Encord vs Tigris
Side-by-side trajectory, velocity, and editorial themes.
Encord pushes labeling toward agentic, multi-file workflows.
Encord is making its labeling pipeline more automated and more complex — agents from the catalog can now be added as workflow nodes, multi-file Data Groups went GA, and Labels in Index went GA across all datasets. UX and integrity work — consensus-review username hiding, a metadata panel, webhook signature verification — round out the recent shipping.
The product is splitting into two layers: an automation runtime where AI agents handle parts of labeling pipelines without manual triggers, and a richer data plane where multi-file groupings, label exploration, and consensus review are first-class objects. Encord is packaging more of the labeling-ops workflow into the platform rather than leaving it to custom integration code.
Expect the Agents Catalog to expand with pre-built agents for common pre-labeling and QA tasks, and expect Index to keep absorbing labeling-aware exploration features now that labels are exposed there.
Tigris turns its object store into agent infrastructure with Agent Kit, agent-shell, and durable global streams.
Tigris's release stream is a sustained product-marketing push around AI-agent storage primitives. Agent Kit landed as a TypeScript SDK exposing bucket forks, workspaces, checkpoints, and event coordination. agent-shell put a virtual bash environment with persistent storage in front of those primitives. Durable global streams via S2 Lite extended the object store into a streaming substrate suitable for per-agent reasoning traces. Around the launches, case studies and tutorials (Basic Memory, the $10 self-updating knowledge base) make the pitch concrete.
Tigris is staking a position that the right substrate for AI agents is not a database, vector store, or queue — it is a globally-distributed, fork-able object store. Each blog and SDK in this batch reinforces that thesis from a different angle: storage as message queue, fork-per-agent sandboxing, storage-protected agent containment, streams for reasoning traces. The competitive map being drawn includes R2, S3 Express, Backblaze, and the agent-runtime vendors (Modal, E2B), not other databases.
Expect a managed Vector or Lance-index surface on top of buckets to compete more directly with Turbopuffer and Pinecone, and a Python counterpart to the @tigrisdata/agent-shell TypeScript runtime to widen the agent-developer surface area.
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