Augment Code Alternative: MCP Graph Context

This page is for teams already sold on the idea that an agent needs real code context beyond a bigger prompt, and who are now comparing Augment Code's Context Engine against a more graph-forward approach. Augment is the closest architectural neighbor Symvanta has, so the comparison is narrower and more useful than most.

Where the two products actually differ

Augment's Context Engine parses a dependency graph, indexes commit history, and generates embeddings, then retrieves against that index when an agent asks a question: fetch the relevant slice, hand it to the model (Augment Code, Context Engine). The graph is real, but based on their public documentation, it functions as an input to retrieval rather than something the agent queries directly. Their docs describe semantic indexing and mapping of relationships across a codebase; they do not document direct graph-traversal tools such as "find every caller of this function" or "compute blast radius" as callable operations.

Symvanta builds the same kind of graph (nodes are symbols, edges are calls, imports, implements, and instantiates) and then exposes it as first-class MCP tools: relate with callers, dependencies, blast_radius, implementers, heritage, and chain modes, plus find_node for symbol resolution and find_http_route for HTTP handlers. An agent does not get a pre-fetched context bundle assembled on Symvanta's side; it walks the graph itself, one traversal at a time, and can ask a second, more specific question if the first answer is not enough. Embeddings and text search sit alongside the graph for the cases where semantic or literal matching is the right tool.

That difference matters most exactly where code embeddings vs. code graph draws the line: embeddings are excellent at "find code that looks like this," and weaker at "tell me everything that would break if I change this," because relatedness in vector space is not the same thing as an edge in a call graph. It is also the pattern behind why AI coding agents fail on large codebases: retrieval that returns a plausible chunk still leaves the agent guessing about what is actually connected to it.

Pricing, as of July 2026

Augment's Business plan is $100 per month flat, no per-seat charge, covering up to 50 seats with a pooled $100 usage credit across LLM inference, the Context Engine, and compute, plus pay-as-you-go top-ups when the pool runs out. LLM inference is billed at the underlying provider's API price plus a 40% service fee on top; compute carries no fee. Enterprise is custom, unlimited seats, volume discounts (Augment Code pricing). If you have seen a $20/month individual tier mentioned in older roundups, that is stale: it is not on Augment's current pricing page.

Symvanta pricing is per seat: Starter at $19/month, Pro at $29 per seat/month with a 7-day trial, Enterprise at $99 per seat/month with a 15-seat minimum. The practical difference shows up at the margins: Augment's flat $100 pool is attractive for a mid-size team with predictable usage, and gets more expensive per person as usage grows past the pool given the 40% fee on inference. A per-seat model is easier to forecast for a team that just wants to know what next month's bill looks like.

Deployment and MCP

Augment runs as cloud SaaS with two modes: a Local mode that indexes the active repository on the developer's machine, and a hosted Remote mode MCP for cross-repo and org-wide context, indexing entire organizations across GitHub, GitLab, and Bitbucket, plus docs and wikis, with auto-sync via CI/CD hooks (Augment Code, Context Engine MCP). MCP is core to the product: Context Engine MCP is generally available for Cursor, Claude Code, Zed, and other MCP clients. Augment also publishes its own benchmark: on 300 Elasticsearch pull requests, they report a "+80%" improvement running Claude Code with Opus 4.6 through the Context Engine MCP versus without it. That is Augment's own published number, not an independent eval, and it is worth reading as such: a vendor benchmark on a vendor-chosen task.

Symvanta is a hosted MCP endpoint by default, working with Claude Code, Cursor, Windsurf, and other MCP clients, with an on-prem bundle available for Enterprise customers. Cross-repo context comes from explicitly linking repositories, including cross-repo HTTP-call edges between services, and indexing is branch-aware, so an agent can pin a session to a feature branch or even query uncommitted working-tree edits.

On-prem specifically: Augment claims VPC and air-gapped deployment for Enterprise customers and holds SOC 2 Type II, but does not publish a detailed self-hosted deployment architecture the way some competitors do. Treat that as a sales-conversation item; their docs alone don't verify it.

The 40% fee, worked through

The fee structure is worth sitting with for a second, because it changes how a team should think about the $100 flat price. The $100/month covers the pool; once a team burns through it, top-ups are pay-as-you-go, and every dollar of LLM inference in that top-up carries the 40% service fee on top of the provider's own API price. A team running heavy agentic workloads (long sessions, big context windows, frequent tool calls) will burn the pool faster than a team doing light autocomplete-style usage, and the fee compounds with usage rather than flattening out. None of that makes the pricing bad; it makes it usage-shaped, which is a different thing to budget for than a flat per-seat number. The other lever on an inference-shaped bill is what the agent re-sends every turn; we work that side of the equation in Sonnet plus a code graph vs Opus alone.

Side by side

Who should pick Augment

If your team wants a single flat monthly number that covers a shared pool of usage across up to 50 people, and you are comfortable with retrieval that happens behind the scenes (the agent never calls it as a tool), Augment's Context Engine is a solid, mature product with real org-wide indexing and a genuinely useful Remote mode for multi-repo work.

Who should pick Symvanta

If you want the agent to be able to ask "who calls this" and get an actual graph traversal back (a retrieved chunk still needs interpreting), and you would rather pay per seat than manage a shared usage pool with a service fee on top, that is the fit. The difference in one line: the graph is the interface itself, the thing the agent queries directly, and search sits beside it as one more tool.

Frequently asked questions

Both of these are claims worth testing on a codebase you actually maintain. Book a 15-minute demo on your own repository and see what the graph returns.