Vespa

Vespa is a search and serving platform for AI applications. It suits teams that need to tune relevance, use fresh data, or handle growing retrieval traffic. Choose it for the combination of search and ranking controls; weigh cloud quotas and support tiers before committing.

Overview

Vespa brings vector and tensor search, positional text search, and structured-data search together with ranking, model inference, and real-time serving. Hybrid search, sparse vectors, metadata filtering, and HNSW indexes support varied retrieval designs. That range is valuable when search quality depends on combining signals, but may be more platform than a team needs for a simple vector lookup.

Teams can self-manage Vespa, use the Vespa Kubernetes Operator, or deploy on Vespa Cloud. Its functions are distributed across node clusters, and cluster size can change without affecting queries or writes. This gives growing applications room to scale while keeping traffic live, but self-management leaves infrastructure operations with the team.

Key features

Ranking and inference

Ranking signals can be combined with tensor functions or machine-learned ONNX and XGBoost models; ONNX Runtime handles model inference. This makes Vespa a strong fit when an application needs retrieval and model-based ranking to work closely together, rather than relying on search alone.

Integrations and security

A client for OpenAI-compatible APIs connects to services including OpenAI, Google Gemini, Anthropic, Cohere, and Together.ai. The integration is beta, so teams should treat it as a developing capability rather than a settled dependency.

Vespa Cloud encrypts data at rest and in transit, uses node and endpoint certificates, and automatically keeps operating-system patches up to date. Vespa Cloud Enclave can run applications in a customer's AWS, Azure, or GCP account, but provider resource charges come in addition to Vespa Cloud costs.

Pricing

Vespa is paid software, with a pricing note of From $0.05. Vespa Cloud prices are usage-based hourly unit rates, shown with monthly plan labels; actual spend depends on resource use. All Cloud plans have quotas, and the Startup plan is restricted to development zones.

• Startup: $0.05 a month (billed). Rates are $ 0.05 / hour per vCPU, $ 0.005 / hour per Memory GB, $ 0.0002 / hour per Disk GB, and $ 0.03 / hour per GPU Memory GB. Shared resources and community support only, with no SLA; it is limited to development zones and has no SSO or autoscaling. It fits development, not production workloads needing operational assurances.
• Basic: $0.10 a month (billed). Initial rates are $ 0.1 / hour per vCPU, $ 0.01 / hour per Memory GB, $ 0.0004 / hour per Disk GB, and $ 0.07 / hour per GPU Memory GB, with prices decreasing by volume. It suits applications that do not need 24/7 operational support.
• Commercial: $0.15 a month (billed). Initial rates are $ 0.145 / hour per vCPU, $ 0.0145 / hour per Memory GB, $ 0.0005 / hour per Disk GB, and $ 0.1 / hour per GPU Memory GB; volume reduces prices. It adds 24/7 operational support, backup, and disaster recovery for teams that need those safeguards.
• Enterprise: $0.18 a month (billed). Initial rates are $ 0.18 / hour per vCPU, $ 0.018 / hour per Memory GB, $ 0.0007 / hour per Disk GB, and $ 0.125 / hour per GPU Memory GB, with a $20,000 minimum monthly spend. This is for organizations prepared for that minimum.
• Enterprise *: $0.18 a month (billed), with the same initial hourly rates and $20,000 minimum monthly spend. Prices decrease by volume; the plan includes backup and disaster recovery, with SSO and dedicated support options.
• Self Managed: custom pricing. It includes self-managed deployment and unlimited support cases, with a dedicated support representative available.

The Vespa Cloud trial provides $300 in usage credits without requiring a credit card. The application stops when credits run out, so it is useful for evaluation but not a continuing free tier.

Platforms

Vespa supports API, Linux, macOS, self-hosted, web, and Windows deployments. The local deployment guide supports Linux, macOS, and Windows 10 Pro on x86_64 or arm64 with Docker Desktop or Podman Desktop. Python and Java SDKs give teams those language options for integration.

Who it's for

Vespa is best suited to teams building AI applications where relevance, ranking, fresh real-time data, or rising retrieval traffic are central requirements. It is less compelling for teams seeking a small, free hosted database or a simple lookup service: there is no free plan, and Cloud use is quota-bound.

Pros and cons

• Pro: Search spans vectors and tensors, text, and structured data, with filtering and hybrid retrieval for more than one retrieval pattern.
• Pro: Tensor functions and ONNX or XGBoost models let teams combine ranking signals and inference within the search platform.
• Pro: Cluster size can change without disrupting queries or writes, supporting growth without planned query downtime for scaling.
• Con: Startup offers only community support, no SLA, development zones, and no SSO or autoscaling, making it a limited production choice.
• Con: Every Cloud plan has a quota, and Enterprise requires at least $20,000 in monthly spend.
• Con: The OpenAI-compatible client is beta, while Enclave customers also pay their cloud provider's resource costs.

Alternatives

For teams comparing vector and search infrastructure, the Vector Databases, Search Databases, and Database Software lists are useful starting points.

• Chroma is a freemium alternative with an open-source Apache 2.0 codebase that also powers Chroma Cloud; consider it if that combination matters more than Vespa's integrated ranking and serving scope.
• Qdrant has a free tier; its listed allowance is a single-node cluster with 0.5 vCPU, 1GB RAM, and 4 GB disk. Choose it if a bounded free cloud starting point is the priority.
• Weaviate has a free tier with one cluster, 100,000 objects, 1 GB memory, 10 GB disk, one collection, and up to three tenants. It is worth considering when those caps fit a small deployment.
• Epsilla has a free tier for one team member, project, AI application, and knowledge base, with 10M vector storage and 50 messages per month; data is cleared after 30 days. It fits a tightly capped evaluation more than an ongoing workload.
• Milvus is free, open-source software for API, Linux, and self-hosted use. Consider it if a free self-managed option is the priority.
• Pinecone offers a Starter plan with up to five indexes, 100 namespaces per index, 2 GB storage, 2M monthly write units, and 1M monthly read units; it suits readers comparing a capped hosted starter plan.
• Upstash Vector has a free tier capped at 10K daily queries or updates and 1 GB of data and metadata, among other limits. It is a fit when that explicitly capped free allowance is sufficient.
• Zilliz Cloud offers a free cluster with 5 GB storage, 2.5M vCUs per month, and up to five collections; consider it if those free-cluster limits match the workload.

Verdict

Choose Vespa when search relevance, model-based ranking, fresh data, and scaling need to live in one platform. Its deployment flexibility and ranking depth are the main reasons to choose it; the lack of a free plan, Cloud quotas, uneven support, and Enterprise minimum are reasons to look elsewhere if cost certainty or a small hosted starting point matters more.