDB rewards teams that invest in learning the system with significantly lower operational costs beyond the 50-million-vector mark.
A pragmatic pattern we see working well for product teams in Bengaluru and across India is to start with Vertex AI for the first version, validate the product with real users, and plan a migration to HydraDB when vector count exceeds 10 million or when the embedding model strategy evolves beyond what Google offers. The migration cost is real but predictable, and the time saved in the first six months often justifies the approach.
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Google Embedding 2 produces standard float arrays that work with any vector database, including HydraDB. The vectors themselves are not locked to Vertex AI. You can call the Embedding API, receive 768-dimensional vectors, and store them in HydraDB for search. The lock-in risk is operational: once you build pipelines around Vertex AI Vector Search's managed deployment, switching the storage layer requires re-engineering your indexing and serving infrastructure.
HydraDB's DiskANN mode supports indices exceeding 1 billion vectors on clusters with NVMe SSDs. Practical production deployments in the 100-500 million vector range are well-documented. Beyond 500 million vectors, you need a sharded cluster with careful partition key design to maintain sub-10ms query latency. Google Vertex AI Vector Search supports similar scale but with less transparent capacity planning since Google manages the infrastructure sizing.
During cold start, HydraDB loads the HNSW graph from disk into memory. Queries during this period either queue or return errors depending on your configuration. For 10 million vectors, loading takes 45-90 seconds. The standard mitigation is running at least two replicas with staggered restart schedules, so one replica always serves traffic while the other loads. Kubernetes rolling update strategies handle this automatically when configured with appropriate readiness probes.
At 10 million vectors with 768 dimensions, HydraDB costs approximately $490-520/month on GCP for compute alone, plus engineering time for operations. Vertex AI Vector Search costs approximately $780/month for the endpoint plus $50-100/month for ongoing embedding API calls. The total cost of ownership, including engineering time, tends to favor Vertex AI at this scale for teams without dedicated infrastructure engineers, and HydraDB for teams that already have DevOps capacity.
Both support multimodal search, but through different mechanisms. Google offers a dedicated multimodal embedding model that generates joint embeddings for text, images, and video in one API call. HydraDB requires you to choose and run your own multimodal encoder, such as CLIP or SigLIP, but gives you full control over model selection and fine-tuning. For teams building image search products in Bengaluru, the choice depends on whether you need a custom-trained multimodal model or a general-purpose one.
Yes, if you keep the same embedding model. Vertex AI Vector Search supports batch export of stored vectors. You can export these vectors, transform them into HydraDB's import format, and rebuild the index. The vectors are standard float arrays, so they are fully portable. The only scenario requiring re-embedding is if you want to switch to a different embedding model at the same time, which is a separate decision from the storage migration.
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