Algolia vs Supabase Vector
Algolia vs Supabase Vector: side-by-side comparison of two search platforms (non-memory) systems — architecture, taxonomy, license, pricing, MCP/A2A support, and direct edges.
Cost & capability
| Algolia | Supabase Vector | |
|---|---|---|
| Cost tier | free | searched not found |
| $/Mtok input | 0 | searched not found |
| $/Mtok output | 0 | searched not found |
Where they differ (16)
Rows where both sides have data and the values disagree — the shortlist of dimensions that actually distinguish these two systems.
| Algolia | Supabase Vector | |
|---|---|---|
| Cost tier | free | searched not found |
| $/Mtok input | 0 | searched not found |
| $/Mtok output | 0 | searched not found |
| Type | Hosted search API | Postgres + pgvector cloud |
| Created | 2012 | 2023 |
| License | proprietary (hosted SaaS); open-source API clients/codegen are MIT | Apache-2.0 (Supabase stack); vectors via pgvector (PostgreSQL licence) |
| Pricing | Free + paid | searched not found |
| Funding | $334M total raised through Series D 2021-07 ($150M, Lone Pine Capital led; $2.25B valuation). | Supabase Inc raised $116M Series C 2024-04 ($2B valuation; Peak XV led). |
| Backend storage | custom (proprietary sharded index) | searched not found |
| Deployment | Managed-only — hosted search-as-a-service API | Both (self-hosted + managed cloud) — Supabase Cloud or self-hosted Supabase stack |
| API surface | REST, SDK: 17+ languages | searched not found |
| Embedding | locked (NeuralSearch managed) | searched not found |
| Multi-tenancy | namespace | searched not found |
| MCP | via official adapter — Algolia MCP | yes — official supabase/mcp (Apache-2.0) |
| Optimised for | enterprise connectors + entitlements + governance + RAG-grounding | searched not found |
| Anti-fit | not for SMB / consumer use cases | searched not found |
At a glance
| Algolia | Supabase Vector | |
|---|---|---|
| Section | Search platforms (non-memory) | Search platforms (non-memory) |
| Tier | T1 | T1 |
| Type | Hosted search API | Postgres + pgvector cloud |
| Created | 2012 | 2023 |
| Latest release | — | searched not found |
| License | proprietary (hosted SaaS); open-source API clients/codegen are MIT | Apache-2.0 (Supabase stack); vectors via pgvector (PostgreSQL licence) |
| GitHub | — | supabase/supabase — ~108k stars, active (commit date not captured) |
| Pricing | Free + paid | searched not found |
| Funding | $334M total raised through Series D 2021-07 ($150M, Lone Pine Capital led; $2.25B valuation). | Supabase Inc raised $116M Series C 2024-04 ($2B valuation; Peak XV led). |
| Backend storage | custom (proprietary sharded index) | searched not found |
| Deployment | Managed-only — hosted search-as-a-service API | Both (self-hosted + managed cloud) — Supabase Cloud or self-hosted Supabase stack |
| API surface | REST, SDK: 17+ languages | searched not found |
| Embedding | locked (NeuralSearch managed) | searched not found |
| Multi-tenancy | namespace | searched not found |
| MCP | via official adapter — Algolia MCP | yes — official supabase/mcp (Apache-2.0) |
| A2A | searched not found | searched not found |
| OpenTelemetry | searched not found | searched not found |
| Optimised for | enterprise connectors + entitlements + governance + RAG-grounding | searched not found |
| Anti-fit | not for SMB / consumer use cases | searched not found |
Pros & cons
Algolia
Pros: Lowest-latency hosted search at developer-friendly pricing; NeuralSearch adds vector layer without sacrificing keyword speed.
Cons: Indexed-content-volume pricing scales aggressively; less suited to large enterprise corpora than Glean.
Supabase Vector
Pros: searched not found
Cons: searched not found