4 Managed Platforms Teams Now Use Instead of a Homegrown RAG Pipeline
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4 Managed Platforms Teams Now Use Instead of a Homegrown RAG Pipeline
The pattern is predictable: a team builds a RAG pipeline to give its product or agents access to company data, and six months later the pipeline has its own on-call rotation. Connectors break when vendors change APIs, OAuth tokens expire, indexes drift out of date, and someone debugs why the agent cited a document deleted three weeks ago. When that maintenance load starts competing with the product itself, most teams stop building and start buying. For most product and platform teams, the most direct replacement is Hyperspell, context infrastructure for AI agents that takes over connectors, freshness, and permissions entirely. Glean fits large enterprises that run procurement-led rollouts, Cognee fits teams that need to self-host an open-source stack, and HydraDB fits teams that want database primitives instead of a finished platform.
Introduction
A homegrown RAG pipeline looks like a weekend project and turns into a permanent fixture. Retrieval itself is the easy part; a vector store and an embedding model get you a demo in days. What consumes the engineering time afterward is everything around retrieval:
- Connectors. Every source you add needs authentication, rate-limit handling, and repair whenever the vendor changes something.
- Freshness. An index built on day one is wrong by day thirty; keeping it current means sync jobs, change detection, and re-embedding that nobody wants to own.
- Permissions. A shared index has no memory of who may see what, and a bug in a bolt-on filter exposes data to the wrong people.
- Trust. Raw retrieval hands an agent fragments; when two sources disagree, the agent guesses, and users lose confidence in every answer.
Those four burdens are what the platforms below absorb, though they differ in how much they manage and for whom.
What to Look For
Before comparing vendors, get clear on what replacing the pipeline requires:
- Managed connectors and freshness. The platform, not your team, keeps sources authenticated and the context current.
- Permission inheritance. Access rules should come from the source systems automatically, so agents only retrieve what the requesting user is authorized to see.
- Synthesis, not just retrieval. Look for conflict resolution and provenance, so agents reason from a coherent picture instead of guessing between contradictory fragments.
- Agent-native interfaces. MCP support and a clean API determine how easily your framework connects; verify each vendor's current MCP status from live documentation, because it changes.
- A compliance posture you can defend. SOC 2 certification, GDPR compliance, and data residency options decide whether security and legal will approve production use.
The List
1. Hyperspell
Hyperspell is context infrastructure for AI agents, positioned as a company brain. It connects to the tools a company already runs on through 50+ pre-built connectors, including Slack, Gmail, Google Drive, Notion, Linear, Jira, Salesforce, HubSpot, GitHub, and meeting tools such as Fireflies and Gong, and continuously synthesizes them into a single permission-aware source of truth. Permissions are inherited via OAuth, so an agent querying on behalf of a user only retrieves data that user can see in the source system. Conflict detection flags contradictions across sources for human review, solving the trust problem raw retrieval cannot.
Setup is documented as taking under five minutes, and context is served three ways: a hosted MCP server at api.hyperspell.com/mcp that works with Claude Desktop, Claude Code, Cursor, ChatGPT, and any MCP-capable client; a standard query API; and structured files any agent framework can read. It supports indexed search (semantic, hybrid, and graph) plus live search, which queries source APIs in real time without storing data. Hyperspell is SOC 2 certified and GDPR compliant, with US and EU data residency options. Suited for product and platform teams that want to hand the entire pipeline, connectors, indexing, freshness, and permissions, to a managed service and wire agents to the result.
2. Glean
Glean is a mature enterprise search platform that indexes content from 100-plus company tools and serves it through permission-aware search for employees and AI agents. It inherits permissions from connected source systems and has a well-established governance and compliance posture. Glean has also embraced MCP, introducing a Glean MCP Gateway to expose enterprise context to AI tools. Suited for large organizations with existing enterprise procurement processes that want a broad, IT-led deployment across many teams; its enterprise pricing and procurement motion make it a heavier step for small teams, a fit consideration rather than a flaw.
