Exabase vs Sentra: Which is Better in 2026?
A comprehensive comparison of Exabase and Sentra covering features, pricing, use cases, and which tool is the right choice for your needs.
⚡ Quick Verdict
Choose Exabase if:
- →You need self-managing agent memory system, benchmarked by the vendor as first on two ai-memory benchmarks or bases — isolated per-tenant instances with version rollback
Choose Sentra if:
- →You want more affordable paid plans (from $16/mo)
- →You need one queryable memory graph shared by humans and agents or rest and mcp access from claude, chatgpt, cursor and windsurf
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Exabase vs Sentra: At a Glance
Pricing Comparison: Exabase vs Sentra
Understanding the pricing differences between Exabase and Sentra is crucial for making the right choice. Here's how their plans compare side by side.
Exabase Pricing
💡 Pricing takeaway: Both Exabase and Sentra offer free tiers, making it easy to try before you buy. Compare the specific plans to find the best value for your use case.
Feature-by-Feature Comparison
Here's how every feature from Exabase and Sentra stacks up.
What Makes Each Tool Unique
🔵 Unique to Exabase
Features available in Exabase but not in Sentra:
- ✓Self-managing agent memory system, benchmarked by the vendor as first on two AI-memory benchmarks
- ✓Bases — isolated per-tenant instances with version rollback
- ✓Sub-document multi-modal hybrid search rather than whole-document similarity
- ✓Structured extraction from PDFs, websites, images, audio and video
- ✓Published credit exchange rates per operation, so cost is modellable up front
🟣 Unique to Sentra
Features available in Sentra but not in Exabase:
- ✓One queryable memory graph shared by humans and agents
- ✓REST and MCP access from Claude, ChatGPT, Cursor and Windsurf
- ✓Semantics resolved at ingestion against a per-org ontology
- ✓Captures interactions and decisions, not just finished artefacts
- ✓Meeting recording, pre-meeting briefs, risk radar and commitment tracking
Use Case Recommendations
Best for: Exabase
Exabase is context infrastructure for teams building AI agents — the storage, retrieval and memory layer that otherwise gets rebuilt badly in every agent project. Six components make up the platform. Memory is a self-managing memory system the vendor states is first on both of the leading AI-memory benchmarks. Bases are isolated per-tenant instances with version rollback, which is the piece that matters if you are shipping an agent to customers and each tenant's knowledge must stay separated and restorable. Resources hold files, notes and links as a portable context server. Deep Search runs sub-document, multi-modal hybrid search rather than whole-document vector similarity. Extract pulls structured data out of PDFs, websites, images and other formats — the website-to-Markdown endpoint is exposed as a standalone tool. Workers are autonomous agents that enrich a knowledge base on their own schedule. Usage is credit-metered with the exchange rates published plainly: one credit buys roughly 18 PDF extractions, 18 website extractions, 30 audio extractions, 0.8 video extractions, 65 memories, or 10 memories with inference mode enabled, which makes cost modelling possible before you commit. The vendor reports over 100,000,000 pages processed and offers a zero-data-retention policy from the paid tier. Deployment is positioned as production-ready, private by design and security-first, and the vendor runs a demo booking route for teams evaluating whether the platform fits before committing to the credit model.
Ideal use cases:
- •Teams or individuals who need self-managing agent memory system, benchmarked by the vendor as first on two ai-memory benchmarks
- •Teams or individuals who need bases — isolated per-tenant instances with version rollback
- •Teams or individuals who need sub-document multi-modal hybrid search rather than whole-document similarity
- •Teams or individuals who need structured extraction from pdfs, websites, images, audio and video
- •Anyone focused on ai-agents workflows
- •Anyone focused on memory workflows
Best for: Sentra
Sentra is a unified memory layer — a company brain shared by a team and its AI agents. It captures interactions, decisions and drift and writes them into one queryable graph exposed over REST or MCP, so what you teach one agent every agent remembers. The technical argument is specific: vector search returns what is close, not what is correct, because embeddings are stored at write time and structure is guessed at query time, which means every request re-crawls Slack, email and docs to rediscover what a term means inside your organisation. Sentra instead resolves semantics at ingestion and builds the graph on demand at query time against an ontology unique to your company, treating meaning as a primitive rather than a side effect. It also argues that most tools capture artefacts — the CRM record, the Jira ticket, the Confluence page — and therefore capture only the output of a decision, not the interaction that produced it. In practice the product covers meeting recording and notes, pre-meeting briefs, deep research across all connected data, a commitment tracker, a risk radar and connection intelligence, plugging into Slack, Gmail, Calendar, Outlook, GitHub and Linear, and into Claude, ChatGPT, Cursor, Perplexity, Codex and Windsurf.
Ideal use cases:
- •Teams or individuals who need one queryable memory graph shared by humans and agents
- •Teams or individuals who need rest and mcp access from claude, chatgpt, cursor and windsurf
- •Teams or individuals who need semantics resolved at ingestion against a per-org ontology
- •Teams or individuals who need captures interactions and decisions, not just finished artefacts
- •Anyone focused on memory workflows
- •Anyone focused on mcp workflows
🤖 Other AI Agent Infrastructure Tools to Consider
Exabase and Sentra aren't the only options. Here are other popular tools in the same space:
SuperAGI
Open-source autonomous AI agent framework with visual dashboard — 14K GitHub stars
MetaGPT
Multi-agent AI framework simulating software teams — 45K GitHub stars, builds full apps from prompts
Cerebras
Fastest LLM inference powered by the Wafer Scale Engine.
Scale AI
AI data platform for training data and model evaluation.
Roboflow
End-to-end computer vision platform for developers.
Labelbox
Enterprise data labeling platform for ML training datasets.
Is one of these your tool?
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Frequently Asked Questions
Is Exabase better than Sentra?
It depends on your needs. Exabase offers 5 key features including Self-managing agent memory system, benchmarked by the vendor as first on two AI-memory benchmarks and Bases — isolated per-tenant instances with version rollback, while Sentra provides 5 features including One queryable memory graph shared by humans and agents and REST and MCP access from Claude, ChatGPT, Cursor and Windsurf. Exabase uses a freemium model with a free tier, while Sentra is freemium with free access available. Choose based on which features and pricing model align with your requirements.
Is Exabase cheaper than Sentra?
Sentra is cheaper, starting at $16/month compared to Exabase's $149/month. Both tools offer free tiers, so you can try each before committing. Always check the official websites for the most current pricing.
Can I use Exabase and Sentra together?
Yes, many users combine Exabase and Sentra in their workflow. Exabase excels at self-managing agent memory system, benchmarked by the vendor as first on two ai-memory benchmarks, while Sentra shines with one queryable memory graph shared by humans and agents. Using both allows you to leverage the strengths of each tool, though this means managing two subscriptions — though free tiers can help manage costs.
What's the main difference between Exabase and Sentra?
While both are ai agent infrastructure tools, Exabase emphasizes self-managing agent memory system, benchmarked by the vendor as first on two ai-memory benchmarks, whereas Sentra is known for one queryable memory graph shared by humans and agents. The best choice depends on your specific workflow and feature priorities.
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