Overview

Vespa combines vector and tensor search, positional text search, and structured-data search with ranking, model inference, and real-time serving. That range is useful when an application depends on relevance as well as retrieval: teams can use hybrid search, sparse vectors, metadata filtering, and HNSW indexes, then combine ranking signals rather than treating search as a single lookup step. Python and Java SDKs are supported.

Deployment can be self-managed, run with the Vespa Kubernetes Operator, or hosted on Vespa Cloud. This flexibility lets teams choose how much infrastructure to operate, but also puts more weight on selecting a deployment model and understanding cloud quotas.

Key features

Search, ranking, and inference

Ranking signals can be combined with tensor functions or machine-learned models in ONNX and XGBoost formats, and ONNX Runtime supports model inference. This is a strong fit for applications where ranking logic and search need to work closely together. Teams that only need basic vector retrieval may not benefit from the added range.

Scaling and operations

Vespa distributes functions across node clusters and can change cluster size without affecting queries or writes. That makes it relevant to teams expecting retrieval traffic to grow while their application remains active. Self-managed users can run it on Linux, macOS, or Windows 10 Pro on x86_64 or arm64, using Docker Desktop or Podman Desktop.

Cloud security and integrations

Vespa Cloud encrypts data at rest and in transit, uses node and endpoint certificates, and applies OS patches automatically. Vespa Cloud Enclave can run applications inside a tenant’s AWS, Azure, or GCP account, but provider resource charges come on top of Vespa Cloud costs. Its client for OpenAI-compatible APIs, including OpenAI, Google Gemini, Anthropic, Cohere, and Together.ai, is marked beta, so teams should treat it accordingly.

Pricing

Vespa Cloud plans have quotas. The cloud free trial provides $300 in usage credits without requiring a credit card; the application stops when those credits run out. Vespa has no free plan, so the trial is a limited way to evaluate the service rather than a continuing no-cost tier.

Startup — 0.05 USD per month: billed resource rates are $ 0.05 / hour per vCPU, $ 0.005 / hour per GB of memory, $ 0.0002 / hour per GB of disk, and $ 0.03 / hour per GB of GPU memory. Shared resources, development zones only, community support with no SLA, and no SSO or autoscaling make this a constrained starting point for development, not a production-support choice.
Basic — 0.10 USD per month: initial hourly rates are $ 0.1 / hour per vCPU, $ 0.01 / hour per GB of memory, $ 0.0004 / hour per GB of disk, and $ 0.07 / hour per GB of GPU memory; rates go down with volume. It suits applications that do not need 24/7 operational support.
Commercial — 0.15 USD per month: initial hourly rates are $ 0.145 / hour per vCPU, $ 0.0145 / hour per GB of memory, $ 0.0005 / hour per GB of disk, and $ 0.1 / hour per GB of GPU memory; rates go down with volume. It adds 24/7 operational support, backup, and disaster recovery, making it the clearer fit when continuity and round-the-clock help matter.
Enterprise — 0.18 USD per month: initial rates are $ 0.18 / hour per vCPU, $ 0.018 / hour per GB of memory, $ 0.0007 / hour per GB of disk, and $ 0.125 / hour per GB of GPU memory, with a $20,000 minimum monthly spend. That minimum makes this a substantial commitment, not a casual upgrade.
Enterprise * — 0.18 USD per month: initial hourly rates match Enterprise, with prices going down with volume and a $20,000 minimum monthly spend. It includes backup and disaster recovery, with SSO and dedicated support options.
Self Managed — custom pricing: includes self-managed deployment and unlimited support cases, with a dedicated support representative available. This fits teams that want to operate Vespa themselves while retaining a support path.

For Startup, the low entry rate comes with development-only zones and community support; Basic also lacks 24/7 operational support. Commercial is the first tier described with that support, while the Enterprise minimum is likely to rule it out for teams without a substantial monthly budget.

Platforms

Vespa supports API, Linux, macOS, self-hosted, web, and Windows deployments. Local installation supports Linux, macOS, and Windows 10 Pro on x86_64 or arm64 with Docker Desktop or Podman Desktop.

Who it's for

Vespa is best suited to teams building AI applications where search quality, ranking, fresh real-time data, or growing retrieval traffic are important. Its model and ranking options serve relevance-sensitive workloads, while cluster scaling and deployment choices suit teams prepared to make infrastructure decisions. A team seeking a no-cost hosted plan or a narrowly focused vector lookup should consider whether Vespa’s operational and plan complexity is justified.

Pros and cons

Pro: Multiple search modes and configurable ranking support applications that need more than a basic vector match.
Pro: Cluster size can change without affecting queries or writes, a practical advantage for changing workloads.
Pro: Self-managed, Kubernetes Operator, and cloud deployments give teams a choice of operational model.
Con: Cloud quotas apply across plans, and Startup is restricted to development zones with no SLA.
Con: No free plan means ongoing use requires a paid plan after the trial credits are exhausted.
Con: The $20,000 minimum monthly spend makes Enterprise a high-commitment option.
Con: Enclave customers pay cloud-provider resource costs in addition to Vespa Cloud charges.

Alternatives

Pinecone is worth considering if a freemium hosted option with a free plan and a defined Starter allowance—up to 5 indexes, 100 namespaces per index, 2 GB storage, 2M monthly write units, and 1M monthly read units—better suits the workload.

Chroma is an alternative for teams seeking a freemium option with broad platform support and an Apache 2.0 open-source codebase also used by Chroma Cloud.

Upstash Vector has a free tier capped at 10K daily queries or updates, 200M vectors × dimensions, 1,536 maximum dimensions, 100 namespaces, and 1 GB of data and metadata; it may fit a workload that stays within those limits.

Zilliz Cloud offers a free cluster with 5 GB storage, 2.5M vCUs per month, and up to 5 collections, making it an option when those free-cluster limits are sufficient.

KDB.AI offers a free Cloud Starter Edition with 4 GB memory per instance, 30 GB storage, and a 10 MB query-size limit.

Qdrant offers a free-forever single-node cloud cluster with 0.5 vCPU, 1GB RAM, and 4 GB disk, which may be preferable for a small workload that fits those caps.

Weaviate provides a free tier with one cluster, 100,000 objects, 1 GB memory, 10 GB disk, one collection, and up to three tenants.

Actian Zen is another alternative.

Browse the Vector Databases, Search Databases, and Database Software directories for more options.

Verdict

Choose Vespa if your AI application needs retrieval, ranking, and real-time serving to operate as one system, especially as data or traffic changes. Its range of search methods, model support, and deployment choices are compelling for teams with specific relevance needs. Look elsewhere if a free ongoing plan, straightforward vector retrieval, or a low-commitment enterprise tier matters more than that control.

	turbopuffer	Vespa
Free plan	No	No
Free trial	No	Yes
Paid from	$16/mo	$0.05/mo
Open source	No	No
Platforms	api, self-hosted, Web	api, Linux, macOS, self-hosted, Web, Windows
Free plan	No	No
Paid from	16 /mo	—
Deployment	both	both

turbopuffer vs Vespa

turbopuffer

Vespa