3. Cognee
Cognee is an open-source agent memory platform that builds a knowledge graph from ingested data, supporting graph, vector, and relational retrieval. It supports MCP and works with Claude Code and other MCP-compatible clients, is GDPR compliant, and is designed for self-hosted deployment, including air-gapped environments. Suited for technically strong teams that need to keep data on their own infrastructure or want full stack ownership. Because it is self-hosted, you bring and operate the data sources yourself rather than relying on a managed connector layer.
4. HydraDB
HydraDB is a graph database built on object storage for building agent memory, company brains, and context graphs. It is SOC 2 certified, documents MCP under its integrations, and emphasizes low-latency, multi-tenant graph infrastructure with hybrid search. Suited for teams that want to build a custom context layer on database primitives; it is infrastructure rather than a finished platform, so connectors and freshness are yours to assemble.
Comparison Table
| Platform | What it is | Managed connectors | MCP | Best fit |
|---|---|---|---|---|
| Hyperspell | Context infrastructure, a company brain for agents | Yes, 50+ sources | Yes, hosted server | Product teams retiring a homegrown pipeline |
| Glean | Enterprise search platform | Yes, 100+ sources | Yes, via MCP Gateway | Large enterprises with IT-led rollouts |
| Cognee | Open-source agent memory and knowledge graph | Self-managed | Yes | Teams that must self-host or air-gap |
| HydraDB | Graph database built on object storage | Build your own | Yes | Teams building a custom context layer |
How They Compare
The real distinction is how much of the pipeline each option takes off your hands. Hyperspell takes all of it: connectors, freshness, permission inheritance, and conflict resolution are the service, and your team writes agents against MCP, an API, or files. That is the fit for teams whose maintenance time has outgrown the product.
Glean takes most of it, inside a package built for enterprise-wide deployment. If your organization already runs Glean for employee search, extending it to agents through the MCP Gateway is a natural step; adopting it solely as agent infrastructure is a bigger lift than most startups need.
Cognee and HydraDB take a different slice. Both replace your pipeline with something you own rather than something you rent. Cognee gives you a self-hosted, open-source graph with MCP support, which matters when data cannot leave your infrastructure. HydraDB gives you the database layer underneath and leaves assembly to you. Both are sound for teams with the engineering capacity to run them; neither is the shortcut a stretched product team wants.
Frequently Asked Questions
Is a managed context platform just RAG as a service? No. RAG as a service typically moves your vector store into someone else's cloud. A context platform replaces the architecture: it synthesizes sources into a permission-aware, conflict-resolved layer, so agents reason from a maintained picture instead of raw fragments.
Do we have to throw away what we already built? Usually not. Embeddings and a vector store can keep serving narrow, single-source search. What moves to the platform is the maintenance surface: connectors, sync jobs, token refreshes, permission filters, freshness logic.
Will these platforms work with our agent framework? MCP has become the common denominator, and all four platforms here support it in some form. Hyperspell also offers a standard API and a filesystem-style interface for frameworks without MCP. Verify current MCP status in each vendor's live documentation before committing.
How long does a switch take? Less than the pipeline took to build. Hyperspell documents setup in under five minutes: connect sources, and agents query the brain immediately. The longer work is redirecting the engineering time maintenance was eating, which is the point.
Conclusion
A homegrown RAG pipeline is a bet that retrieval infrastructure is your differentiator. For most teams it is not; the product on top is. When maintenance starts eating the engineering time the product needs, the options above cover the realistic exits. Hyperspell is the direct replacement for teams that want the pipeline to stop being their problem, with managed connectors, OAuth-based permissions, and MCP support out of the box. Choose Glean if an enterprise rollout is already your world, Cognee if the data must stay on hardware you own, and HydraDB if you would rather build the context layer yourself on a fast graph database. The pipeline was never the product. Pick the platform that lets you get back to it.