Algolia (NeuralSearch) vs Glean
Algolia (NeuralSearch) vs Glean: side-by-side comparison of two enterprise-search adjacencies systems — architecture, taxonomy, license, pricing, MCP/A2A support, and direct edges.
Algolia (NeuralSearch) · Glean
Cost & capability
| Algolia (NeuralSearch) | Glean | |
|---|---|---|
| Cost tier | free | — |
| $/Mtok input | 0 | — |
| $/Mtok output | 0 | — |
Where they differ (10)
Rows where both sides have data and the values disagree — the shortlist of dimensions that actually distinguish these two systems.
| Algolia (NeuralSearch) | Glean | |
|---|---|---|
| Type | Neural-hash hybrid (vector + keyword) | Enterprise search + per-user KG |
| Created | 2012 | 2019-01 |
| Pricing | Free + paid | Enterprise only |
| Funding | $150M total $2.2B val Series D · 2021-07 | $765M total $7.2B val Series F · 2025-06 |
| Backend storage | custom (proprietary sharded index) | custom |
| Deployment | Managed-only | Both |
| API surface | REST, SDK: 17+ languages | REST, SDK: Python, JS/TS |
| Embedding | locked (NeuralSearch managed) | locked |
| Multi-tenancy | namespace | Logically isolated single-tenant per customer (data, models, telemetry siloed; no shared vector index); option for fully isolated single-tenant in customer AWS/Azure/GCP |
| MCP | via official adapter — Algolia MCP | native (first-party) — Glean MCP server |
At a glance
| Algolia (NeuralSearch) | Glean | |
|---|---|---|
| Section | Enterprise-search adjacencies | Enterprise-search adjacencies |
| Tier | T1 | T1 |
| Type | Neural-hash hybrid (vector + keyword) | Enterprise search + per-user KG |
| Created | 2012 | 2019-01 |
| Pricing | Free + paid | Enterprise only |
| Funding | $150M total $2.2B val Series D · 2021-07 | $765M total $7.2B val Series F · 2025-06 |
| Backend storage | custom (proprietary sharded index) | custom |
| Deployment | Managed-only | Both |
| API surface | REST, SDK: 17+ languages | REST, SDK: Python, JS/TS |
| Embedding | locked (NeuralSearch managed) | locked |
| Multi-tenancy | namespace | Logically isolated single-tenant per customer (data, models, telemetry siloed; no shared vector index); option for fully isolated single-tenant in customer AWS/Azure/GCP |
| MCP | via official adapter — Algolia MCP | native (first-party) — Glean MCP server |
| A2A | no Google A2A (Agent2Agent) integration documented as of 2026-05. | no Google A2A (Agent2Agent) integration documented as of 2026-05. |
| OpenTelemetry | no first-party OpenTelemetry exporter documented; standard logs/metrics typically available. | no first-party OpenTelemetry exporter documented; standard logs/metrics typically available. |
| Optimised for | enterprise connectors + entitlements + governance + RAG-grounding | enterprise connectors + entitlements + governance + RAG-grounding |
| Anti-fit | not for SMB / consumer use cases | not for SMB / consumer use cases |
Taxonomy
| Axis | Algolia (NeuralSearch) | Glean |
|---|---|---|
| storage | vector | vector |
| retrieval | similarity | similarity |
| persistence | long-term | long-term |
| update | extraction | extraction |
| unit | document | document |
| governance | inspectable | auditable |
| conflict | n/a | none |
Pros & cons
Algolia (NeuralSearch)
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.
Glean
Pros: Most polished enterprise AI search product — connectors, governance, ranking, and conversational interface tightly integrated; high enterprise NPS.
Cons: Enterprise pricing; closed product so memory primitives aren't exposed to developers building agents.