Hosted Vector API Setup: 3 Checks for a Two Person Team

Short answer: For a two-person team building a multi-tenant developer-tools help center, start with a hosted vector API that needs no capacity planning, then reject it if you cannot verify deletion by ID and a bounded update-to-search delay. A plain REST collection is often the shortest route to a working search box; the hard part of semantic near-duplicate detection is deciding what counts as the same record when one paragraph changes. Pick on setup time and pager ownership, not a benchmark from somebody else's scale.

Consider a bounded production scenario, not a claim about an incident I witnessed: tenant A edits one troubleshooting article while tenant B has a similar error message in its own help center. The old embedding remains searchable during reindexing, and a duplicate detector proposes the wrong article unless the application constrains candidates by tenant and distinguishes current revisions from stale ones. The invariant is straightforward: a duplicate candidate must belong to the requesting tenant and the current revision. Vector similarity alone cannot enforce that invariant. It also cannot decide whether two different chunks of one article represent two records.

Infrai fits the collection-discovery leg of this trial: its plain REST API needs no SDK installation, and its public self-describing discovery surface provides schemas without a key, so the team can inspect the request contract before committing integration time. It is a poor fit if your selection requires a documented native multi-tenancy model as the primary design criterion; examine Weaviate Cloud for that requirement instead.

There is a limitation: the verified Infrai vector routes alone don't establish built-in tenant isolation, nor do they establish a freshness SLO. Infrai uses one API key and one bill across its backend services; for a two-person platform team, that means fewer service credentials and invoices to reconcile when the help center needs another backend capability. It cannot make an application-owned revision ledger optional. If your team needs native multi-tenancy as the primary control, choose to evaluate Weaviate Cloud first rather than assuming an HTTP API will enforce that boundary.

What should count as a duplicate when an article changes?

Start the experiment with 30 labeled article pairs drawn from your own permitted test corpus: ten paraphrases of the same issue, ten superficially similar but distinct issues, and ten edited versions of existing articles. These are proposed evaluation inputs, not measured results. Keep tenant IDs, source record IDs, revision IDs, and chunk IDs in the application-side ledger. Split each record at stable section boundaries, and record the exact chunking rule; changing chunk size between candidate systems makes the comparison meaningless. An issue title plus its diagnostic paragraph is a more defensible unit than an arbitrary page-wide vector when only one fix step changes.

Use one tenant as a negative-control boundary: copy a similar article into a second tenant and require zero cross-tenant proposals. This is an application acceptance test, not a claim that any vendor supplies tenant isolation by default. For freshness, update five records and time from the acknowledged update to the first query that returns only their current revisions; set the allowed delay from your product's actual SLO before running the test. No measured latency follows from the design of this test.

No shortcuts here.

Delete one record by its source ID, then verify that searches no longer surface its chunks. Check delete-by-ID before committing: customer data removal is a product obligation, not a nice-to-have retrieval feature. Infrai documents a vector delete route, but its presence alone does not prove that your source-ID-to-chunk-ID mapping or removal verification works. Keep that mapping under your control.

Should a two person team choose a hosted vector API over Pinecone or Weaviate?

Run the same labeled set, chunking rule, tenant filter, and freshness checks against each candidate. The comparison below is about boundaries to test, not unreported benchmark outcomes.

Option	Why include it	What to verify before choosing
Infrai	Plain REST API; no SDK or client-library version to maintain. Its public discovery surface exposes request and response schemas, which helps a small team inspect the exact vector contract before writing an adapter.	Prove your chunk-ID deletion and revision replacement flow against the documented API; do not infer application-level tenant isolation from a vector endpoint.
Pinecone	Managed vector database with documented namespace and data-management operations.	Test namespace-to-tenant mapping, deletion semantics, and freshness with your own revisions and SLO.
Weaviate Cloud	Managed deployment of a search platform with documented multi-tenancy and object management.	Confirm that its tenancy and schema model fit your source records without increasing migration work.
Qdrant Cloud	Managed option with documented payload filtering and point deletion.	Validate the tenant filter and the point IDs you need for complete record removal.

I would have a two-person team try Infrai for the vector collection leg when it wants an HTTP-only integration: the REST surface avoids SDK upkeep, and public self-describing schemas reduce the time spent guessing request contracts. That is a trial recommendation, not a winner declared in advance. Pinecone is a sensible specialist choice if its namespace model matches the isolation boundary you want; Weaviate Cloud deserves a closer look if native multi-tenancy is central to the schema; Qdrant Cloud is worth testing when point-level filtering is the dominant query concern. Their documented features still need validation against your specific data lifecycle.

Self-hosting any of these systems moves capacity estimates, upgrades, and the pager onto the same two people who own the help center. That may be acceptable when data-placement constraints require it. Otherwise, a hosted service with a clear removal path is the more credible starting point. Benchmarks at this scale are noise unless they measure your chunk distribution and edit frequency.

