Understand it

What it actually saves

Where the token, cost, and correctness impact of connecting the contextlake MCP to your AI coding tools comes from, new-code grounding first, plus search, maintenance, the caveats, and how to measure it on your own repos.

What connecting the contextlake MCP server to an AI coding tool (Claude, Devin, Copilot, Cursor, Windsurf …) does to token usage, where it helps most, and where it does not help at all.

The short version#

• Writing new code in an existing estate is where it matters most. New code in a large codebase is not greenfield invention, it is mostly grounding: which service to call, its real signature, the pattern to follow, whether a utility already exists, where it plugs in. That work is almost entirely retrieval, and it is where hallucinated integrations and duplicated code come from. contextlake answers those questions from an index, cited, and matches concepts rather than just keywords.
• Search of every kind, semantic / knowledge search, "who calls X", "what breaks if I change X", "which repo has X", is the other strong case, where brute-force is intractable or hugely wasteful on a large fleet.
• Assessing impact on and maintaining unfamiliar existing code benefits for the same reason: orientation is retrieval.
• What it does not do (with nuance): it does not make a single correct generation shorter, the code you need is the code you need. But across a whole task it does cut total generation, by reducing failed attempts and reinvented code (see Does it cut generation tokens?). It also helps little on true greenfield work in an empty repo, there is nothing to ground against.
• On cost: real but modest on per-token API billing; the larger lever is rework avoided (a hallucinated integration is a failed build, a re-prompt, and a wasted review), which is time and correctness more than a line item.

Treat contextlake as a scale-and-correctness tool first, a token-cost tool second.

Where the difference comes from#

Per question, what the agent ingests with contextlake against what it gathers without it. This is a description of the two mechanisms, not a measurement of either:

Rows 3 and 4 are capability differences rather than savings: transitive reach and concept matching are not things a grep-and-read loop does more expensively, they are things it does not do at all, at any budget. The definition and caller rows are where a token saving is plausible, and its size depends entirely on how common the name is and how disciplined the agent's search would otherwise have been. The ownership row is where contextlake saves least, because git log was already cheap.

The other structural difference is that brute-force search costs time as well as tokens on a large fleet: a fleet-wide grep for a common term returns more hits than an agent can read, before it opens a single file. contextlake answers the same question from its index in one bounded, cited call.

The one fixed cost#

Any session pays for the tool schemas once, whether or not it calls a tool: 21 of them on a graph-only store (20 graph tools plus the ask router) and 23 once embeddings exist, all listed in Serve. That cost is real, and it can leave you net-negative if the agent reaches for the server on questions it does not help with. There is no published token figure for it here for the reason at the top of the page; the recipe in Measure it on your own repositories counts it in the same pass as everything else.

By use case, honestly#

New code development: the most important case#

In a real company you rarely write code into a vacuum; you add a feature, endpoint, or service to an estate that already has hundreds of repositories. Most of that work is grounding the new code in existing reality: which service to call and its real signature, the established pattern to model on, whether a utility for this already exists, and where the new code plugs in. That is retrieval, and it is exactly where an ungrounded model hallucinates an integration or reinvents a helper.

A realistic new-code task, "add a feature that integrates with an existing service, following existing conventions", decomposes into a sequence of grounding questions the agent asks before it writes anything:

1. Find the pattern to follow (semantic search, no exact name to grep for).
2. Find how to integrate with the existing service (semantic search again).
3. Look up the API to call (definition lookup).
4. Find usage examples to model on (callers).
5. Check a utility for this is not already there (semantic search).

With contextlake, each is one cited call. Without it, steps 1, 2 and 5 have no keyword to search for and degrade into reading candidate files, which is where the bulk of the baseline's tokens go. The grounding phase finishes with cited, concept-matched context instead of keyword hits the model has to guess from, before a single line is generated. The larger, harder-to-quantify win sits downstream: correct grounding prevents the hallucinated integration or the duplicate util that turns into a failed build, a re-prompt, and a wasted review.

What contextlake does not shorten is a single correct generation, the code you need is the code you need, and it helps little on true greenfield work in an empty repo, where there is nothing to ground against.