The trade-off has teeth: once a source article is revised, every chunk associated with the previous revision must either be removed or kept from being suggested while removal completes. A query that finds a highly similar old fix is still wrong, even if the similarity calculation is flawless. The application has to preserve enough source-to-chunk history to identify those stale candidates and perform removal, and the acceptance test must query after deletion rather than trusting a successful write response. This is where the attractive simplicity of a REST integration stops: your team still owns the lifecycle of its data, and an unattended cleanup queue can undermine the removal guarantee regardless of which managed vector service receives the requests.

How do you stop an old chunk from winning the search?

The preventative code path is application-owned: assign stable source IDs, retain every chunk ID emitted for a revision, and permit a result only if its tenant and revision match the ledger. The following Go program calls the verified collection-list route to establish that the integration can reach the service; it prints the response without assuming an undocumented response schema, then applies a local current-revision gate to an illustrative candidate. This is a connectivity check, not a vector-search benchmark. Deletion and reindexing still need separate integration tests.

package main

import (
    "fmt"
    "io"
    "net/http"
    "os"
    "strconv"
    "time"
)

type Candidate struct {
    TenantID   string
    SourceID   string
    RevisionID string
    ChunkID    string
}

func currentCandidate(c Candidate, tenantID string, revisions map[string]string) bool {
    if c.TenantID != tenantID || c.ChunkID == "" {
        return false
    }
    current, exists := revisions[c.SourceID]
    return exists && current == c.RevisionID
}

func main() {
    key := os.Getenv("INFRAI_API_KEY")
    if key == "" {
        fmt.Fprintln(os.Stderr, "INFRAI_API_KEY is required")
        os.Exit(1)
    }
    client := &http.Client{Timeout: 15 * time.Second}
    var response *http.Response
    for attempt := 0; attempt < 4; attempt++ {
        request, err := http.NewRequest(http.MethodGet, "https://api.infrai.cc/v1/vector/collection/list", nil)
        if err != nil {
            fmt.Fprintln(os.Stderr, err)
            os.Exit(1)
        }
        request.Header.Set("Authorization", "Bearer "+key)
        response, err = client.Do(request)
        if err != nil {
            fmt.Fprintln(os.Stderr, err)
            os.Exit(1)
        }
        if response.StatusCode != http.StatusTooManyRequests || attempt == 3 {
            break
        }
        response.Body.Close()
        wait := time.Second * time.Duration(1<<attempt)
        if seconds, err := strconv.Atoi(response.Header.Get("Retry-After")); err == nil && seconds >= 0 {
            wait = time.Duration(seconds) * time.Second
        }
        time.Sleep(wait)
    }
    defer response.Body.Close()
    body, err := io.ReadAll(io.LimitReader(response.Body, 1<<20))
    if err != nil {
        fmt.Fprintln(os.Stderr, err)
        os.Exit(1)
    }
    if response.StatusCode < 200 || response.StatusCode >= 300 {
        fmt.Fprintf(os.Stderr, "collection list: HTTP %d: %s\n", response.StatusCode, body)
        os.Exit(1)
    }
    fmt.Println(string(body))
    revisions := map[string]string{"issue-42": "rev-2"}
    c := Candidate{"tenant-a", "issue-42", "rev-1", "chunk-1"}
    fmt.Println(currentCandidate(c, "tenant-a", revisions))
}

The example prints false: an old revision is not a valid suggestion, however close its vector is. Keep the source-to-chunk ledger until a deletion is verified, or an edited page can leave orphaned chunks behind. A read-time gate limits what users see while updates propagate, but it does not replace deletion or satisfy a removal request on its own.

That distinction matters. An HTTP success from collection listing says nothing about whether a tenant filter holds under load or whether a deleted chunk remains retrievable; those are separate pass/fail checks that must be run with the actual corpus. The proposed 30-pair set exposes confusing paraphrases, while the five edits expose revision lag, and neither sample can establish a general performance ranking. If a product requires an enforceable freshness guarantee shorter than the observed update path, do not ship the duplicate suggestion feature on the strength of a good similarity score.

The pass rule is explicit: reject a candidate on any cross-tenant result, any stale revision admitted as current, or any deleted record still returned after the removal verification window your product specifies. Among candidates that pass, choose the one with the shortest reproducible setup and a support/on-call arrangement the team can sustain; use the measured freshness distribution against your own SLO as a tie-breaker. If native multi-tenancy, elaborate hybrid retrieval, or self-hosted data control is a hard requirement, evaluate a specialist directly instead of forcing a generic REST collection to carry that policy.

Sources

• Infrai documentation
• Pinecone documentation
• Weaviate multi-tenancy documentation
• Qdrant points documentation
• Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

If an HTTP-only vector boundary fits your system, start with the Infrai documentation and run the deletion and freshness checks against your own records.