Does it cut generation tokens?#

Short answer: not the way "measured" implies, but yes at the level of a whole task. A single correct generation is irreducible. What contextlake reduces is the number of generations and the amount of new code generated, ranked by impact:

1. It cuts the hallucinate → fail → regenerate loop (biggest lever). The expensive thing isn't the first draft; it's the second, third, and fourth. An ungrounded model invents an import, calls the wrong service, or guesses a signature; the build fails; the agent regenerates. Every retry is generation tokens spent again. Grounding in the real API/signature/pattern collapses N attempts toward 1, the failure mode that dominates new-code work in a large estate.
2. Reuse instead of reinvent. When semantic search surfaces an existing client/util/validator, the agent emits a call (a few tokens) instead of a reimplementation (tens to hundreds), and doesn't add a duplicate to the codebase.
3. Surgical edits instead of full rewrites. With a precise definition + blast radius, the agent emits a small diff rather than regenerating a whole file "to be safe."
4. Less defensive/exploratory/hedged output. Precise context yields one confident implementation instead of multiple candidate approaches and just-in-case scaffolding.

The honest boundary: the irreducible core (typing out the correct code) can't shrink; if the first generation would already be correct without contextlake, the saving is ~0; and it can't rescue a model that ignores the context it was handed.

Search: the other strong case#

On a large estate, brute-force search is either intractable (a concept query has no single keyword to grep for, and the keyword you pick returns more than an agent can read) or slow and noisy. contextlake turns it into a bounded, cited, ranked call, and semantic search finds conceptually related code that keyword search misses entirely.

Modifying / maintaining existing code: a moderate win#

Largest when the code is unfamiliar. The value is orientation: where is this, who calls it, what breaks if I touch it, who owns it, answered in one cited call instead of many grep → read → re-grep round-trips.

Translating to ACU / dollars / subscription#

Different tools bill differently, so the honest answer differs per platform.

• Claude via MCP (per-token API). This is the only place token deltas map directly to money. But a per-query ratio is not a per-session saving: most agent tokens are reasoning, generation, and the conversation growing turn over turn, not retrieval. A retrieval-heavy session is where any input-token saving shows up at all, and a generation-heavy one is where it disappears. (Separately, output tokens can drop too, via fewer failed regenerations and less reinvented code, but that is a mechanism, not a measured figure; see Does it cut generation tokens?.)

• Devin (ACU). We did not measure this, and anyone quoting you an ACU number is guessing. An ACU is compute-time, not tokens; the plausible lever is exploration steps and rework avoided, which is highly task-dependent. Expect a meaningful reduction on exploration-heavy, unfamiliar-fleet tasks and ≈0 on tightly-scoped generation tasks, a hypothesis to A/B test, not a measured claim.

• VS Code + Copilot (flat subscription). There is no per-token bill to save. The value is accuracy and speed: fewer wrong suggestions from missing cross-repo context, better-targeted file reads, and staying inside the context window. It shows up as developer time and correctness, not a line item.

Caveats worth reading#

• Whatever you measure will be per-query. Whole-task savings are diluted by all the non-retrieval tokens around them, so don't multiply a per-query ratio onto your whole bill.
• The baseline depends on how the agent searches. A smart, well-scoped agent that greps one known repository spends far less than one that greps the whole fleet. Both are realistic; they are not the same baseline, and a ratio means nothing without saying which one you used.
• contextlake adds a fixed schema cost per session and can be net-negative if an agent calls it for questions it doesn't help with.
• Semantic recall is good, not perfect, the built-in CPU embedder is fast, not frontier-grade. Results are cited and advisory; verify against the source.
• Fleet size and the mix of questions your team actually asks dominate everything here. A result from someone else's estate does not transfer to yours, which is the other half of why this page no longer prints one.

Measure it on your own repositories#

The comparison is straightforward to run, and yours is the one worth having:

1. Index your repos (contextlake bootstrap or contextlake kb index --workspace …).
2. Pick a handful of representative questions your team actually asks, and write them down before you look at any output. Post-hoc question selection is how benchmarks flatter themselves.
3. For each question, capture both sides: the contextlake MCP response (the JSON the agent receives, which you can get from contextlake kb query --json, kb impact --json, and the other --json commands) and the baseline your agent would otherwise gather (the grep output plus the files it would then read).
4. Tokenize both with the same tokenizer, the one your model actually uses, and record the tokenizer and the contextlake version alongside the numbers. Both change; a figure without them ages into fiction, which is what happened to the numbers this page used to carry.
5. Add the one-off schema cost once per session, not once per query.

The point is not a single magic number, it is to see, on your codebase and your question mix, where the retrieval cost actually lives.